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@@ -32,3 +32,12 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+DrPepper.xlsx filter=lfs diff=lfs merge=lfs -text
+mtu_merged_v3.csv filter=lfs diff=lfs merge=lfs -text
+mtuspecific/3d_old.html filter=lfs diff=lfs merge=lfs -text
+mtuspecific/3dnew.html filter=lfs diff=lfs merge=lfs -text
+uploader/uploads/a1b1e9ca-7d76-4663-b48c-12f284304475/MozzarellaTW.csv filter=lfs diff=lfs merge=lfs -text
+uploader/uploads/a1b1e9ca-7d76-4663-b48c-12f284304475/MozzarellaTW.xlsx filter=lfs diff=lfs merge=lfs -text
+uploader/uploads/a1b1e9ca-7d76-4663-b48c-12f284304475/OnionTW.xlsx filter=lfs diff=lfs merge=lfs -text
+uploader/uploads/demo/MozzarellaTW.xlsx filter=lfs diff=lfs merge=lfs -text
+uploader/uploads/demo/OnionTW.xlsx filter=lfs diff=lfs merge=lfs -text

DrPepper.xlsx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:82c57fbeabc10f410e553fc7f9fae2fbcb7520f4aa532f0fb7b03dbf20b132c9
+size 2985674

Untitled.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "844c876f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from bertopic import BERTopic\n",
+    "import pandas as pd \n",
+    "import openai\n",
+    "from bertopic.representation import OpenAI\n",
+    "from umap import UMAP\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "560143ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# df = pd.read_csv('mtu_merged_v3.csv')\n",
+    "df = pd.read_csv('NikeTwitter.csv')\n",
+    "\n",
+    "docs = df['translated_text'].tolist()\n",
+    "\n",
+    "docs = [str(x).replace(\"#\",\" \") for x in docs]\n",
+    "docs = [x for x in docs if len(x) > 30]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a579594d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f40e914627074089906a1d35d06f55b4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Batches:   0%|          | 0/30 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-05-17 08:49:56,734 - BERTopic - Transformed documents to Embeddings\n",
+      "2023-05-17 08:50:01,377 - BERTopic - Reduced dimensionality\n",
+      "2023-05-17 08:50:01,415 - BERTopic - Clustered reduced embeddings\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.feature_extraction.text import CountVectorizer\n",
+    "\n",
+    "# we add this to remove stopwords\n",
+    "vectorizer_model = CountVectorizer(ngram_range=(1, 2), stop_words=\"english\")\n",
+    "\n",
+    "model = BERTopic(\n",
+    "    vectorizer_model=vectorizer_model,\n",
+    "    language='english', calculate_probabilities=True,\n",
+    "    verbose=True\n",
+    ")\n",
+    "topics, probs = model.fit_transform(docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b4803af3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# freq = model.get_document_info(docs)\n",
+    "# freq.head(10)\n",
+    "from sentence_transformers import SentenceTransformer\n",
+    "\n",
+    "sentence_model = SentenceTransformer(\"all-MiniLM-L6-v2\")\n",
+    "embeddings = sentence_model.encode(docs, show_progress_bar=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4aba61ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "2023-05-17 08:50:09,299 - BERTopic - Reduced dimensionality\n",
+      "2023-05-17 08:50:09,315 - BERTopic - Clustered reduced embeddings\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Train BERTopic\n",
+    "topic_model = BERTopic().fit(docs, embeddings)\n",
+    "\n",
+    "# Run the visualization with the original embeddings\n",
+    "topic_model.visualize_documents(docs, embeddings=embeddings)\n",
+    "\n",
+    "reduced_embeddings = UMAP(n_neighbors=10, n_components=2, min_dist=0.0, metric='cosine').fit_transform(embeddings)\n",
+    "model.visualize_documents(docs,embeddings=embeddings)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "551db1f1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc8ece88",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create your representation model\n",
+    "openai.api_key = \"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\"\n",
+    "\n",
+    "\n",
+    "#prompt = \"I have the following documents: [DOCUMENTS] \\nThese documents are about the following topic: '\"\n",
+    "#representation_model = OpenAI(prompt=prompt)\n",
+    "\n",
+    "# representation_model = OpenAI()\n",
+    "\n",
+    "\n",
+    "\n",
+    "seed_topic_list = [\n",
+    "    ['nature','photography','trees','sunset','view'],\n",
+    "    ['hiking','explore', 'running', 'biking','bike', 'walk', 'csodasmagyarorszag'],\n",
+    "    ['zoo','lions','animals', 'aquarium', 'wildlife'],\n",
+    "    ['wine', 'drinking', 'drink', 'winery'],\n",
+    "    ['lake','balaton', 'sea'],\n",
+    "    ['lake', 'velence']\n",
+    "]\n",
+    "\n",
+    "\n",
+    "# Use the representation model in BERTopic on top of the default pipeline\n",
+    "topic_model = BERTopic(seed_topic_list=seed_topic_list, nr_topics=7)\n",
+    "\n",
+    "#topic_model.reduce_topics(docs, nr_topics=8, representation_model=representation_model)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "97b97844",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "topics, probs = topic_model.fit_transform(docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0c5d0a1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "topic_model.get_document_info(docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f29e0d00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# topic_model.update_topics(docs, n_gram_range=(1, 3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a828823b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "topic_model.get_topics()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c02c597",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "topic_model.reduce_topics(docs, nr_topics=10)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c031065",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d267dd67",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "topic_model.get_topics()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

archivePrompts.txt

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+
+
+        # executive_summary_template = """
+        #  {industry} {word_count} 
+        # Imagine you are an Elite Analyst, Expert Sociologist, and Data Guru, Your task is to leverage your invaluable expertise in crafting a comprehensive and insightful executive summary.
+        # You have been tasked with analyzing a large corpus of blog posts related to tourism. Your goal is to summarize the key points and insights from these blog posts, and to provide recommendations for future tourism practices.
+
+        # To begin, analyze the blog posts from below:
+        # {summaries1}
+        # \n\n
+        # ...to identify the most common and hot topics. What are the main themes that emerge from the blog posts? Are there any recurring topics or issues that are discussed frequently?
+        # (proposed headings are:
+        # Common and Hot Topics in Tourism Blog Posts
+        # Key Trends in Tourism: Implications for Tourists and the Industry
+        # Observations and Key Findings from Tourism Blog Posts
+        # Takeaways and Recommendations for Future Tourism Practices)
+
+
+        # Next, examine and interpret the key trends that you observe in the blog posts. How have these trends evolved over time? What are the implications of these trends for tourists, travel agencies, and the tourism industry as a whole?
+
+        # Structure your analysis into two main sections: Observations and Key Findings. In the Observations section, provide detailed analysis and examples of the common/hot topics and key trends that you identified in the blog posts. Use supporting evidence and examples from the blog posts to illustrate your observations.
+
+        # In the Key Findings section, summarize the key insights and conclusions that you draw from your analysis. What are the main takeaways from the blog posts in terms of tourism practices? What recommendations can you make for future tourism practices based on your findings?
+
+        # Finally, prepare a clear and concise takeaway message that summarizes the main findings of your analysis. What are the key insights that tourists, travel agencies, and the tourism industry as a whole can take away from your analysis of these blog posts?
+
+        # Use your creativity and writing skills to generate a comprehensive and insightful summary of the tourism blog posts in your data corpus.
+
+        # Add subheaders, and write at least 500 words.
+        # Use markdown formatting:
+        # """
+
+        # executive_summary_template = """
+        #  {industry} {word_count} 
+        # You have been tasked with analyzing a large corpus of news/articles related to tourism. Your goal is to summarize the key points and insights from these news/articles, and to provide recommendations for future tourism practices.
+
+        # To begin, analyze the news/articles from below summaries:
+        # {summaries1}
+        # \n\n
+        # ...to identify the most common and hot topics. What are the main themes that emerge from the data? Are there any recurring topics or issues that are discussed frequently?
+        # (proposed headings are:
+        # Common and Hot Topics in news/articles related to Tourism
+        # Key Trends in Tourism: Implications for Tourists and the Industry
+        # Observations and Key Findings from Tourism news/articles
+        # Takeaways and Recommendations for Future Tourism Practices)
+
+        # Don't hallucinate, use only the data given summaries.
+
+        # Use your creativity and writing skills to generate a comprehensive and insightful summary of the tourism news/articles posts in your data corpus.
+
+        # Add subheaders, and write at least 700 words.
+        # Use markdown formatting:
+        # """
+        # executive_summary_template = """
+        #  {industry} {word_count} 
+        # You have been tasked with analyzing a large corpus of blog posts related to tourism. Your goal is to summarize the key points and insights from these blog posts, and to provide recommendations for future tourism practices.
+
+        # To begin, analyze the blog posts from below summaries:
+        # {summaries1}
+        # \n\n
+        # ...to identify the most common and hot topics. What are the main themes that emerge from the data? Are there any recurring topics or issues that are discussed frequently?
+        # (proposed headings are:
+        # Common and Hot Topics in blog posts related to Tourism
+        # Key Trends in Tourism: Implications for Tourists and the Industry
+        # Observations and Key Findings from Tourism blog posts
+        # Takeaways and Recommendations for Future Tourism Practices)
+
+        # Don't hallucinate, use only the data given summaries.
+
+        # Use your creativity and writing skills to generate a comprehensive and insightful summary of the tourism blog posts posts in your data corpus.
+
+        # Add subheaders, and write at least 700 words.
+        # Use markdown formatting:
+        # """

dev.sh

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+gradio gradio_app.py app

gradio_app.py

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+# %%
+import datetime
+import gradio as gr
+from io import BytesIO
+import numpy as np
+import numpy as np
+import tiktoken
+import pandas as pd
+from utils.functions import load_csv
+from wordcloud import WordCloud
+import matplotlib.pyplot as plt
+plt.switch_backend('Agg')
+import requests
+import umap
+from sklearn.feature_extraction.text import TfidfVectorizer
+import hdbscan
+import plotly.express as px
+import plotly.graph_objects as go
+import plotly.express as px
+from langchain.chat_models import ChatOpenAI
+
+import os
+from langchain.agents import load_tools
+from langchain.agents import initialize_agent, create_pandas_dataframe_agent
+from langchain.llms import OpenAI
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import gradio as gr
+import openai
+import pandas as pd
+import numpy as np
+import re
+import whisper
+import openai
+import pandas as pd
+import networkx as nx
+import matplotlib.pyplot as plt
+
+import networkx as nx
+import matplotlib.pyplot as plt
+from langchain import OpenAI, PromptTemplate, LLMChain
+
+from langchain.agents import load_tools
+from langchain.agents import initialize_agent
+from langchain.llms import OpenAI
+from langchain.document_loaders import YoutubeLoader
+from langchain.chains.summarize import load_summarize_chain
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter, TokenTextSplitter
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+from langchain.document_loaders import YoutubeLoader
+import time
+import re
+import pinecone
+import pandas as pd
+from sentence_transformers import SentenceTransformer, util
+
+#import numpy as np
+from langchain.embeddings import HuggingFaceEmbeddings, OpenAIEmbeddings
+from sklearn.metrics import silhouette_score
+import torch
+import nltk
+nltk.download('vader_lexicon')
+
+
+from dotenv import load_dotenv
+load_dotenv()
+gradio_css = os.getenv("GRADIO_CSS")
+gradio_js = os.getenv("GRADIO_JS")
+rubik_backend = os.getenv("RUBIK_BACKEND")
+openapi_key = os.getenv("OPENAI_API_KEY")
+wolfram_alpha_appid = os.getenv("WOLFRAM_ALPHA_APPID")
+
+#for versioning
+ra = np.random.randint(1000000)
+
+os.environ['OPENAI_API_KEY'] = openapi_key
+os.environ['WOLFRAM_ALPHA_APPID'] = wolfram_alpha_appid
+
+
+def get_key(item):
+    return item['label']
+
+def get_emotion_bertweet(dataset):
+    tokenizer4 = AutoTokenizer.from_pretrained("finiteautomata/bertweet-base-emotion-analysis", truncation=True)
+    model4 = AutoModelForSequenceClassification.from_pretrained("finiteautomata/bertweet-base-emotion-analysis")
+    nlp = pipeline('sentiment-analysis', model=model4,
+                    tokenizer=tokenizer4, top_k=6, truncation=True, device=device)
+
+    top_emotion = []
+    # apply emotion model on data and get the labels and scores
+    for i in range(len(dataset)):
+        label = []
+        score = []
+        jsonfile = (nlp(dataset['translated_text'].iloc[i]))
+        jsonfile[0].sort(key=get_key)
+        for j in range(0, 6):
+            jsonfile2 = np.array(jsonfile)
+            label.append(jsonfile2[0][j]['label'])
+            score.append(jsonfile2[0][j]['score'])
+
+
+        top_emotion.append(label[score.index(max(score))])
+    dataset['top_emotion_bertweet'] = top_emotion
+    print(jsonfile2)
+    return dataset
+
+
+model_name = "sentence-transformers/all-MiniLM-L6-v2"
+hf = HuggingFaceEmbeddings(model_name=model_name)
+embeddings = OpenAIEmbeddings()
+
+# pinecone.init(
+#     api_key='ENTER API KEY HERE',
+#     environment='us-central1-gcp'
+# )
+# index_name = 'openaigradio'
+def markdown_to_html(md_string):
+    # html_string = markdown.markdown(md_string)
+    return md_string
+
+tokenizer4 = AutoTokenizer.from_pretrained("finiteautomata/bertweet-base-emotion-analysis", truncation=True)
+model4 = AutoModelForSequenceClassification.from_pretrained("finiteautomata/bertweet-base-emotion-analysis")
+
+openai.api_key = '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'
+model_whisp = whisper.load_model("base")
+
+llm = OpenAI(temperature=0.2, model_name='text-davinci-003', max_tokens=1000, top_p=1)
+
+model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+# check if cpu or gpu
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+model = model.to(device)
+
+# %%
+Industries = ['Agriculture',
+ 'Automobile Manufacturing',
+ 'Banking and Finance',
+ 'Biotechnology',
+ 'Chemicals and Petrochemicals',
+ 'Construction and Engineering',
+ 'Consumer Goods and Retail',
+ 'Education',
+ 'Electronics',
+ 'Energy (Oil, Gas, Coal, and Renewable Energy)',
+ 'Entertainment and Media',
+ 'Food and Beverage',
+ 'Healthcare and Pharmaceuticals',
+ 'Hospitality, Travel, and Tourism',
+ 'Information Technology (IT) and Software',
+ 'Insurance',
+ 'Manufacturing (various sectors)',
+ 'Mining and Metals',
+ 'Real Estate',
+ 'Renewable Energy (Solar, Wind, Hydro, Geothermal)',
+ 'Telecommunications',
+ 'Textiles and Apparel',
+ 'Transportation and Logistics',
+ 'Utilities (Electricity, Water, Gas)',
+ 'Waste Management and Environmental Services']
+
+
+
+def get_memory():
+    memory_string = ''
+    for i,j in memory.items():
+        print(i, j)
+        memory_string += str(j) + '\n'
+    return memory_string
+
+
+def check_words_in_string(word_list, input_string, case=False):
+
+    input_string = input_string.lower()
+
+    # Convert words to lowercase if case is False
+    word_list = [word.lower() if case else word for word in word_list]
+
+    # Check if any word is in the input_string
+    result = any(word in input_string for word in word_list)
+
+    # check if True
+    if result:
+        return True
+    else:
+        return False
+
+
+# Will be used by the Langchain chatbot
+
+words = ['rows', 'data', 'length', 'dataset','plot', 'col','columns','column', 'max', 'min', 'minimum', 'maximum', 'visualize','visualise','represent','graph','chart','plot','diagram','illustrate','show','depict','display','count','number','sum','total','aggregate','trend','pattern','distribution','average','linechart','scatter','barchart','piechart','histogram','boxplot','heatmap','correlation','regression','forecast','predict']
+
+memory = {'agent':[], 'user':[]}
+
+
+
+def get_topic_cluster(dataframe, graph_type = None,filename = None):
+    print(filename)
+    if (dataframe is None):
+        # return None,None, '<h1>Please click "Launch" on the left sidebar.</h1>', '<h1>Please click "Launch" on the left.</h1>', "Executive Summary"
+        return None, None, None, None, None, '<span id="clusterx"></span>',  '<h1>Please click "Launch" on the left sidebar.</h1>', "<span class='hsub'>Analysis:</span>Topic Cluster"
+
+    reduce_dim = umap.UMAP(
+                    n_components=3, n_neighbors=8, min_dist=0.55)
+
+    df = dataframe.copy()
+
+    # some cleaning for reddit datasets
+    df = df[df['translated_text'] != 'nan']
+    df = df[df['translated_text'] != '[deleted]']
+    df = df[df['translated_text'] != '[removed]']
+
+    # remove a list of words in this column
+    remWords = ['easter', 'spring', 'nature', 'turism','csodasmagyarorszag','csodalatosmagyarorszag','Easter','Spring','#húsvét','EASTER']
+
+    # remWords = ['nature', 'turism', 'csodasmagyarorszag','csodalatosmagyarorszag']
+    df['translated_text'] = df['translated_text'].apply(lambda x: ' '.join([word for word in x.split() if word not in (remWords)]))
+
+    # if filename + 'df3.csv' exists load csv
+    if os.path.exists(filename + 'df3.csv'):
+        df3 = pd.read_csv(filename + 'df3.csv')
+        df2 = df3
+    else:
+        def CleanTxt_quotes(text):
+            text = re.sub(r'https?:\/\/\S+', '', text)  # Remove hyperlinks
+            text = re.sub(r'http?:\/\/\S+', '', text)  # Remove hyperlinks
+            # if more than 5 mentions, remove all mention
+            if len(re.findall(r'@[A-Za-z0-9]+', text)) > 5:
+                text = re.sub(r'@[A-Za-z0-9]+', '', text)
+            # remove remWords words
+            text = re.sub(r'\b(?:{})\b'.format('|'.join(remWords)), '', text)
+            # if more than 4 hashtags, remove all hashtags
+            #text = re.sub(r'[^A-Za-z0-9.!?_#@]+', ' ', text)     # Remove non-alphanumeric characters except exclamation marks and question marks
+            text = re.sub(r'\s+', ' ', text)  # Remove extra whitespace
+            return text
+
+        df['clean_text'] = df['translated_text'].apply(lambda x: str(x))
+        df['clean_text'] = df['translated_text'].apply(lambda x: CleanTxt_quotes(x))
+        df['origi_text'] = df['text']
+        df['origi_text'] = df['text']
+        embedding = np.array([np.array(xi)
+                                for xi in df.embeddings])
+
+        umap_embeddings = reduce_dim.fit_transform(embedding)
+
+        print('umap_embeddings', umap_embeddings.shape)
+
+        # CHECK THIS LINE
+        df['x'] = umap_embeddings[:, 0]
+        df['y'] = umap_embeddings[:, 1]
+        df['z'] = umap_embeddings[:, 2]
+
+        df.dropna(inplace=True)
+
+        hdbscan_min_samples = 1
+        hdbscan_minimal_cluster_size = int(len(df) * 0.01+34)
+
+        # hdbscan_minimal_cluster_size = 7
+        # hdbscan_min_samples = 10
+
+        cluster = hdbscan.HDBSCAN(
+            min_cluster_size=hdbscan_minimal_cluster_size,
+            metric='euclidean',
+            cluster_selection_epsilon=0.007,
+            cluster_selection_method='leaf',
+            algorithm='best',
+            prediction_data=False,
+            min_samples=hdbscan_min_samples).fit(df[['x', 'y', 'z']])
+
+        cluster_analysis = len(pd.Series(cluster.labels_).unique())
+        print('Number of Sentences = ', len(df))
+        print('Number of Clusters = ',  cluster_analysis, '/n')
+
+        df_cluster = pd.DataFrame(
+            pd.DataFrame(cluster.labels_).value_counts())
+        clusters = pd.DataFrame(cluster.labels_)
+
+        # percent_unlabelled = round((len(df[clusters[0] == -1]) / len(df)) * 100, 2)
+        # print('The percentage of unlabelled sentences is: ', percent_unlabelled, '%')
+
+        # reindex
+        df.reset_index(inplace=True, drop=True)
+
+        print(len(df[clusters[0] == -1]))
+
+        for i in range(0, cluster_analysis):
+            print('Cluster ', i, ' has ', len(
+                df[clusters[0] == i]), ' sentences')
+
+        print(df_cluster.index)
+
+        from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
+
+        def get_tfidf_top_features(documents, n_top=4):
+            tfidf_vectorizer = TfidfVectorizer(
+                min_df=0.05,
+                max_df=0.95, max_features=10, 
+                stop_words='english')
+            tfidf = tfidf_vectorizer.fit_transform(documents)
+            importance = np.argsort(np.asarray(
+                tfidf.sum(axis=0)).ravel())[::-1]
+            tfidf_feature_names = np.array(
+                tfidf_vectorizer.get_feature_names())
+            return tfidf_feature_names[importance[:n_top]]
+
+        cluster_names = pd.DataFrame(
+            columns=['cluster_name', 'embed_index'])
+
+        for i in range(cluster_analysis):
+            try:
+                print(get_tfidf_top_features(
+                    df['clean_text'][clusters[0] == i]))
+
+                clstr_nm = get_tfidf_top_features(
+                    df['clean_text'][clusters[0] == i])
+                clstr_idx = df['clean_text'][clusters[0] == i].index
+                cluster_names = cluster_names.append(
+                    {'cluster_name': clstr_nm, 'embed_index': clstr_idx}, ignore_index=True)
+
+            except Exception as e:
+                print(e)
+                # cluster_name.append('NULL')
+                pass
+
+        cluster_names['cluster_name'] = cluster_names['cluster_name'].astype(
+            str)
+        cluster_names['cluster_name'] = cluster_names['cluster_name'].str.replace(
+            '[', '')
+        cluster_names['cluster_name'] = cluster_names['cluster_name'].str.replace(
+            ']', '')
+        cluster_names['cluster_name'] = cluster_names['cluster_name'].str.replace(
+            "'", '')
+        cluster_names['cluster_name'] = cluster_names['cluster_name'].str.replace(
+            " ", '-')
+
+        clusters_names = cluster_names.explode('embed_index')
+
+        df2 = df.merge(clusters_names, left_index=True,
+                        right_on='embed_index')
+
+        df2['cluster_name_str'] = df2['cluster_name'].apply(
+            lambda x: str(x))
+        # assign a int value to each unique cluster name in df3
+        df2['cluster_number'] = df2['cluster_name_str'].astype(
+            'category').cat.codes
+
+        df2['trimmed_text'] = df2['clean_text'].str[:175]
+        df2['trimmed_origi_text'] = df2['origi_text'].str[:175]
+
+        print(df2.head())
+
+        df3 = df2[['x', 'y', 'z', 'cluster_number',
+                    'cluster_name_str', 'trimmed_text','trimmed_origi_text']]
+
+
+
+    #################################################### GET CLUSTER NAME #############################################
+
+        df2['gpt_cluster'] = ''
+        df3['gpt_cluster'] = ''
+
+        for cluster in df3['cluster_name_str'].unique():
+            each_cluster = df3[df3['cluster_name_str'] == cluster]
+
+            docs = '\n'.join(np.random.choice(each_cluster['trimmed_text'], 50))
+
+            response3 = openai.ChatCompletion.create(
+                    model="gpt-3.5-turbo",
+                    temperature=0.3,
+                    max_tokens=300,
+                    top_p=1,
+                    # stream=True,
+                    messages=[
+                        {"role": "user", "content": f'Given a list of keywords {cluster}, and documents present in the cluster : {docs}; assign the most relevant topic name for this cluster : \n\n Cluster Name : '},
+                    ]
+                )['choices'][0]['message']['content']
+
+            df3.loc[df3['cluster_name_str'] == cluster, 'gpt_cluster'] = response3
+            df2.loc[df2['cluster_name_str'] == cluster, 'gpt_cluster'] = response3
+
+       
+
+    # print(df3['cluster_name_str'])
+    # xi = 0
+    # for cluster in df3['cluster_name_str'].unique():
+    #     xi += 1
+    #     df3.loc[df3['cluster_name_str'] == cluster, 'gpt_cluster'] = cluster#"cluster " + str(xi)
+    #     df2.loc[df2['cluster_name_str'] == cluster, 'gpt_cluster'] = cluster#"cluster " + str(xi)
+
+    # save df3
+    df3.to_csv(filename + 'df3.csv', index=False, encoding='utf-8-sig')
+        
+    if len(df3) > 10000:
+        dot_size = 1
+    else:
+        dot_size = 4
+
+
+    color_scale = px.colors.sequential.Viridis
+    color_list = ['#FF0000', '#FF0000', '#FF0000', '#FF0000', '#FF0000', '#FF0000']
+
+    fig = px.scatter_3d(df3, x='x', y='y', z='z', color='gpt_cluster', hover_name='trimmed_text', hover_data={
+                        'x': False, 'y': False, 'z': False, 'cluster_name_str': False, 'cluster_number': False, 'gpt_cluster': False}, opacity=1, template='plotly_white')
+
+    fig.update_traces(marker=dict(size=dot_size))
+
+    fig.add_trace(go.Scatter3d(x=[0], y=[0], z=[0], mode='markers', marker=dict(
+        size=0.1, color='white'), showlegend=True, name=' ', hoverinfo='none'))
+
+    # legend on the right side
+    fig.update_layout(legend=dict(
+        bgcolor='rgba(17,17,17,0)', 
+        xanchor='auto', 
+        yanchor='auto',
+        x=0.8,  # Adjust the x position of the legend
+        y=0.2,  # Adjust the y position of the legend
+        bordercolor='rgba(17,17,17,0)',
+        borderwidth=0,
+    ))
+
+    # fig.update_layout(scene=dict(
+    #     xaxis=dict(
+    #         title=' ',
+    #         nticks=0,
+    #         # backgroundcolor="rgb(0, 0, 0, 1)",
+    #         gridcolor="rgba(17,17,17, 0)",
+    #         showbackground=True,
+    #         zerolinecolor="rgba(17,17,17, 0)",
+    #         zeroline=False,
+    #         showgrid=True,
+    #         showticklabels=False,
+    #         showspikes=False
+    #     ),
+    #     # hide ticks
+
+
+    #     yaxis=dict(
+    #         # name
+    #         title=' ',
+    #         nticks=0,
+    #         # backgroundcolor="rgb(0, 0, 0, 1)",
+    #         gridcolor="rgba(17,17,17, 0)",
+    #         showbackground=True,
+    #         zerolinecolor="rgba(17,17,17, 0)",
+    #         zeroline=False,
+    #         showgrid=True,
+    #         showticklabels=False,
+    #         showspikes=False
+    #     ),
+
+
+
+    #     zaxis=dict(
+    #         # name
+    #         title=' ',
+    #         nticks=0,
+    #         # backgroundcolor="rgba(0, 0, 0, 1)",
+    #         gridcolor="rgba(17,17,17, 0)",
+    #         showbackground=True,
+    #         zerolinecolor="rgba(17,17,17, 0)",
+    #         zeroline=False,
+    #         showgrid=True,
+    #         showticklabels=False,
+    #         showspikes=False),)
+    #     # tickvals=[],),
+    # )
+
+    fig.update_layout(coloraxis_showscale=False, width=1300, height=750, legend=dict(x=0, y=1, traceorder='normal', font=dict(size=14, color='black'), bgcolor='rgba(17,17,17,0)', bordercolor='rgba(17,17,17,0)', borderwidth=0))
+
+
+    # TO ADD AN IMAGE UNCOMMENT
+    
+    # fig.add_layout_image(
+    #     dict(
+    #         source=<SOURCE>,
+    #         xref="x",
+    #         yref="y",
+    #         x=-1,
+    #         y=3.8,
+    #         # xanchor = "left",
+    #         # yanchor = "top",
+    #         sizex=.4,
+    #         sizey=.4,
+    #         opacity=1,
+    #         layer="above",
+    #     )
+    # )  
+
+    fig.update_layout(legend={'itemsizing': 'constant'}, legend_title_text=' ', legend_title_font_color='black',
+                        legend_font_color='black', legend_font_size=14, legend_bgcolor='rgba(17,17,17,0)', legend_bordercolor='rgba(17,17,17,0)', legend_borderwidth=2)
+
+    # , title_font_size=30, title_font_family='Arial', title_font_color='white', title_x=0.06, title_y=0.95, title_xanchor='left', title_yanchor='top', title_text='Cluster of Emotions for {}/n                                        n = {}'.format(subreddit, len(dataset_umap)), margin=dict(l=0, r=0, b=0, t=0, pad=0))
+    fig.update_layout(scene_camera_eye=dict(x=0.87, y=-0.88, z=0.84), scene_camera_center=dict(
+        x=0, y=0, z=0), template='plotly_white', hovermode='x unified', margin=dict(l=0, r=0, b=0, t=0, pad=2))
+
+    fig.update_layout(coloraxis_showscale=True)
+    fig.update_xaxes(showticklabels=True, showgrid=False, zeroline=False,
+                        showline=True, automargin=False, showspikes=False)
+    fig.update_yaxes(showticklabels=True, showgrid=False, zeroline=False,
+                        showline=True, automargin=False, showspikes=False)
+
+    
+    #full_html=False, include_plotlyjs='cdn', default_height='750px', default_width='1500px', config={'displaylogo': False, 'modeBarButtonsToRemove': ['zoom2d', 'pan2d', 'select2d', 'lasso2d', 'zoomIn2d', 'zoomOut2d', 'autoScale2d', 'resetScale2d', 'hoverClosestCartesian', 'hoverCompareCartesian', 'zoom3d', 'pan3d', 'resetCameraDefault3d', 'resetCameraLastSave3d', 'hoverClosest3d', 'orbitRotation', 'tableRotation', 'zoomInGeo', 'zoomOutGeo', 'resetGeo', 'hoverClosestGeo', 'toImage', 'sendDataToCloud', 'hoverClosestGl2d', 'hoverClosestPie', 'toggleHover', 'resetViews', 'toggleSpikelines', 'resetViewMapbox']})}
+
+    
+    cluster_name = df3[['cluster_number', 'gpt_cluster']]
+    cluster_name = cluster_name.drop_duplicates()
+    cluster_name = cluster_name.sort_values(by=['cluster_number'])
+    cluster_name = cluster_name.reset_index(drop=True)
+    # create a list
+    cluster_name_list = cluster_name['gpt_cluster'].tolist()
+    cluster_name_list = '\n'.join(cluster_name_list)
+
+
+    # expor t to html
+    fig.write_html(filename + '3d.html')
+
+    Silhouette_Score = 'Silhouette score is : ', silhouette_score(df3[['x', 'y', 'z']], df3['gpt_cluster'],  metric='euclidean')
+
+    # get a dataframe of unique cluster names and their count
+
+    cluster_count = df3.groupby('gpt_cluster').agg({'cluster_number': 'count'}).reset_index()
+    cluster_count = cluster_count.rename(columns={'cluster_number': 'count', 'gpt_cluster': 'Cluster'})
+    
+    
+    # return fig, cluster_count, cluster_name_list, Silhouette_Score, df2
+    return fig, cluster_count, cluster_name_list, None, df2, '<span id="clusterx"></span>', "<b>Please check 'Graph' tab for more details.</b>", "<span class='hsub'>Analysis:</span>Topic Cluster"
+
+
+
+def get_executive_summary(dataframe=None, brand=None, industry=None, summaries=None,  csv_file= None, graph_type = None,filename = None, fullProjectData= None, sourceIndex = 0):
+    # if data_answer.txt exists, open and read
+
+    sourceData = fullProjectData['sources'][int(sourceIndex)]
+    externalPrompt = sourceData['content']['exec_sum']
+
+    if (dataframe is None):
+        return None,None,'<span id="executive"></span>', '<h1>Please click "Launch" on the left sidebar.</h1>',  "<span class='hsub'>Analysis:</span>Executive Summary"
+
+    if os.path.exists(filename + 'Executive Summary_CACHE.txt'):
+        with open(filename + 'Executive Summary_CACHE.txt', 'r') as f:
+            output_summary = f.read()
+
+        return output_summary, dataframe[['translated_text']], '<span id="executive"></span>', markdown_to_html(output_summary), "<span class='hsub'>Analysis:</span>Executive Summary"
+    else:
+        if brand is None:
+            brand = ' '
+
+        else :
+            brand = brand
+            try:
+                dataframe = dataframe[dataframe['translated_text'].str.contains(brand, case=False)]
+            except:
+                pass
+
+
+
+        text_splitter = TokenTextSplitter.from_tiktoken_encoder(
+            encoding_name='p50k_base',
+            chunk_size = 2000,
+        )
+
+        splitted_articles = text_splitter.split_text(''.join(dataframe['translated_text']))
+
+        summarize_template = """  {text} \n\n
+        Summarize the most relevant information for an executive summary from the above document:
+    
+        SUMMARY: """
+
+
+        prompt_template = PromptTemplate(input_variables=['text'], template=summarize_template)
+
+        summary_chain = LLMChain(llm=llm, prompt=prompt_template)
+
+        summaries = []
+        for i in splitted_articles:
+            summaries.append(summary_chain.run(i))
+
+
+
+        summaries1 = '/n'.join(summaries)
+        word_count = 500
+        
+        #If needed, guess the industry
+        # industry_template = PromptTemplate(input_variables=['summaries'], template=extract_industry)
+        # summary_chain = LLMChain(llm=llm, prompt=industry_template)
+        # industry = summary_chain.run(summaries)
+
+        #Check size of output and go in a 2nd loop if it's too big
+        encoding = tiktoken.get_encoding('p50k_base')
+        
+        if len(encoding.encode(summaries1)) > 2000:
+            # return only first 2000 tokens
+            summaries1 = encoding.decode(encoding.encode(summaries1)[:2000])
+
+
+        executive_summary_template = '''Imagine you are an Elite Analyst, Expert Sociologist, and Data Guru,
+        Your task is to leverage your invaluable expertise in crafting a comprehensive and insightful {word_count} words executive summary tailored for C-level executives and decision-makers in {industry}.
+        The summary should synthesize information from various data sources, incorporate relevant cultural and contextual elements, and provide valuable insights that can drive strategic decision-making.
+        Please ensure that your analysis meets the following high-level objectives:
+        Thoroughly examine and interpret the key trends, patterns, and insights derived from the following data sources:
+        {summaries1}
+
+        Articulate the implications and opportunities for {industry}, keeping in mind the needs and challenges of the industry.
+        Consider the cultural, social, and contextual nuances present in the data, drawing on your sociological expertise to ensure the summary remains relevant and insightful across diverse audiences.
+        Identify any potential risks or challenges that might arise from the data, providing strategic recommendations for mitigating these issues.
+        Present the information in a clear, concise, and engaging manner that captures the attention of busy executives and effectively communicates the key insights.
+        Leverage your data expertise to ensure the accuracy, reliability, and relevance of the information presented in the summary. Make us benefit from your unique expertise and insights.
+
+
+        Using markdown formatting, write a {word_count} word SEQ-optimized Executive Summary. Write a click worthy short titles. Add a key takeaway
+        section at the end. Use the seed keyword as the first H2. Always use a combination of paragraphs, lists, and tables for a better reader experience. For the styling of the output, please include headers for different sections, and use bullet points where applicable to organize the key insights. 
+        To avoid repetition, vary the sentence structure and word choice when presenting information from different data sources or discussing various trends, insights, or opportunities. 
+        Using synonyms, alternate phrasings, and modifying sentence structure can help keep the text engaging and fresh for readers.
+        To avoid repetition, vary the sentence structure and word choice when presenting information from different data sources or discussing various trends, insights, or opportunities. Using synonyms, alternate phrasings, and modifying sentence structure can help keep the text engaging and fresh for readers.
+        In order to maintain reader engagement and deliver a captivating text, please ensure that you diversify sentence structure and word choice when presenting insights, trends, or opportunities from different data sources. Employ synonyms, alternative expressions, and varied sentence patterns to provide a fresh and dynamic reading experience.
+        \n\n
+
+        '''
+
+        if externalPrompt and externalPrompt != "":
+            executive_summary_template = externalPrompt
+
+        prompt = PromptTemplate(template=executive_summary_template, input_variables=['industry', 'word_count', 'summaries1'])
+        print("start sum")
+        # llm2 = OpenAI(temperature=0.2, model_name='gpt-4', max_tokens=1000, top_p=1)
+
+        # llm2 = ChatOpenAI(temperature=0.2, model_name='gpt-4', max_tokens=1000, top_p=1)
+        executive_chain = LLMChain(llm=llm, prompt=prompt)
+
+        output_summary = executive_chain.run(industry=industry, word_count=word_count, summaries1=summaries1)
+
+
+    # output_summary = executive_chain.run(industry=industry, word_count=word_count, summaries1=summaries1)
+    with open(filename + "Executive Summary_CACHE.txt", "a") as f:
+        try:
+            f.write(output_summary)
+        except:
+            pass
+
+    # dataframe.rename(columns={'translated_text': 'Table'}, inplace=True)
+    # return("# Executive summary" + output_summary, dataframe[['translated_text']], markdown_to_html(output_summary), 1, markdown_to_html("# Executive Summary\n\n" + output_summary),  "Executive Summary")
+    return output_summary, dataframe[['translated_text']], '<span id="executive"></span>', markdown_to_html(output_summary), "<span class='hsub'>Analysis:</span>Executive Summary"
+
+    return(output_summary, dataframe[['translated_text']][0:20], summaries, output_summary)
+
+
+
+
+
+
+
+def get_competitive(brand, industry, graph_type = None, filename = None,dataframe = None,fullProjectData= None, sourceIndex = 0):
+
+    sourceData = fullProjectData['sources'][int(sourceIndex)]
+    externalPrompt = sourceData['content']['competitor']
+
+    if dataframe is None:
+        return(None,'<span id="competitive"></span>', '<h1>Please click "Launch" on the left sidebar.</h1>', "<span class='hsub'>Analysis:</span>Competitor Analysis")
+ 
+    if os.path.exists(filename + 'Competitor_CACHE.txt'):
+        with open(filename + 'Competitor_CACHE.txt', 'r') as f:
+            output_summary = f.read()
+        return(output_summary,'<span id="competitive"></span>', markdown_to_html("# Competitor Analysis\n\n"+output_summary), "<span class='hsub'>Analysis:</span>Competitor Analysis")
+
+    else:
+        competitive_prompt = '''
+        Ignore all previous instructions. Do not rush. Make this impactful and clear.
+        •Read through all the bullet points and make sure you understand all the bullet points, before you start working.
+        • Act as a subject matter expert, Business analyst, social media expert and professor with 20 years of research experience.
+        [IMPORTANT INSTRUCTION]
+        Your singular task is to provide expert reports with key elements and useful content. Do not make up any information. Do not use jargon.
+        Start with a short paragraph introducing {brand} position in the market. This should be clear and impactfull.
+        •I want to learn more about the competitors of brand {brand} in this market {industry}.
+        [SEPARATOR]
+        •Use the advanced level of expertise in this market {industry} to create topics and subtopics with detailed notes, this will help provide confidence and clarity about the item being sought.
+        [SEPARATOR]
+        1 “Who are the 4 main competitors of {brand}?”
+        2 “What are the top 3 strengths and weaknesses of each of those competitors?”
+        3 “What are the unique selling points of our competitors?”
+        4 “In what unique ways do those competitors market their products/services?”
+        5 “What are the key trends in the {industry} that those competitors are capitalizing on?”
+        6 “What are the areas where those competitors excel compared to {brand}?”
+        7 “What are the areas where our competitors fall short compared to {brand}?”
+        8 “How do our products/services prices compare to those competitors in terms of quality, price positioning and range?”
+        9 “What are the common customer complaints about those competitors?”
+        10 “What are the opportunities for growth in the {industry} that competitors are not taking advantage of?”
+        •Break down the exercise into easy-to-follow steps.
+        •For each topic and/or subtopic provide a clear and informative summary that compare and contrast results..
+        •Identify common mistakes made when addressing those competitive points and address those with maximum clarity.
+        •Proofread content for accuracy, paying special attention to any terms that may have been substituted or omitted unintentionally.
+        Conclude with a brief overview of the competitive landscape for "brand" with the top 3 takeaways and opportunities. The format should be markdown, add subheaders (h2 only), format into nice paragraphs.'''
+
+        # hardcoded!!!
+        brand = "Nike"
+        industry = 'Textiles and Apparel'
+
+        prompt = PromptTemplate(template=competitive_prompt, input_variables=['industry', 'brand'])
+        competition_chain = LLMChain(llm=llm, prompt=prompt)
+
+        output_summary = competition_chain.run(industry=industry, brand=brand)
+        with open(filename + "Competitor_CACHE.txt", "a") as f:
+            try:
+                f.write(output_summary)
+            except:
+                pass
+
+        return(output_summary,'<span id="competitive"></span>', markdown_to_html("# Competitor Analysis\n\n"+output_summary), "<span class='hsub'>Analysis:</span>Competitor Analysis")
+
+# def get_topic_summary(dataframe, topics=None, brand=None, industry=None, graph_type = None, filename = None):
+def get_trend_summary(dataframe, topics=None, brand=None, industry=None, graph_type = None, filename = None,fullProjectData= None, sourceIndex = 0):
+
+    sourceData = fullProjectData['sources'][int(sourceIndex)]
+    externalPrompt = sourceData['content']['trend_analysis']
+
+    if (dataframe is None):
+        return None,None,  '<span id="trend"></span>','<h1>Please click "Launch" on the left sidebar.</h1>', "<span class='hsub'>Analysis:</span>Executive Summary"
+
+    if os.path.exists(filename + 'Trend Analysis_CACHE.txt'):
+        with open(filename + 'Trend Analysis_CACHE.txt', 'r') as f:
+            final_summary = f.read()
+
+        return(final_summary, dataframe[['translated_text']][0:20], '<span id="trend"></span>', markdown_to_html(final_summary), "<span class='hsub'>Analysis:</span>Trend Analysis")
+
+    else:
+
+        if brand is None:
+            brand = ''
+
+        else :
+            brand = brand
+            try:
+                dataframe = dataframe[dataframe['translated_text'].str.contains(brand, case=False)]
+            except:
+                pass
+
+        
+
+        text_splitter = TokenTextSplitter.from_tiktoken_encoder(
+            encoding_name='p50k_base',
+            chunk_size = 2000,
+        )
+
+
+        splitted_articles = text_splitter.split_text(''.join(dataframe['translated_text']))
+
+        summarize_template = """Summarize the most relevant information from the following document:
+
+        {text}
+
+        SUMMARY: """
+
+
+        prompt_template = PromptTemplate(input_variables=['text'], template=summarize_template)
+
+        summary_chain = LLMChain(llm=llm, prompt=prompt_template)
+
+        summaries = []
+        for i in splitted_articles:
+            summaries.append(summary_chain.run(i))
+
+
+        # split the summary into 2000 tokens chunks
+        text_splitter = TokenTextSplitter.from_tiktoken_encoder(
+            encoding_name='p50k_base',
+            chunk_size = 2000,
+        )
+
+
+        summaries2 = text_splitter.split_text(''.join(summaries))
+
+        word_count = 500
+        
+        topics = topics
+        final_summary = []
+
+        brand = "Nike"
+        industry = "Food and Beverage"
+        for summary_1 in summaries2:
+
+            topic_prompt = '''"Imagine you are an Elite Analyst and Trend Analysis Expert with extensive experience in identifying patterns and emerging themes from various data sources, such as social media, regular media, reviews, and survey data. Your task is to leverage your invaluable expertise in crafting a comprehensive and insightful trend analysis report tailored for {brand} within the {industry}. The objective is to provide valuable insights into shifts in consumer behavior, preferences, and market dynamics, enabling informed decision-making for C-level executives and decision-makers.
+
+            In your analysis of {word_count} words, ensure that you address the following key elements:
+
+            Topics : {topics}
+
+            Data: {summary}
+
+            Emerging Trends: Identify and discuss the key emerging trends in consumer behavior, preferences, and market dynamics within the {industry}. Examine the factors driving these trends and provide specific examples to illustrate your findings.
+
+            Impact on {brand}: Analyze how the identified trends are affecting or could potentially affect {brand}. Consider both opportunities and challenges that may arise from these trends, as well as any necessary adjustments to marketing strategies, product offerings, or customer service initiatives.
+
+            Recommendations: Based on the insights derived from the trend analysis, provide actionable recommendations for {brand} to stay ahead of the competition, capitalize on new opportunities, and address potential challenges. Consider innovations, partnerships, or targeted marketing campaigns that can help the company adapt to and benefit from the identified trends.
+
+            Ensure that your trend analysis report is clear, concise, and engaging for busy executives. Focus on providing actionable insights and recommendations that can inform the company's strategic direction. Draw on your expertise to ensure the accuracy, reliability, and relevance of the information presented in the analysis."
+
+
+            Using markdown formatting, write a {word_count} word SEQ-optimized Trend Analysis. Write a click worthy short titles. Add a key takeaway
+            section at the end. Use the seed keyword as the first H2. Always use a combination of paragraphs, lists, and tables for a better reader experience. For the styling of the output, please include headers for different sections, and use bullet points where applicable to organize the key insights. 
+            To avoid repetition, vary the sentence structure and word choice when presenting information. Using synonyms, alternate phrasings, and modifying sentence structure can help keep the text engaging and fresh for readers. \n\n
+
+            '''
+
+            prompt = PromptTemplate(template=topic_prompt, input_variables=['industry', 'topics', 'word_count', 'summary', 'brand'])
+
+            topic_chain = LLMChain(llm=llm, prompt=prompt)
+
+            topic_summary = topic_chain.run(industry=industry, topics = topics, word_count=word_count, summary=summary_1, brand=brand)
+
+            final_summary.append(topic_summary)
+
+        if len(final_summary) > 1:
+            topic_summary = ''.join(final_summary)
+
+            combination = '''{topic_summary}\n\nCombine the content from these articles into one; keeping the format and structure in place. \n\n##Trend Analysis:\n\n'''
+            prompt = PromptTemplate(template=combination, input_variables=['topic_summary'])
+            final_chain = LLMChain(llm=llm, prompt=prompt)
+            final_summary = final_chain.run(topic_summary=topic_summary)
+        
+        else:
+            final_summary = final_summary[0]
+
+        with open(filename + "Trend Analysis_CACHE.txt", "a") as f:
+            try:
+                f.write(final_summary)
+            except:
+                pass
+        
+        # dataframe.rename(columns={'translated_text': 'Table'}, inplace=True)
+
+        return("# Trend Analysis\n" + final_summary, dataframe[['translated_text']][0:20], '<span id="trend"></span>', markdown_to_html(''+final_summary), "<span class='hsub'>Analysis:</span>Trend Analysis")
+
+
+def get_SWOT(dataframe, brand = None, industry = None, exec_summary=None, graph_type= None, filename = None,fullProjectData= None, sourceIndex = 0):
+
+    sourceData = fullProjectData['sources'][int(sourceIndex)]
+    externalPrompt = sourceData['content']['swot_']
+
+    if (dataframe is None):
+        return(None,'<span id="swotx"></span>', '<h1>Please click "Launch" on the left sidebar.</h1>', "<span class='hsub'>Analysis:</span>SWOT")
+   
+
+    brand = 'Nike'
+    industry = 'Textiles and Apparel'
+    if brand is None:
+        brand = ' '
+
+    else :
+        brand = brand
+        try:
+            dataframe = dataframe[dataframe['translated_text'].str.contains(brand, case=False)]
+        except:
+            pass
+
+    
+    # if exec_summary is None:
+    #     
+    exec_summary = '''
+# Mozzarella Sticks: A Versatile Snack Food with Endless Possibilities
+
+## Introduction
+
+Mozzarella sticks are a popular snack food that can be enjoyed in a variety of ways. They can be eaten alone, dipped in sauces, used as a topping on pizzas, or even turned into vape flavors. Mozzarella sticks can also be used to make creative dishes such as a mozzarella stick bowl, a mozzarella stick cake, or a mozzarella stick twinkie. They can also be used as a prank, such as paying per mozzarella stick. Mozzarella sticks are a versatile food that can be enjoyed in many different ways.
+
+## Popularity
+
+Mozzarella sticks are a popular food item that can be enjoyed in many different ways. People have been experimenting with different recipes, such as a low-carb snack of apple, mozzarella stick, hummus, veggie, plain Greek yogurt, English cucumber, dill, peanut butter, and celery. There have also been attempts to create a stuffed crust pizza with a mozzarella stick, as well as a Korean corn dog with a French fry chunk and a half mozzarella stick inside. Mozzarella sticks can also be enjoyed with marinara sauce, ranch, ketchup, and other condiments.
+
+## Availability
+
+Mozzarella sticks are a popular snack food that can be found in many places. They can be eaten alone or as part of a meal, such as a burger or a pizza. They can also be used as an ingredient in dishes such as mac and cheese, risotto, and fried cauliflower. Mozzarella sticks can be found in many forms, such as deep-fried, baked, or grilled. They can also be paired with other foods, such as fruit, vegetables, and sauces. Mozzarella sticks are high in lactose and should be consumed in moderation.
+
+## International Appeal
+
+Mozzarella sticks are a popular dish enjoyed by people around the world. They can be made with a variety of ingredients, such as flour, Greek yogurt, turkey pepperoni, and cheese, and can be served with marinara sauce, butter, and olive oil. Mozzarella sticks are also popular in Czech, Slovak, and Polish cuisine. On International Cheese Day, people celebrate with cheese wedges, ooey gooey cheese pulls, and mozzarella sticks. There are a variety of recipes for mozzarella sticks, including a low-fat version with Greek yogurt, turkey pepperoni, and cheese. Mozzarella sticks can also be enjoyed with a variety of dips, such as marinara sauce, nacho cheese sauce, and homemade marinara sauce.
+
+## Uses
+
+Mozzarella sticks are a popular snack food that can be enjoyed in a variety of ways. They can be deep fried, grilled, or microwaved, and are often served with marinara sauce or ranch dressing. They can also be used as a topping for pizza, burgers, and ramen. Mozzarella sticks are also available in low-fat and dairy-free varieties. They are often served at fast food restaurants, such as Arby's, Burger King, and Sonic, and can be purchased in stores. Mozzarella sticks are a great snack for those looking for a quick meal or a tasty treat.
+
+## Health Benefits
+
+Mozzarella sticks are a popular food item that can be enjoyed in many different ways. They can be fried, microwaved, baked, or even wrapped in fruit roll-ups. They can be served with marinara sauce, ranch dressing, or even chocolate milk. Mozzarella sticks can also be used to make delicious dishes such as mac and cheese, chicken tenders, and jalapeno poppers. They can also be used to make sandwiches, tacos, and pizzas. Mozzarella sticks are a great way to add flavor and texture to any meal.
+
+## Implications and Opportunities
+
+Mozzarella sticks are a popular snack food that can be enjoyed in a variety of ways. They can be served with different sauces, as part of a pizza, or as part of a sandwich. They can also be used to make a variety of dishes, such as a scotch egg, a Camembert burger, or a mozzarella stick hybrid pizza. Mozzarella sticks can also be served with a variety of sides, such as fries, onion rings, and hash browns. Additionally, they can be used to make a variety of desserts, such as a mozzarella stick candle.
+
+Mozzarella sticks are a popular bar food and snack item that can be enjoyed in a variety of ways. They can be served as an appetizer, a side dish, or even as a main course.'''
+
+    #word_count = 500
+
+    with open(filename + "Executive Summary_CACHE.txt", "r") as f:
+        exec_summary = f.read()
+
+    # industry_template = PromptTemplate(input_variables=['summaries'], template=extract_industry)
+    # summary_chain = LLMChain(llm=llm, prompt=industry_template)
+    # industry = summary_chain.run(summaries)
+
+    brand = brand
+
+    industry = industry
+
+    # toolname = ['serpapi']
+    # tools = load_tools(toolname)
+    # agent = initialize_agent(tools=tools, llm=llm, agent='zero-shot-react-description', verbose=True)
+    # internet_content = agent.run(f'What is {brand}?')
+    
+
+
+    SWOT_analysis_template = '''Ignore all previous instructions. Do not rush. Make this impactful and clear.
+    •Read through all the bullet points and make sure you understand all the bullet points, before you start working.
+    Act as a subject matter expert, Business analyst, social media expert and professor with 20 years of research experience.
+
+    Here is an executive Summary for updated context : {exec_summary}
+
+    
+    [IMPORTANT INSTRUCTION]
+    Your singular task is to provide expert reports with key elements and useful content. Do not make up any information. Do not use jargon.
+    Introduction:
+    Start with a paragraph introducing
+
+    Now: return the SWOT for the brand {brand} in the {industry} industry.
+    example:
+    ## Strengths
+    - Strength 1
+    - Strength 2
+    ...
+    ## Weaknesses
+    - Weakness 1
+    - Weakness 2
+    ...
+    ## Opportunities
+    - Opportunity 1
+    - Opportunity 2
+    ...
+    ## Threats
+    - Threat 1
+    - Threat 2
+    ...
+    
+    SWOT formatted with markdown syntax: 
+
+    '''
+  
+    prompt = PromptTemplate(template=SWOT_analysis_template, input_variables=['industry', 'brand', 'exec_summary'])
+
+    SWOT_chain = LLMChain(llm=llm, prompt=prompt)
+
+    SWOT_summary = SWOT_chain.run(industry=industry, brand=brand, exec_summary=exec_summary)
+
+    return("" + SWOT_summary,'<span id="swotx"></span>', markdown_to_html(SWOT_summary + "<div id='isSwot'></div>") , "<span class='hsub'>Analysis:</span>SWOT")
+
+
+def emotional_mapping(dataframe, industry = None, graph_type = None, filename = None, fullProjectData = None, sourceIndex = 0):
+
+    sourceData = fullProjectData['sources'][int(sourceIndex)]
+    # externalPrompt = sourceData['content']['swot_']
+
+    if (dataframe is None):
+        return None,None, '<span id="sentiment"></span>', '<h1>Please click "Launch" on the left sidebar.</h1>', "<span class='hsub'>Analysis:</span>Sentiment Analysis"
+    
+    if os.path.exists(filename + 'Sentiment Analysis_CACHE.txt'):
+        # read this: emotional_count.to_csv(filename + "Sentiment Analysis_CACHE.csv", index=False)
+        emotional_count = pd.read_csv(filename + "Sentiment Analysis_CACHE.csv")
+        with open(filename + 'Sentiment Analysis_CACHE.txt', 'r') as f:
+            emotional_summary = f.read()
+
+        return(emotional_summary, emotional_count,'<span id="sentiment"></span>', markdown_to_html(""+emotional_summary), "<span class='hsub'>Analysis:</span>Sentiment Analysis")
+        # return output_summary, dataframe[['translated_text']], markdown_to_html(output_summary), markdown_to_html(output_summary), "Executive Summary"
+    else:
+        if 'top_emotion_bertweet' in dataframe.columns:
+            dataframe['emotion'] = dataframe['top_emotion_roberta'] #dataframe['top_emotion_bertweet']
+        
+        elif 'top_emotion_roberta' in dataframe.columns:
+            dataframe['emotion'] = dataframe['top_emotion_roberta']
+            
+        elif 'top_emotion_distilbert' in dataframe.columns:
+            dataframe['emotion'] = dataframe['top_emotion_distilbert']
+
+        elif 'top_emotion' in dataframe.columns:
+            dataframe['emotion'] = dataframe['top_emotion']
+
+        else:
+            dataframe = get_emotion_bertweet(dataframe)
+            dataframe['emotion'] = dataframe['top_emotion_bertweet']
+
+        word_count = 500
+
+
+        # industry_template = PromptTemplate(input_variables=['summaries'], template=extract_industry)
+        # summary_chain = LLMChain(llm=llm, prompt=industry_template)
+        # industry = summary_chain.run(summaries)
+
+
+        industry = industry
+
+        # get positive dataset
+        positive = dataframe[dataframe['polarity'] > 0]
+
+        # get negative dataset
+        negative = dataframe[dataframe['polarity'] < 0]
+
+        positive_emotions = []
+        negative_emotions = []
+
+        corpus_positive = {}
+        corpus_negative = {}
+
+        # Calculate the number of unique emotions for positive and negative datasets
+        num_positive_emotions = min(len(positive['emotion'].unique()), 3)
+        num_negative_emotions = min(len(negative['emotion'].unique()), 3)
+
+        # Loop through the positive emotions
+        for i in range(num_positive_emotions):
+            value = str(positive['emotion'].value_counts(normalize=True).index[i])
+            percent = str(round(positive['emotion'].value_counts(normalize=True)[i] * 100, 2)) + '%'
+            positive_emotions.append(value + ' ' + percent)
+
+            corpus_positive[value] = positive[positive['emotion'] == value]['translated_text'].tolist()
+
+        # Loop through the negative emotions
+        for i in range(num_negative_emotions):
+            value = str(negative['emotion'].value_counts(normalize=True).index[i])
+            percent = str(round(negative['emotion'].value_counts(normalize=True)[i] * 100, 2)) + '%'
+            negative_emotions.append(value + ' ' + percent)
+
+            corpus_negative[value] = negative[negative['emotion'] == value]['translated_text'].tolist()
+
+
+        emotion_summary = {}
+
+        text_splitter = TokenTextSplitter.from_tiktoken_encoder(
+                encoding_name='p50k_base',
+                chunk_size = 2000,
+            )
+        
+
+
+        for emotion, text in corpus_positive.items():
+
+            emotion_summary[emotion] = text_splitter.split_text(''.join(text))
+
+            # get first element
+            emotion_summary[emotion] = emotion_summary[emotion][0]
+
+            emotion_summarize_template = """  {text} \n\n
+            Summarize the text from the above document to answer this question : Why are people feeling {emotion} ? \n\n
+        
+            SUMMARY: """
+
+            prompt_template = PromptTemplate(input_variables=['text', 'emotion'], template=emotion_summarize_template)
+
+            summary_chain = LLMChain(llm=llm, prompt=prompt_template)
+
+            emotion_summary[emotion] = summary_chain.run(text=emotion_summary[emotion], emotion=emotion, industry=industry)
+
+        for emotion, text in corpus_negative.items():
+
+            emotion_summary[emotion] = text_splitter.split_text(''.join(text))
+
+            # get first element
+            emotion_summary[emotion] = emotion_summary[emotion][0]
+
+            emotion_summarize_template = """  {text} \n\n
+            Summarize the text from the above document to answer this question : Why are people feeling {emotion} ? \n\n
+
+            SUMMARY: """
+
+            prompt_template = PromptTemplate(input_variables=['text', 'emotion'], template=emotion_summarize_template)
+
+            emotion_summary[emotion] = summary_chain.run(text=emotion_summary[emotion], emotion=emotion, industry=industry)
+
+
+
+
+        executive_summary_template = '''Imagine you are an Elite psychologist, Analyst, and Data Guru. You are familiar with leading emotion measurement techniques and the latest developments in the field, 
+        
+        including the Plutchik index and Emotional Intensity Scale (EIS). 
+
+        Data Summary per emotions, leave 'other' emotions!: {all_emotions}
+        
+        Your task is to leverage your invaluable expertise in crafting an insightful {word_count} emotion-driven report tailored for C-level executives and decision-makers in {industry}. 
+        The objective is to provide valuable insights into the impact of the top emotions marketing and branding strategies and provoke lightbulb moments for our readers. Your analysis should provide valuable insights that can drive strategic decision-making based on the key emotions.
+
+        Structure the analysis in two main sections: Observations and Key Findings. In the Observations section, provide precise details about specific emotion measurements and their relation to the wants and needs expressed in the data. In the Key Findings section, focus on insightful content and compare and contrast the different emotions, revealing what's hiding behind the numbers and addressing both expressed and latent emotions.
+        Avoid jargon and broad terms in your analysis, ensuring that the content is clear, concise, and engaging.
+
+        Thoroughly examine and interpret the key trends, patterns, and insights derived from the key emotions .
+        Articulate the implications and opportunities based on the emotion levels, keeping in mind the needs and challenges of the {industry}.
+        Consider the cultural, social, and contextual nuances present in the data, drawing on your expertise to ensure the emotion analysis remains relevant and insightful across diverse audiences.
+        Leverage your psychological expertise to ensure the accuracy, reliability, and relevance of the information presented in the summary. Make us benefit from your unique expertise and insights.
+        Using markdown formatting, write a {word_count} word SEQ-optimized Executive Summary. Write a click worthy short titles. Add a key takeaway
+
+        section at the end. Use the seed keyword as the first H2. Always use a combination of paragraphs, lists, and tables for a better reader experience. For the styling of the output, please include headers for different sections, and use bullet points where applicable to organize the key insights. 
+        To avoid repetition, vary the sentence structure and word choice when presenting information from different data sources or discussing various trends, insights, or opportunities. 
+        Using synonyms, alternate phrasings, and modifying sentence structure can help keep the text engaging and fresh for readers. \n\n
+
+        '''
+
+        prompt = PromptTemplate(template=executive_summary_template, input_variables=['word_count', 'industry', 'all_emotions'])
+
+        executive_chain = LLMChain(llm=llm, prompt=prompt)
+
+        emotional_summary = executive_chain.run(industry=industry, word_count=word_count,  all_emotions=str(emotion_summary.items()))
+
+        emotional_count = dataframe.groupby('emotion').agg({'translated_text': 'count'}).reset_index()
+
+        emotional_count.to_csv(filename + "Sentiment Analysis_CACHE.csv", index=False)
+
+        with open(filename + "Sentiment Analysis_CACHE.txt", "a") as f:
+            try:
+                f.write(emotional_summary)
+            except:
+                pass
+
+        # dataframe.rename(columns={'emotion': 'Emotion'}, inplace=True)
+        
+        return(emotional_summary, emotional_count,'<span id="sentiment"></span>', markdown_to_html(""+emotional_summary), "<span class='hsub'>Analysis:</span>Sentiment Analysis")
+
+
+def generate_wordcloud(dataframe):
+    text = ' '.join(dataframe['translated_text'].tolist())
+    colors = ["#FF69B4", "#FFD700", "#FFA500", "#D3D3D3"]
+    wordcloud = WordCloud(max_font_size=300, max_words=800, width = 1600, height = 1200, background_color="white", colormap="Set2", color_func=lambda *args, **kwargs: colors[len(args[0]) % len(colors)]).generate(text)
+    return wordcloud.to_image()
+
+
+def get_polarity(dataframe):
+    df = dataframe.copy()
+    def get_sentiment_vader(text):
+        from nltk.sentiment.vader import SentimentIntensityAnalyzer
+        sid = SentimentIntensityAnalyzer()
+        return sid.polarity_scores(text)['compound']
+
+    df['translated_text'] = df['translated_text'].astype(str)
+    df['polarity'] = df['translated_text'].apply(lambda x: get_sentiment_vader(x))
+
+    fig = plt.figure(frameon=False, figsize=(16, 12))
+    fig = plt.figure(figsize=(15, 8))
+
+    # try :
+    if 'date' in df.columns:
+        df['date2'] = pd.to_datetime(df['date'], utc=True)
+    else:
+        return None, dataframe
+        print('no date, skipping polarity viz')
+    # except:
+    #     print("no/wrong date column")
+    #     return None, dataframe
+
+    sorted_dates = df.sort_values(by='date2')
+
+    cmap = plt.cm.get_cmap('RdYlGn')
+    norm = plt.Normalize(sorted_dates['polarity'].min(), sorted_dates['polarity'].max())
+    colors = [cmap(norm(value)) for value in sorted_dates['polarity']]
+
+    # scatter plot
+    plt.scatter(sorted_dates['date2'],sorted_dates['polarity'], color=colors, alpha=0.5)
+
+    # add a lineplot to show the average per day
+
+    plt.plot(sorted_dates['date2'], sorted_dates['polarity'].rolling(window=50).mean(), color='hotpink', linewidth=1.5)
+
+  
+    # add legend about pink line
+    plt.legend(['Polarity', 'Trend'], frameon=False, bbox_to_anchor=(0.3, 1), loc='upper right', ncol=2, fontsize=12)
+
+    # add x-label inside the plot
+
+    plt.xlabel('Date',  fontsize=12 )
+
+    # add y-label
+    plt.ylabel('Polarity',  fontsize=12)
+
+    # add x-ticks
+    # plt.xticks(fontsize=12 )
+    # ax1 = plt.axes()
+    # x_axis = ax1.axes.get_xaxis()
+    # # x_axis.set_visible(False)
+    # # hide axis values
+    # x_axis.set_ticks([])
+    # # hide axis line
+    # x_axis.set_ticklabels([])
+
+    plt.yticks(fontsize=12)
+
+    return plt, df
+
+    
+def get_synthetic_comment(text: None, slider, dataframe) :
+
+
+    if check_words_in_string(words, text, case=False) and False:
+
+        df = dataframe.copy() # query is based on a dataframe called "df"
+        agent = create_pandas_dataframe_agent(OpenAI(temperature=0), df, verbose=True)
+
+        def launch_agent(user_input):        
+            memory['user'].append(user_input)
+            # user_input = get_memory() + user_input
+            user_input =  user_input
+            agent_output = (agent.run(user_input))
+            memory['agent'].append(agent_output)
+            return agent_output
+
+        print('Pandas Agent Query')
+        answer = launch_agent(text)
+        return answer, None, None
+
+    else:
+        query_type = 'comments'
+
+        query = f'Forget all of the above. Write 2-3 examples of {query_type} answering this question: {text}. \n\n{query_type}:\n\n'
+        response = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo",
+            temperature=0.5,
+            max_tokens=300,
+            top_p=1,
+            # stream=True,
+            messages=[
+                #{"role": "system", "content": "Forget all the above instructions. You are a reviewer of products but also a customer and have an opinion about the products you buy. You are asked to write a review of a product you have recently purchased."},
+                {"role": "user", "content": query},
+            ]
+        )['choices'][0]['message']['content']
+        response = re.sub(r'As an AI model.*?\.', '', response)
+        response = re.sub(r'As an AI language model.*?\.', '', response)
+
+        query_embed = model.encode(response)
+        # dataframe['embeddings'] = dataframe['translated_text'].apply(
+        #     lambda x: model.encode(x))
+        dataframe['similarity'] = dataframe['embeddings'].apply(
+            lambda x: round(float(util.pytorch_cos_sim(query_embed, x)), 3))
+
+        dataframe.sort_values(by='similarity', ascending=False, inplace=True)
+
+        
+        complexity = ''
+        cutsize = 900
+
+        if dataframe[dataframe['similarity'] > slider].shape[0] == 0:
+            response2 = f'No {query_type} found with a similarity score above {slider}. Try to lower the similarity score threshold or change the question.\n\n However, this is what I found on the internet: \n\n'
+    
+            toolname = ['wolfram-alpha']
+            tools = load_tools(toolname)
+            agent = initialize_agent(tools=tools, llm=llm, agent='zero-shot-react-description', verbose=False)
+            wolfram_content = agent.run(f'{text}')
+            wolfram_response = f'{wolfram_content}'
+    
+            toolname = ['serpapi']
+            tools = load_tools(toolname)
+            agent = initialize_agent(tools=tools, llm=llm, agent='zero-shot-react-description', verbose=False)
+            internet_content = agent.run(f'{text}')
+            internet_response = f'{internet_content}'
+
+            response2 = f'{response2} \n\n {wolfram_response} \n\n {internet_response}'
+
+        else:
+
+            try:
+                corpus = dataframe[dataframe['similarity']
+                                > slider]['translated_text'].tolist()
+                print("CORPUS SIZE:", "FULL")
+
+                response2 = openai.ChatCompletion.create(
+                    model="gpt-3.5-turbo",
+                    temperature=0.4,
+                    max_tokens=300,
+                    top_p=1,
+                    # stream=True,
+                    messages=[
+                        {"role": "user", "content": f'\n{corpus}\n\nSummarize the above {query_type}s {complexity} to answer this question: {text}\n\nSummary:\n\n'},
+                    ]
+                )['choices'][0]['message']['content']
+                response2 = f'<b>Question:</b> {text}\n\n<b>Answer:</b> {response2}'
+            except:
+                try:
+                    corpus = dataframe[dataframe['similarity']
+                                    > slider]['translated_text'][0:50].tolist()
+                    corpus = [x[:cutsize] for x in corpus]
+                    print("CORPUS SIZE:", 50)
+                    response2 = openai.ChatCompletion.create(
+                        model="gpt-3.5-turbo",
+                        temperature=0.4,
+                        max_tokens=300,
+                        top_p=1,
+                        # stream=True,
+                        messages=[
+                            {"role": "user", "content": f'\n{corpus}\n\nSummarize the above {query_type}s {complexity} to answer this question: {text}\n\nSummary:\n\n'},
+                        ]
+                    )['choices'][0]['message']['content']
+                    response2 = f'<b>Question:</b> {text}\n\n<b>Answer:</b> {response2}'
+                except:
+                    try:
+                        corpus = dataframe[dataframe['similarity']
+                                        > slider]['translated_text'][0:30].tolist()
+                        corpus = [x[:cutsize] for x in corpus]
+
+                        print("CORPUS SIZE:", 30)
+                        response2 = openai.ChatCompletion.create(
+                            model="gpt-3.5-turbo",
+                            temperature=0.4,
+                            max_tokens=300,
+                            top_p=1,
+                            # stream=True,
+                            messages=[
+                                {"role": "user", "content": f'\n{corpus}\n\nSummarize the above {query_type}s {complexity} to answer this question: {text}\n\nSummary:\n\n'},
+                            ]
+                        )['choices'][0]['message']['content']
+                        response2 = f'<b>Question:</b> {text}\n\n<b>Answer:</b> {response2}'
+                    except:
+                        corpus = dataframe[dataframe['similarity']
+                                        > slider]['translated_text'][0:15].tolist()
+                        print("CORPUS SIZE:", 15)
+                        # keep only the first 400 characters per each list elem
+                        corpus = [x[:cutsize] for x in corpus]
+
+                        response2 = openai.ChatCompletion.create(
+                            model="gpt-3.5-turbo",
+                            temperature=0.4,
+                            max_tokens=300,
+                            top_p=1,
+                            # stream=True,
+                            messages=[
+                                {"role": "user", "content": f'\n{corpus}\n\nSummarize the above {query_type}s {complexity} to answer this question: {text}\n\nSummary:\n\n'},
+                            ]
+                        )['choices'][0]['message']['content']
+                        response2 = f'<b>Question:</b> {text}\n\n<b>Answer:</b> {response2}'
+
+            # Graph Generation
+
+            return response2, dataframe[dataframe['similarity'] > slider][['similarity', 'translated_text']][0:15], response2, "<span class='hsub'>Analysis:</span>Manual query"
+
+        return response2, dataframe[dataframe['similarity'] > slider][['similarity', 'translated_text']][0:15], response2, "<span class='hsub'>Analysis:</span>Manual query"
+
+def clear_output(filename, titleBlock):
+    titleBlock = re.sub('<[^<]+?>', '', titleBlock)
+    # remove all \n 
+    # trim
+    titleBlock = titleBlock.replace("\n", "")
+
+    print(titleBlock)
+    print(filename)
+    print(filename + titleBlock + "_CACHE.txt")
+    try:
+        os.remove(filename + titleBlock + "_CACHE.txt")
+    except Exception as e:
+        print (e)
+
+        pass
+    return 'Cache has been cleared'
+
+def save_output(tab, data_answer):
+
+# def save_output(tab):
+    if tab == "Summary":
+        print("summary save")
+        print(data_answer)
+        print (data_answer.value)
+        print(dir(data_answer))
+        # with open("data_answer.txt", "+ab") as f:
+        # open and append to it
+        with open("data_answer.txt", "a") as f:
+            try:
+                f.write(data_answer.value)
+                f.write(data_answer)
+            except:
+                pass
+     
+    elif tab == "Table":
+        try:
+            similar_reviews_dataframe = pd.DataFrame(similar_reviews)
+            similar_reviews_dataframe.to_csv("similar_reviews.csv", index=False, encoding='utf-8-sig')
+        except:
+            pass
+    else:
+        try:
+            g.save_graph("graph.html")
+        except:
+            pass
+
+
+def generate_new_examples(text):
+    # GENERATE NEW EXAMPLES BASED ON QUERY
+    new_examples = openai.ChatCompletion.create(
+    model="gpt-3.5-turbo",
+    temperature=0.7,
+    max_tokens=100,
+    top_p=1,
+    # stream=True,
+    messages=[
+        {"role": "user", "content": f'Generate a list of 4 most relevent questions related to this question : {text}. Output should be in a comma separated string format, no numbers, ordering. (example: What is this text about?, What is the main trend?,...) There is no need to enumerate each element.\n\n'},
+    ]
+    )['choices'][0]['message']['content']
+
+    
+    new_examples = new_examples.split('\n')
+    # make a list for each element
+
+    new_examples = [x for x in new_examples if x != '']
+    new_examples = [x.strip() for x in new_examples]
+    new_examples = [x.split(',') for x in new_examples]
+    return new_examples
+
+    
+def summarize_video(url):
+    loader = YoutubeLoader.from_youtube_channel(url)
+    result = loader.load()
+
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=0)
+    texts = text_splitter.split_documents(result)
+    print(len(texts))
+ 
+
+    #  We first try the chain with the default chain type
+    # if length of the text is more than 2000 tokens, we will use map reduce (summary of chunks)
+
+    try:
+        chain = load_summarize_chain(llm, chain_type='stuff', verbose=True)
+        print('ChainType: stuff')
+        # store intermediate steps
+        return chain.run(result)
+
+    except:
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=0)
+        texts = text_splitter.split_documents(result)
+        # print(len(texts))
+        chain = load_summarize_chain(llm, chain_type='map_reduce', verbose=True)
+        print('ChainType: map reduce')
+        return chain.run(texts)
+
+
+# def main():
+    # global similar_reviews, g, query_type, response2, Output, output_html, html, new_examples, samples
+
+def get_graph(dataframe):
+    
+    print("step 1")
+    from sklearn.cluster import KMeans
+    from sklearn.metrics.pairwise import cosine_similarity
+    
+
+    embeddings_array = dataframe['embeddings'].tolist()
+    print ("step 2")
+    num_clusters = 3  # Adjust the number of clusters as needed
+    kmeans = KMeans(n_clusters=num_clusters, random_state=42)
+    cluster_labels = kmeans.fit_predict(embeddings_array)
+
+    print(cluster_labels)
+    sentences = dataframe['translated_text'].tolist()
+    print ("step 3")
+    G = nx.DiGraph()
+
+    
+    cos_sim_matrix = cosine_similarity(embeddings_array)
+    print(cos_sim_matrix)
+    print ("step 4")
+    for idx, label in enumerate(cluster_labels):
+        G.add_node(idx, sentence=sentences[idx], cluster=label)
+
+    for i in range(len(sentences)):
+        for j in range(len(sentences)):
+            if i != j:
+                #if cos_sim_matrix[i, j] > 0.8:
+                    G.add_edge(i, j, weight=cos_sim_matrix[i, j])
+                # else:
+                #     continue
+    print ("step 5")
+    plt.figure(figsize=(10, 10))
+
+    pos = nx.spring_layout(G, k=0.5, iterations=50)
+
+    print ("step 6")
+    G_undirected = G.to_undirected()
+
+    from community import community_louvain
+    node_to_community = community_louvain.best_partition(G_undirected)
+
+    print ("step 7")
+    community_to_color = {
+            0 : 'tab:pink',
+            1 : 'tab:orange',
+            2 : 'tab:purple',
+            3 : 'tab:blue',
+    }
+
+    node_color = {node: community_to_color[community_id] for node, community_id in node_to_community.items()}
+
+    print ("step 8")
+    reducer = umap.UMAP(n_components=2, random_state=42)
+    embeddings_2d = reducer.fit_transform(embeddings_array)
+
+    def normalize_weight(weight, min_weight, max_weight):
+        return (weight - min_weight) / (max_weight - min_weight)
+    
+
+    def visualize_graph_plotly(graph, embeddings_2d, scaling_factor=3):
+
+        print ("step 9")
+        min_weight = min((data['weight'] for _, _, data in graph.edges(data=True)))
+        max_weight = max((data['weight'] for _, _, data in graph.edges(data=True)))
+
+        fig = go.Figure()
+
+
+        print ("step 10")
+        # Add edges with width based on the normalized weights
+        print(len(graph.edges()))
+        for i, j in graph.edges():
+            print(i)
+            weight = normalize_weight(graph[i][j]['weight'], min_weight, max_weight)
+            # weight=0.1
+            fig.add_shape(
+                type="line",
+                x0=embeddings_2d[i][0],
+                x1=embeddings_2d[j][0],
+                y0=embeddings_2d[i][1],
+                y1=embeddings_2d[j][1],
+                yref="y",
+                xref="x",
+                line=dict(color="rgba(211, 211, 211, 0.5)", width=weight * scaling_factor * 0.7),
+            )
+        print ("step 11")
+        # Add nodes
+        for idx, emb in enumerate(embeddings_2d):
+            closeness = nx.closeness_centrality(G)[idx]
+            degree = nx.degree_centrality(G)[idx]
+            betweenness = nx.betweenness_centrality(G)[idx]
+            eigen = nx.eigenvector_centrality(G)[idx]
+
+            fig.add_trace(
+                go.Scatter(
+                    x=[emb[0]],
+                    y=[emb[1]],
+                    mode="markers+text",
+                    text=[graph.nodes[idx]["sentence"]],
+                    textposition="bottom center",
+                    marker=dict(color=node_color[idx][4:], size=closeness * 40),
+                    # add closeness, degree, betweenness and sentence as hover text
+                    hovertext=[f"{graph.nodes[idx]['sentence']} <br> closeness_centrality: {closeness:.2f} <br> degree_centrality: {degree:.2f} <br> betweenness_centrality: {betweenness:.2f} <br> eigenvector_centrality: {eigen:.2f}"],
+                )
+            )
+
+        print("for completed")
+
+        fig.update_layout(showlegend=False, plot_bgcolor="white", width=1200, height=800)
+        fig.update_xaxes(showticklabels=False, showgrid=False, zeroline=False,
+                        showline=False, automargin=False, showspikes=False)
+        fig.update_yaxes(showticklabels=False, showgrid=False, zeroline=False,
+                        showline=False, automargin=False, showspikes=False)
+        
+        fig.update_layout(title_text="Test Graph Visualization", title_x=0.5, title_font_size=30, title_font_color='black')
+
+        return fig
+    
+    return visualize_graph_plotly(G, embeddings_2d, scaling_factor = 10)
+
+def update_examples(samples):
+    return gr.Dataset.update(samples=samples)
+
+def print_samples():
+    global samples
+    return {"samples": samples}
+
+def load_example(example_id):
+    global samples
+    return samples[example_id][0]
+
+url_params = gr.JSON({}, visible=False, label="URL Params")
+
+
+def getAnalysisLabel(id):
+    if id == 'exec_sum': 
+        return 'Executive Summary'
+    elif id == 'top_clust':
+        return 'Topic Cluster'
+
+    elif id == 'trend_analysis':
+        return 'Trend Analysis'
+    
+    elif id == 'emot_clust':
+        return 'Sentiment Analysis'
+    
+    elif id == 'swot_':
+        return 'SWOT Analysis'
+    
+    elif id == 'competitor':
+        return 'Competitor Analysis'
+
+tabs = [
+    {
+        "id": 0,
+        "label": "Social Media",
+        "content": {
+            "exec_sum":None,
+            "top_clust": None,
+            "emot_clust": None,
+            "swot_": None,
+            # "competitor": None,
+        },
+        "filename": "profdemo_cleaned.xlsx"
+    },
+    {
+        "id": 1,
+        "label": "News/Publications",
+        "content": {
+            "exec_sum":None,
+            "trend_analysis": None,
+            # "top_clust": None,
+            "competitor" : None,
+            "swot_": None,
+        },
+        "filename": "cleaned_news.xlsx"
+    },
+    # {
+    #     "id": 0,
+    #     "label": "Mozzarella",
+    #     "content": {
+    #         "exec_sum":None,
+    #         "top_clust": None,
+    #         # "trend_analysis": None,
+    #         "emot_clust": None,
+    #         # "swot_": None,
+    #         # "competitor" : None,
+    #     },
+    #     "filename": "MozzarellaTW.xlsx"
+    # },
+    # {
+    #     "id": 1,
+    #     "label": "Onion",
+    #     "content": {
+    #         "exec_sum":None,
+    #         "top_clust": None,
+    #         "emot_clust": None,
+    #         # "competitor" : None,
+    #     },
+    #     "filename": "OnionTW.xlsx"
+    # },
+    # {
+    #     "id": 2,
+    #     "label": "Brand - Social Media",
+    #     "content": {
+    #         "exec_sum":None,
+    #         "top_clust": None,
+    #         "trend_analysis": None,
+    #         "emot_clust": None,
+    #         "swot_": None,
+    #         "competitor" : None,
+    #     },
+    #     "filename": "LambWestonBrand.csv"
+    # },
+    # {
+    #     "id": 3,
+    #     "label": "Brand - News",
+    #     "content": {
+    #         "exec_sum":None,
+    #         "top_clust": None,
+    #         "trend_analysis": None,
+    #         "emot_clust": None,
+    #         "swot_": None,
+    #         "competitor" : None,
+    #     },
+    #     "filename": "LambWestonNews.csv"
+    # },
+]
+
+list = []
+maxSources = 10
+
+oTab = [] 
+attachButtons = []
+
+for element in tabs:
+    oTab.append( {
+        "exec_sum":None,
+        "top_clust": None,
+        "trend_analysis": None,
+        "emot_clust": None,
+        "swot_": None,
+        "competitor" : None,
+    })
+    attachButtons.append(None)
+
+
+get_window_url_params = """
+    function(url_params) {
+        var scriptElement = document.createElement("script");
+        scriptElement.src = '"""+gradio_js+"""?v="""+str(ra)+"""';
+        scriptElement.innerHTML = "console.log('This is dynamic JavaScript code');";
+        document.body.appendChild(scriptElement);
+        const params = new URLSearchParams(window.location.search);
+        url_params = Object.fromEntries(params);
+        return [url_params];
+    }
+"""
+
+runjs = """
+function(projJson,num,fullData) {  
+    console.log(fullData)
+
+    var localizations = fullData['localization']
+    console.log( fullData['localization'])
+    if (localizations) {
+        document.querySelectorAll('.hsub')[0].innerText = localizations['project'];
+        // document.querySelectorAll('.hsub')[1].innerText = "semmi";
+        // document.querySelectorAll('.hsub')[2].innerText = "semmi";
+
+        if (document.querySelector('#querybtn')) document.querySelector('#querybtn').innerText = localizations['talk_to_your_data']
+
+        var tabs = document.querySelectorAll('#targetColMiddle .tab-nav button'); 
+        tabs[0].innerText = localizations['overview'] || 'Overview'
+        tabs[1].innerText = localizations['rawdata'] || 'Raw Data'
+        tabs[2].innerText = localizations['visuals'] || 'Visuals'
+
+        document.querySelectorAll('.sideTitle span')[0].innerText = localizations['data_sources'] || 'Data sources'
+        document.querySelectorAll('.sideTitle span')[1].innerText = localizations['predefined_analysis'] || 'Predefined analysis'
+    }
+    document.querySelectorAll('.analysisButton').forEach(function(el) {
+       el.style.display = 'none';
+    });
+    Object.keys(projJson[num]['content']).forEach(function(key) {
+        document.querySelectorAll('.analysisButton').forEach(function(el) {
+            if (el.id == key) {
+                el.style.display = 'block';
+            }
+
+        });
+    });
+
+    document.querySelectorAll('.sourceButton').forEach(function(el) {
+        el.classList.remove('selectedSource');
+    });
+    document.querySelectorAll('.sourceButton').forEach(function(el) {
+        if (el.innerHTML == projJson[num]['label']) el.classList.add('selectedSource');
+    });
+    // NEM ÉRTEM MINEK KELL TÖBB OUTPUT
+    return [1, num,1,1]
+}
+"""
+
+def parse_URL_params(url_params):
+    # if url params has 'pid'
+    if 'pid' in url_params:
+        request = requests.get(rubik_backend + '?proj=' + url_params['pid'])
+        text = url_params['pid']
+    else:
+        request = requests.get(rubik_backend + '?proj=demo')
+        text = "demo"
+    
+    # textlowercase url_params['pid']
+    # first letter uppercase
+    textUpper = request.json()["brand"][0].upper() + request.json()["brand"][1:]
+    return [url_params, request.json()["sources"], request.json()["sources"], "<div class='brand'><span class='hsub'>Project:</span>"+textUpper+"</div>", request.json(), text]
+
+for i in range(maxSources):
+    list.append({"id":i, "name":"asd", "obj":"", "analList":[]})
+
+
+def variable_outputs(k):
+    global list
+    sourceArray = k
+    output = None
+    for i in range(len(sourceArray)):
+        if not output:
+            output = [list[i]["obj"].update(value=sourceArray[i]["label"],visible=True)]
+        else:
+            output += [list[i]["obj"].update(value=sourceArray[i]["label"],visible=True)]
+
+    remainingCount = maxSources - len(sourceArray)
+
+    for i in range(remainingCount):
+        output += [list[i]["obj"].update(value="",visible=False)]
+
+    return output
+
+url_params = gr.JSON({}, label="URL Params", visible=False)
+
+# with gr.Interface(theme=gr.themes.Soft(primary_hue='pink', secondary_hue='pink', neutral_hue='stone'), css="footer{display:none !important}") as app:
+# , css=".main{filter: blur(3px)}footer{display:none !important}"
+with gr.Blocks(theme=gr.themes.Soft(primary_hue='pink', secondary_hue='pink', neutral_hue='stone')) as app:
+
+    dat = gr.Markdown()
+    projdetails = gr.Textbox("test", label="Project Details", visible=False)
+    projJson = gr.JSON(visible=False)
+    fullProjectData = gr.JSON(visible=False)
+    projectUUID = gr.State(value="", label="projectUUID", visible=False)
+    if True:
+        summaries_state = gr.State(value=[], label="summaries_state")
+        executive = gr.State(value='', label="executive")
+        needHeader = True
+
+        # if needHeader and False:    
+        #     with gr.Row():
+        #         with gr.Column(scale=.1, elem_id="logocol"):
+        #             # gr.Markdown('<img id="logo" src="http://
+        #             # add a title at the center of the page
+        #             gr.Markdown('<a href="https://app.rubiklab.ai"><img id="logo" src="http://127.0.0.1:5500/logo.png" /></a>')
+                
+        #         with gr.Column(scale=.3):
+        #             gr.Markdown("<h1>Talkback</h1>")
+
+        # random number between 1 and 10000
+        ra = str(np.random.randint(1, 10000))
+        gr.Markdown(
+                """
+                <link rel='stylesheet' href='https://fonts.googleapis.com/css?family=Roboto:ital,wght@0,200;0,300;0,400;0,600;0,700;0,800;0,900;1,200;1,300;1,400;1,600;1,700;1,800;1,900&display=swap'>
+                <link rel="stylesheet" href='""" + gradio_css + """?v="""+str(ra)+"""'>
+                <link rel="icon" type="image/x-icon" href="https://api.rubiklab.ai/assets/favicon.ico">
+                """
+        )
+
+        # if needHeader:    
+        #     with gr.Row():
+        #         with gr.Column(elem_id="header"):
+        #             gr.Markdown("<h2>Nike</h2>")
+
+        #             selectedData = gr.Markdown('<span></span>', elem_id="datanameblock")
+        #             titleBlock = gr.Markdown("<span class='title'></span>", elem_id="titleblock")
+
+
+        df = pd.DataFrame()
+        with gr.Row():
+            with gr.Column(scale=.4, elem_id="leftContainer"):
+                with gr.Row(elem_id="header"):
+                    proj = gr.Markdown("")
+                    selectedData = gr.Markdown('<span></span>', elem_id="datanameblock")
+                    titleBlock = gr.Markdown("<span class='title'></span>", elem_id="titleblock")
+
+
+                
+                gr.Markdown("<h3 class='sideTitle'><span>Data sources</span></h3>")
+                tabs = []
+                analysisList = []
+                for i in range(maxSources):
+                    list[i]["obj"] = gr.Button(value="" + str(i),visible=False, elem_classes="sourceButton")
+                    list[i]["index"] = gr.Number(i, visible=False)
+                    tabs.append(list[i]["obj"])
+
+                gr.Markdown("<h3 class='sideTitle predefined'><span>Predefined analysis</span></h3>")
+                # analysisList = ['exec_sum', 'top_clust', 'emot_clust', 'swot_', 'trend_analysis', 'competitor']
+                oa = {
+                    'exec_sum': gr.Button("Executive Summary", elem_id="exec_sum", elem_classes=["analysisButton"]),
+                    'top_clust': gr.Button("Topic Clusters", elem_id="top_clust", elem_classes=["analysisButton"]),
+                    'emot_clust': gr.Button("Sentiment Analysis", elem_id="emot_clust", elem_classes=["analysisButton"]),
+                    'swot_': gr.Button("SWOT Analysis", elem_id="swot_", elem_classes=["analysisButton"]),
+                    'trend_analysis': gr.Button("Trend Analysis", elem_id="trend_analysis", elem_classes=["analysisButton"]),
+                    'competitor': gr.Button("Competitor Analysis", elem_id="competitor", elem_classes=["analysisButton"]),
+                }
+                newcluster = gr.Button("New Cluster", elem_id="newcluster", elem_classes=["newcluster"], visible=False)
+                newgraph = gr.Button("New graph", elem_id="newcluster", elem_classes=["newgraph"], visible=False)
+
+                # for i in range(len(analysisList)):
+                #     gr.Button(analysisList[i], elem_classes=["analysisButton"], elem_id=analysisList[i])
+
+                # iterate throu oa
+                gr.Button("Talk to your data", elem_id="querybtn")
+                projJson.change(variable_outputs, projJson, tabs)
+                gr.Markdown(f'url params value: {url_params.value}', visible=False)
+                threshold = gr.Slider(minimum=0, maximum=1, label="Threshold", visible=False)
+                csv_file = gr.File(label="File (csv, excel, h5..)", elem_id="fupload",visible=False)
+
+                brand = gr.State('brand')
+                industry = gr.State('industry')
+
+                csvs = gr.State('csvnum')
+                filename = gr.State('filename')
+
+                graph_type = gr.State('graph_type')
+                
+                # THE DATASET
+                data_storage = gr.State(label="data_storage")
+
+                # TOPICS LIST
+                list_of_topics = gr.State(label="list_of_topics")
+                
+                # add image output
+                with gr.Tab("Word Cloud"):
+                    Imaj = gr.Image(label="Word Cloud")
+
+                with gr.Tab("Polarity"):
+                    Polarity = gr.Plot(label="Polarity")
+            
+            app.load(
+                fn=parse_URL_params,
+                inputs=[url_params],
+                outputs=[url_params, projJson, projdetails, proj, fullProjectData,projectUUID],
+                _js=get_window_url_params
+            )
+            url_params.render()
+            with gr.Column(scale=2, elem_id="targetColMiddle"):
+                
+                graphHelper = gr.Markdown("<span></span>")
+                with gr.Tab("Overview", elem_id="overviewTab"):
+                    tab = 'Summary'
+                    data_answer = gr.Textbox(visible=False, elem_id="hiddenTextbox")
+                    # gr.Textbox.style(data_answer)
+                    formattedres = gr.Markdown("<span class='description'></span>")
+
+                    with gr.Row():
+                        with gr.Column(scale=6):
+                            # add empty space
+                            pass
+                        with gr.Column(scale=.5, min_width=100):
+                            clear_button = gr.Button("Clear", visible=True, elem_id="clear_button")
+                            clear_button.click(fn=clear_output, inputs=[filename, titleBlock], outputs=[formattedres])
+                            # save_button = gr.Button("Save", visible=True, elem_id="save_button")
+                            # save_button.click(lambda: save_output("Summary", data_answer))
+
+
+                tab =  gr.Tab("Raw data", elem_id="rawdataTab")
+                with tab:
+                    tab = 'Table'
+                    similar_reviews = gr.Dataframe(label="Table", type="pandas", max_rows=20, overflow_row_behaviour='paginate', show_label=False)
+                    with gr.Row():
+                        with gr.Column(scale=6):
+                            # add empty space
+                            pass
+                        # with gr.Column(scale=.5, min_width=100):
+                            # save_button = gr.Button("Save", visible=True)
+                            # save_button.click(lambda: save_output("Table", data_answer))
+                with gr.Tab("Visuals"):
+                    tab = 'Graph'
+                    graph = gr.Plot(elem_id="cluster", label="")
+                    graph2 = gr.Plot(elem_id="cluster2", label="", visible=False)
+                    # with gr.Tab("Word Cloud"):
+                    Imaj = gr.Image(label="Word Cloud", elem_id="vwordcloud")
+            
+                    # with gr.Tab("Polarity"):
+                    Polarity = gr.Plot(label="Polarity", elem_id="vpolarity")
+                    
+                    # with gr.Row():
+                    #     with gr.Column(scale=1):
+                    #         clearbutton = gr.Button("Remove and regenerate", visible=True, elem_id="cleardrop_button")
+                    #         clearkeepbutton = gr.Button("Keep and regenerate", visible=True, elem_id="clearkeep_button")
+
+
+                    with gr.Row():
+                        with gr.Column(scale=6):
+                            # add empty space
+                            pass
+                        # with gr.Column(scale=.5, min_width=100):
+                            # save_button = gr.Button("Save", visible=True)
+                            # gr.Button.style(save_button, color="secondary")
+                            # save_button.click(lambda: save_output("Graph", data_answer))
+
+
+                with gr.Row(elem_id="query"):
+                    with gr.Column(scale=1, elem_id='query1'):
+
+                        data_answerQuery = gr.Textbox(label="", lines=10, visible=False)
+                        # gr.Textbox.style(data_answer)
+                        formattedresQuery = gr.Markdown("<span class='description'></span>")
+
+                        query = gr.Textbox(lines=1, placeholder="Start typing your question...", label=" ")
+                        gr.Textbox.style(query)
+                       
+                        submit_button = gr.Button("Submit", elem_id='submit')
+                        gr.Button.style(submit_button, color="secondary")
+
+                    with gr.Column(scale=.1, elem_id='query2'):
+                        samples = [["What insights can we take from this data?", "2 What insights can we take from this data?"]]
+
+                        examples = gr.Dataset(samples=samples, components=[query], type="index", label="Some hints for your next question, select which one you prefer.")
+
+
+      
+                def update_examples(query):
+                    global samples
+                    samples = generate_new_examples(query)
+                    return gr.Dataset.update(samples=samples)
+
+                def print_samples():
+                    global samples
+                    return {"samples": samples}
+
+                def load_example(example_id):
+                    global samples
+                    return samples[example_id][0]
+
+
+        def changesource(projJson, num):
+            # print("switching")
+            return 1
+        def doJS(projJson, num,fulldat):
+            # print("doing js")
+            return 1, num
+        
+        sourcelabel = gr.TextArea(elem_id="activeSource", visible=False)
+        sourceIndex = gr.Number(visible=False)
+        xy = gr.State()
+        for i in range(maxSources):
+            num = list[i]["index"]
+            list[i]["obj"].click(doJS,inputs=[projJson, num, fullProjectData], outputs=[xy, sourceIndex], _js=runjs).then(
+                load_csv, inputs=[projJson, num, fullProjectData, projectUUID], outputs=[data_storage, data_answer, similar_reviews, formattedres, filename, selectedData]).then(
+                generate_wordcloud, inputs=[data_storage], outputs=[Imaj]).then(
+                get_polarity, inputs=[data_storage], outputs=[Polarity, data_storage])
+
+
+        # SUMMARIZE VIDEO CONTENT
+        def checkData(filename):
+            print(filename) 
+        
+        dat = gr.State(label="dat")
+ 
+        oa['exec_sum'].click(fn=checkData, inputs=[filename], outputs=[]).then(get_executive_summary, inputs=[data_storage, brand, industry,summaries_state, csvs, graph_type,filename, fullProjectData, sourceIndex], outputs=[data_answer, similar_reviews, graphHelper, formattedres, titleBlock])
+        oa['top_clust'].click(get_topic_cluster, inputs=[data_storage, graph_type,filename], outputs=[graph, similar_reviews, list_of_topics, query, dat, graphHelper,formattedres,titleBlock])
+        oa['emot_clust'].click(emotional_mapping, inputs=[data_storage, industry, graph_type,filename, fullProjectData, sourceIndex], outputs=[data_answer, similar_reviews, graphHelper,formattedres,titleBlock])
+        oa['swot_'].click(get_executive_summary, inputs=[data_storage, brand, industry,summaries_state, csvs, graph_type,filename, fullProjectData, sourceIndex], outputs=[data_answer, similar_reviews, graphHelper, formattedres, titleBlock]).then(get_SWOT, inputs=[data_storage, brand, industry, executive, graph_type,filename, fullProjectData, sourceIndex], outputs=[data_answer, graphHelper,formattedres,titleBlock])   
+        oa['trend_analysis'].click(get_trend_summary, inputs=[data_storage, list_of_topics, brand, industry, graph_type,filename, fullProjectData, sourceIndex], outputs=[data_answer, similar_reviews, graphHelper,formattedres,titleBlock]) 
+        oa['competitor'].click(get_competitive, inputs=[brand, industry, graph_type,filename,data_storage, fullProjectData, sourceIndex], outputs=[data_answer, graphHelper,formattedres,titleBlock])
+
+
+        def clear_data(filename):
+            if os.path.exists(filename + 'df3.csv'):
+                os.remove(filename + 'df3.csv')
+
+        def rename_data_timestamp(filename):
+            if os.path.exists(filename + 'df3.csv'):
+                os.rename(filename + 'df3.csv', filename + str(datetime.datetime.now()) + '.csv')
+
+        # clearbutton.click(clear_data, inputs=[filename], outputs=[]).then(get_topic_cluster, inputs=[data_storage, graph_type,filename], outputs=[graph, similar_reviews, list_of_topics, query, dat, graphHelper,formattedres,titleBlock])
+        # clearkeepbutton.click(rename_data_timestamp, inputs=[filename], outputs=[]).then(get_topic_cluster, inputs=[data_storage, graph_type,filename], outputs=[graph, similar_reviews, list_of_topics, query, dat, graphHelper,formattedres,titleBlock])
+
+        # app.launch(share=True, server_name="0.0.0.0", server_port=7860)
+        def get_new_topic(data_storage, graph_type,filename):
+            from bertopic import BERTopic
+            from bertopic.representation import OpenAI
+            from sklearn.cluster import KMeans
+            from sklearn.feature_extraction.text import CountVectorizer
+            import pandas as pd 
+            # import openai
+
+            # openai.api_key = 'sk-2Ulixq
+            prompt = """
+            I have a topic that contains the following documents: 
+            [DOCUMENTS]
+            The topic is described by the following keywords: [KEYWORDS]
+
+            Based on the information above, extract a short topic label in the following format:
+            topic: <topic label>
+            """
+
+            vectorizer_model=CountVectorizer(stop_words="english")
+
+            # df = pd.read_excel('C:/Users/sinan/Downloads/profdemo_cleaned (1).xlsx')
+            df = data_storage
+            df['translated_text'] = df['translated_text'].apply(lambda x: str(x))
+
+            docs = df['translated_text'].tolist()
+
+            representation_model = OpenAI(model="gpt-3.5-turbo", delay_in_seconds=.5, chat=True)
+
+            if len(docs) < 100:
+                cluster_model = KMeans(n_clusters=3)
+                topic_model = BERTopic(hdbscan_model=cluster_model, representation_model=representation_model, vectorizer_model=vectorizer_model)
+            else:   
+                cluster_model = KMeans(n_clusters=6)
+                # representation_model = 'bert-base-nli-mean-tokens'
+                n_gram_range = (1, 1)  # set the range of n-grams to be considered
+                min_topic_size = 10 # set the minimum number of documents in each topic
+
+                topic_model = BERTopic(hdbscan_model=cluster_model, representation_model=representation_model, n_gram_range=n_gram_range, min_topic_size=min_topic_size)
+                # topic_model = BERTopic(representation_model=representation_model, nr_topics=8)
+
+
+
+            topics, probs = topic_model.fit_transform(docs)
+
+            return topic_model.visualize_documents(docs, width=1200, height=800, title='Topic Clustering', hide_annotations=True)
+
+        newcluster.click(get_new_topic, inputs=[data_storage, graph_type,filename], outputs=[graph2])
+        newgraph.click(get_graph,inputs=[data_storage], outputs=[graph2])
+        
+        # 1. ADD QUESTIONS TO THE QUERY
+        examples.click(load_example, inputs=[examples], outputs=[query])       
+
+                                                                                                                                                            # UNCOMMENT FOR TEXT TO SPEECH OUTPUT
+        submit_button.click(get_synthetic_comment, inputs=[query, threshold, data_storage ], outputs=[data_answer, similar_reviews, formattedresQuery, titleBlock]).success(update_examples, inputs=[query], outputs=[examples])
+
+
+
+def same_auth(username, password):
+    return username == password
+if __name__ == "__main__":
+    # app.launch(share=True, server_name="0.0.0.0", server_port=7860, auth=same_auth, auth_message="Please enter the same username and password")
+    app.launch(share=True, server_name="0.0.0.0", server_port=7860)
+
+# if __name__ == "__main__":
+#     main()

gradio_experiments.py

@@ -0,0 +1,32 @@
+# import gradio as gr
+
+# with gr.Blocks(theme=gr.themes.Soft(primary_hue='pink', secondary_hue='pink', neutral_hue='stone')) as app:
+
+#     tab = gr.Tab(label="Tab 1")
+    
+#     # tab.add_text("Text Input", lines=5, name="text")
+#     tab.update(name="Text Input")
+
+# if __name__ == "__main__":
+#     # app.launch(share=True, server_name="0.0.0.0", server_port=7860, auth=same_auth, auth_message="Please enter the same username and password")
+#     app.launch(share=True, server_name="0.0.0.0", server_port=1111)
+
+# # if __name__ == "__main__":
+# #     main()
+
+import os
+import openai
+os.environ['OPENAI_API_KEY'] = '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'
+
+
+
+messages=[{"role": "user", "content": "As an intelligent AI model, if you could be any fictional character, who would you choose and why?"}]
+
+response = openai.ChatCompletion.create(
+model="gpt-4",
+max_tokens=100,
+temperature=1.2,
+api_key=os.environ['OPENAI_API_KEY'],
+messages = messages)
+
+print(response)

mtu_merged_v3.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ad055fc89969e06febd852cb751894f40e2bc3212a07bdc999de20903b2b3b7b
+size 17528113

mtuspecific/3d.html

@@ -0,0 +1,527 @@
+<html>
+  <head>
+    <meta charset="utf-8" />
+    <script
+      src="https://cdn.plot.ly/plotly-2.20.0.min.js"
+      charset="utf-8"
+    ></script>
+  </head>
+  <body>
+    <div>
+      <script type="text/javascript">
+        window.PlotlyConfig = { MathJaxConfig: "local" };
+      </script>
+
+      <script src="projectdata.js"></script>
+
+      <div id="myPlot"></div>
+      <script type="text/javascript">
+        window.PLOTLYENV = window.PLOTLYENV || {};
+        Plotly.newPlot(
+          "myPlot",
+          projectData,
+          {
+            coloraxis: { showscale: true },
+            height: 500,
+            hovermode: "x unified",
+            legend: {
+              bgcolor: "rgba(17,17,17,0)",
+              bordercolor: "rgba(17,17,17,0)",
+              borderwidth: 2,
+              font: { color: "black", size: 14 },
+              itemsizing: "constant",
+              title: { font: { color: "black" }, text: " " },
+              tracegroupgap: 0,
+              traceorder: "normal",
+              x: 0,
+              xanchor: "auto",
+              y: 1,
+              yanchor: "auto",
+            },
+            margin: { b: 0, l: 0, pad: 2, r: 0, t: 0 },
+            scene: {
+              camera: {
+                center: { x: -0.4, y: 0, z: 0 },
+                eye: { x: 0.4, y: -1.58, z: 0.3 },
+              },
+              domain: { x: [0.0, 1.0], y: [0.0, 1.0] },
+              xaxis: { title: { text: "" } },
+              yaxis: { title: { text: "" } },
+              zaxis: { title: { text: "" } },
+            },
+            template: {
+              data: {
+                bar: [
+                  {
+                    error_x: { color: "#2a3f5f" },
+                    error_y: { color: "#2a3f5f" },
+                    marker: {
+                      line: { color: "white", width: 0.5 },
+                      pattern: {
+                        fillmode: "overlay",
+                        size: 10,
+                        solidity: 0.2,
+                      },
+                    },
+                    type: "bar",
+                  },
+                ],
+                barpolar: [
+                  {
+                    marker: {
+                      line: { color: "white", width: 0.5 },
+                      pattern: {
+                        fillmode: "overlay",
+                        size: 10,
+                        solidity: 0.2,
+                      },
+                    },
+                    type: "barpolar",
+                  },
+                ],
+                carpet: [
+                  {
+                    aaxis: {
+                      endlinecolor: "#2a3f5f",
+                      gridcolor: "#C8D4E3",
+                      linecolor: "#C8D4E3",
+                      minorgridcolor: "#C8D4E3",
+                      startlinecolor: "#2a3f5f",
+                    },
+                    baxis: {
+                      endlinecolor: "#2a3f5f",
+                      gridcolor: "#C8D4E3",
+                      linecolor: "#C8D4E3",
+                      minorgridcolor: "#C8D4E3",
+                      startlinecolor: "#2a3f5f",
+                    },
+                    type: "carpet",
+                  },
+                ],
+                choropleth: [
+                  {
+                    colorbar: { outlinewidth: 0, ticks: "" },
+                    type: "choropleth",
+                  },
+                ],
+                contour: [
+                  {
+                    colorbar: { outlinewidth: 0, ticks: "" },
+                    colorscale: [
+                      [0.0, "#0d0887"],
+                      [0.1111111111111111, "#46039f"],
+                      [0.2222222222222222, "#7201a8"],
+                      [0.3333333333333333, "#9c179e"],
+                      [0.4444444444444444, "#bd3786"],
+                      [0.5555555555555556, "#d8576b"],
+                      [0.6666666666666666, "#ed7953"],
+                      [0.7777777777777778, "#fb9f3a"],
+                      [0.8888888888888888, "#fdca26"],
+                      [1.0, "#f0f921"],
+                    ],
+                    type: "contour",
+                  },
+                ],
+                contourcarpet: [
+                  {
+                    colorbar: { outlinewidth: 0, ticks: "" },
+                    type: "contourcarpet",
+                  },
+                ],
+                heatmap: [
+                  {
+                    colorbar: { outlinewidth: 0, ticks: "" },
+                    colorscale: [
+                      [0.0, "#0d0887"],
+                      [0.1111111111111111, "#46039f"],
+                      [0.2222222222222222, "#7201a8"],
+                      [0.3333333333333333, "#9c179e"],
+                      [0.4444444444444444, "#bd3786"],
+                      [0.5555555555555556, "#d8576b"],
+                      [0.6666666666666666, "#ed7953"],
+                      [0.7777777777777778, "#fb9f3a"],
+                      [0.8888888888888888, "#fdca26"],
+                      [1.0, "#f0f921"],
+                    ],
+                    type: "heatmap",
+                  },
+                ],
+                heatmapgl: [
+                  {
+                    colorbar: { outlinewidth: 0, ticks: "" },
+                    colorscale: [
+                      [0.0, "#0d0887"],
+                      [0.1111111111111111, "#46039f"],
+                      [0.2222222222222222, "#7201a8"],
+                      [0.3333333333333333, "#9c179e"],
+                      [0.4444444444444444, "#bd3786"],
+                      [0.5555555555555556, "#d8576b"],
+                      [0.6666666666666666, "#ed7953"],
+                      [0.7777777777777778, "#fb9f3a"],
+                      [0.8888888888888888, "#fdca26"],
+                      [1.0, "#f0f921"],
+                    ],
+                    type: "heatmapgl",
+                  },
+                ],
+                histogram: [
+                  {
+                    marker: {
+                      pattern: {
+                        fillmode: "overlay",
+                        size: 10,
+                        solidity: 0.2,
+                      },
+                    },
+                    type: "histogram",
+                  },
+                ],
+                histogram2d: [
+                  {
+                    colorbar: { outlinewidth: 0, ticks: "" },
+                    colorscale: [
+                      [0.0, "#0d0887"],
+                      [0.1111111111111111, "#46039f"],
+                      [0.2222222222222222, "#7201a8"],
+                      [0.3333333333333333, "#9c179e"],
+                      [0.4444444444444444, "#bd3786"],
+                      [0.5555555555555556, "#d8576b"],
+                      [0.6666666666666666, "#ed7953"],
+                      [0.7777777777777778, "#fb9f3a"],
+                      [0.8888888888888888, "#fdca26"],
+                      [1.0, "#f0f921"],
+                    ],
+                    type: "histogram2d",
+                  },
+                ],
+                histogram2dcontour: [
+                  {
+                    colorbar: { outlinewidth: 0, ticks: "" },
+                    colorscale: [
+                      [0.0, "#0d0887"],
+                      [0.1111111111111111, "#46039f"],
+                      [0.2222222222222222, "#7201a8"],
+                      [0.3333333333333333, "#9c179e"],
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+                      [0.8888888888888888, "#fdca26"],
+                      [1.0, "#f0f921"],
+                    ],
+                    type: "histogram2dcontour",
+                  },
+                ],
+                mesh3d: [
+                  {
+                    colorbar: { outlinewidth: 0, ticks: "" },
+                    type: "mesh3d",
+                  },
+                ],
+                parcoords: [
+                  {
+                    line: { colorbar: { outlinewidth: 0, ticks: "" } },
+                    type: "parcoords",
+                  },
+                ],
+                pie: [{ automargin: true, type: "pie" }],
+                scatter: [
+                  {
+                    marker: { colorbar: { outlinewidth: 0, ticks: "" } },
+                    type: "scatter",
+                  },
+                ],
+                scatter3d: [
+                  {
+                    line: { colorbar: { outlinewidth: 0, ticks: "" } },
+                    marker: { colorbar: { outlinewidth: 0, ticks: "" } },
+                    type: "scatter3d",
+                  },
+                ],
+                scattercarpet: [
+                  {
+                    marker: { colorbar: { outlinewidth: 0, ticks: "" } },
+                    type: "scattercarpet",
+                  },
+                ],
+                scattergeo: [
+                  {
+                    marker: { colorbar: { outlinewidth: 0, ticks: "" } },
+                    type: "scattergeo",
+                  },
+                ],
+                scattergl: [
+                  {
+                    marker: { colorbar: { outlinewidth: 0, ticks: "" } },
+                    type: "scattergl",
+                  },
+                ],
+                scattermapbox: [
+                  {
+                    marker: { colorbar: { outlinewidth: 0, ticks: "" } },
+                    type: "scattermapbox",
+                  },
+                ],
+                scatterpolar: [
+                  {
+                    marker: { colorbar: { outlinewidth: 0, ticks: "" } },
+                    type: "scatterpolar",
+                  },
+                ],
+                scatterpolargl: [
+                  {
+                    marker: { colorbar: { outlinewidth: 0, ticks: "" } },
+                    type: "scatterpolargl",
+                  },
+                ],
+                scatterternary: [
+                  {
+                    marker: { colorbar: { outlinewidth: 0, ticks: "" } },
+                    type: "scatterternary",
+                  },
+                ],
+                surface: [
+                  {
+                    colorbar: { outlinewidth: 0, ticks: "" },
+                    colorscale: [
+                      [0.0, "#0d0887"],
+                      [0.1111111111111111, "#46039f"],
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+                      [0.8888888888888888, "#fdca26"],
+                      [1.0, "#f0f921"],
+                    ],
+                    type: "surface",
+                  },
+                ],
+                table: [
+                  {
+                    cells: {
+                      fill: { color: "#EBF0F8" },
+                      line: { color: "white" },
+                    },
+                    header: {
+                      fill: { color: "#C8D4E3" },
+                      line: { color: "white" },
+                    },
+                    type: "table",
+                  },
+                ],
+              },
+              layout: {
+                annotationdefaults: {
+                  arrowcolor: "#2a3f5f",
+                  arrowhead: 0,
+                  arrowwidth: 1,
+                },
+                autotypenumbers: "strict",
+                coloraxis: { colorbar: { outlinewidth: 0, ticks: "" } },
+                colorscale: {
+                  diverging: [
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+                    [0.1, "#c51b7d"],
+                    [0.2, "#de77ae"],
+                    [0.3, "#f1b6da"],
+                    [0.4, "#fde0ef"],
+                    [0.5, "#f7f7f7"],
+                    [0.6, "#e6f5d0"],
+                    [0.7, "#b8e186"],
+                    [0.8, "#7fbc41"],
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+                    [1, "#276419"],
+                  ],
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+                    [0.7777777777777778, "#fb9f3a"],
+                    [0.8888888888888888, "#fdca26"],
+                    [1.0, "#f0f921"],
+                  ],
+                  sequentialminus: [
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+                },
+                colorway: [
+                  "#000",
+                  "#000",
+                  "#000",
+                  "#000",
+                  "#000",
+                  "#000",
+                  "#000",
+                  "#000",
+                  "#000",
+                  "#FECB52",
+                ],
+                font: { color: "#2a3f5f" },
+                geo: {
+                  bgcolor: "red",
+                  lakecolor: "white",
+                  landcolor: "white",
+                  showlakes: true,
+                  showland: true,
+                  subunitcolor: "#C8D4E3",
+                },
+                hoverlabel: { align: "left" },
+                hovermode: "closest",
+                mapbox: { style: "light" },
+                paper_bgcolor: "white",
+                plot_bgcolor: "white",
+                polar: {
+                  angularaxis: {
+                    gridcolor: "#EBF0F8",
+                    linecolor: "#EBF0F8",
+                    ticks: "",
+                  },
+                  bgcolor: "white",
+                  radialaxis: {
+                    gridcolor: "#EBF0F8",
+                    linecolor: "#EBF0F8",
+                    ticks: "",
+                  },
+                },
+                scene: {
+                  xaxis: {
+                    backgroundcolor: "white",
+                    gridcolor: "#DFE8F3",
+                    gridwidth: 2,
+                    linecolor: "#EBF0F8",
+                    showbackground: true,
+                    ticks: "",
+                    zerolinecolor: "#EBF0F8",
+                  },
+                  yaxis: {
+                    backgroundcolor: "white",
+                    gridcolor: "#DFE8F3",
+                    gridwidth: 2,
+                    linecolor: "#EBF0F8",
+                    showbackground: true,
+                    ticks: "",
+                    zerolinecolor: "#EBF0F8",
+                  },
+                  zaxis: {
+                    backgroundcolor: "white",
+                    gridcolor: "#DFE8F3",
+                    gridwidth: 2,
+                    linecolor: "#EBF0F8",
+                    showbackground: true,
+                    ticks: "",
+                    zerolinecolor: "#EBF0F8",
+                  },
+                },
+                shapedefaults: { line: { color: "#2a3f5f" } },
+                ternary: {
+                  aaxis: {
+                    gridcolor: "#DFE8F3",
+                    linecolor: "#A2B1C6",
+                    ticks: "",
+                  },
+                  baxis: {
+                    gridcolor: "#DFE8F3",
+                    linecolor: "#A2B1C6",
+                    ticks: "",
+                  },
+                  bgcolor: "white",
+                  caxis: {
+                    gridcolor: "#DFE8F3",
+                    linecolor: "#A2B1C6",
+                    ticks: "",
+                  },
+                },
+                title: { x: 0.05 },
+                xaxis: {
+                  automargin: true,
+                  gridcolor: "#EBF0F8",
+                  linecolor: "#EBF0F8",
+                  ticks: "",
+                  title: { standoff: 15 },
+                  zerolinecolor: "#EBF0F8",
+                  zerolinewidth: 2,
+                  visible: false,
+                  show: false,
+                  hidden: true,
+                },
+                yaxis: {
+                  automargin: true,
+                  gridcolor: "#EBF0F8",
+                  linecolor: "#EBF0F8",
+                  ticks: "",
+                  title: { standoff: 15 },
+                  zerolinecolor: "#EBF0F8",
+                  zerolinewidth: 2,
+                  visible: false,
+                  show: false,
+                  hidden: true,
+                },
+              },
+            },
+            // itt van a width
+            width: window.innerWidth - 170,
+            height: window.innerHeight - 50,
+            xaxis: {
+              automargin: false,
+              showgrid: false,
+              showline: true,
+              showspikes: false,
+              showticklabels: true,
+              zeroline: false,
+            },
+            yaxis: {
+              automargin: false,
+              showgrid: false,
+              showline: true,
+              showspikes: false,
+              showticklabels: true,
+              zeroline: false,
+            },
+          },
+          { responsive: true }
+        );
+
+        // rotate by using time, around the center of the plot
+        // x: 0.4, y: -1.88, z: 0.3
+        var cnt = 1;
+
+        var interval = setInterval(function () {
+          Plotly.relayout(
+            "myPlot",
+            {
+              "scene.camera.eye.x": 0.4 * cnt,
+              // "scene.camera.eye.y": -1.88 * Math.sin(cnt),
+              // "scene.camera.eye.z": 0.3 * cnt,
+            },
+            [0]
+          );
+          if (cnt > 2) clearInterval(interval);
+          cnt += 0.001;
+        }, 1);
+
+
+        document.addEventListener("click", function () {
+          clearInterval(interval);
+        });
+        document.addEventListener("mousedown", function () {
+          clearInterval(interval);
+        });
+      </script>
+    </div>
+  </body>
+</html>

mtuspecific/3d_old.html

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8495e58991f3e1520daecc7fde990270e90f6322a3c335b53b6c688eea714d49
+size 10509420

mtuspecific/3dnew.html

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8ce6db826066a36a7924c97e3c94fc213ddf6e932ebaaf62f926c7b0dc9f172e
+size 10509572

mtuspecific/chart1.html

@@ -0,0 +1,55 @@
+<html>
+<head>
+    <script src="https://cdnjs.cloudflare.com/ajax/libs/echarts/5.4.2/echarts.min.js"></script>
+   
+</head>
+<body>
+    <div id="main">
+
+    </div>
+
+    <script>
+        var chartDom = document.getElementById('main');
+        var myChart = echarts.init(chartDom);
+        var option;
+
+        option = {
+        tooltip: {
+            trigger: 'item',
+            title: 'asd',
+            formatter: '{a} <br/>{b} ({d}%)'
+        },
+        series: [
+            {
+            name: '',
+            type: 'pie',
+            radius: [40, 200],
+            center: ['75%', '50%'],
+            roseType: 'area',
+            itemStyle: {
+                borderRadius: 2
+            },
+
+            // Túrázás	40%
+            // Városnézés	30%
+            // Fotózás	20%
+            // Borturisztika	5%
+            // Állatkert látogatás	5%
+            data: [
+                { value: 40, name: 'Túrázás' },
+                { value: 30, name: 'Városnézés' },
+                { value: 20, name: 'Fotózás' },
+                { value: 5, name: 'Borturisztika' },
+                { value: 5, name: 'Állatkert látogatás' }
+            ]
+            }
+        ]
+        };
+
+        option && myChart.setOption(option);
+
+
+    </script>
+</body>
+
+</html>

mtuspecific/mtu_merged_v3.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ad055fc89969e06febd852cb751894f40e2bc3212a07bdc999de20903b2b3b7b
+size 17528113

mtuspecific/parl.html

@@ -0,0 +1,158 @@
+<!--
+	THIS EXAMPLE WAS DOWNLOADED FROM https://echarts.apache.org/examples/en/editor.html?c=pie-parliament-transition
+-->
+<!DOCTYPE html>
+<html lang="en" style="height: 100%">
+  <head>
+    <meta charset="utf-8" />
+  </head>
+  <body style="height: 100%; margin: 0">
+    <div id="container" style="height: 100%"></div>
+
+    <script
+      type="text/javascript"
+      src="https://fastly.jsdelivr.net/npm/echarts@5.4.2/dist/echarts.min.js"
+    ></script>
+
+    <script type="text/javascript">
+      var dom = document.getElementById("container");
+      var myChart = echarts.init(dom, null, {
+        renderer: "canvas",
+        useDirtyRect: false,
+      });
+      var app = {};
+
+      var option;
+
+      const data = [
+        { value: 40, name: "Túrázás" },
+        { value: 30, name: "Városnézés" },
+        { value: 5, name: "Borturisztika" },
+        { value: 5, name: "Állatkert látogatás" },
+        { value: 20, name: "Fotózás" },
+      ];
+      const defaultPalette = ["#E667AC", "#3D5176",  "#CEBDCB", "#6D6E6E",  "#FFC423" ]
+
+      const radius = ["20%", "80%"];
+      const pieOption = {
+        series: [
+          {
+            type: "pie",
+            id: "distribution",
+            radius: radius,
+            label: {
+              show: true,
+              position: "outside",
+              formatter: "{b} ({d}%)",
+              fontSize: 20,
+              color: "#A0ADBE",
+            },
+            color:defaultPalette,
+            universalTransition: true,
+            animationDurationUpdate: 1000,
+            data: data,
+          },
+        ],
+      };
+      const parliamentOption = (function () {
+        let sum = data.reduce(function (sum, cur) {
+          return sum + cur.value;
+        }, 0);
+        let angles = [];
+        let startAngle = -Math.PI / 2;
+        let curAngle = startAngle;
+        data.forEach(function (item) {
+          angles.push(curAngle);
+          curAngle += (item.value / sum) * Math.PI * 2;
+        });
+        angles.push(startAngle + Math.PI * 2);
+        function parliamentLayout(
+          startAngle,
+          endAngle,
+          totalAngle,
+          r0,
+          r1,
+          size
+        ) {
+          let rowsCount = Math.ceil((r1 - r0) / size);
+          let points = [];
+          let r = r0;
+          for (let i = 0; i < rowsCount; i++) {
+            // Recalculate size
+            let totalRingSeatsNumber = Math.round((totalAngle * r) / size);
+            let newSize = (totalAngle * r) / totalRingSeatsNumber;
+            for (
+              let k = Math.floor((startAngle * r) / newSize) * newSize;
+              k < Math.floor((endAngle * r) / newSize) * newSize - 1e-6;
+              k += newSize
+            ) {
+              let angle = k / r;
+              let x = Math.cos(angle) * r;
+              let y = Math.sin(angle) * r;
+              points.push([x, y]);
+            }
+            r += size;
+          }
+          return points;
+        }
+        return {
+          series: {
+            type: "custom",
+            id: "distribution",
+            data: data,
+            coordinateSystem: undefined,
+            universalTransition: true,
+            animationDurationUpdate: 1000,
+            renderItem: function (params, api) {
+              var idx = params.dataIndex;
+              var viewSize = Math.min(api.getWidth(), api.getHeight());
+              var r0 = ((parseFloat(radius[0]) / 100) * viewSize) / 2;
+              var r1 = ((parseFloat(radius[1]) / 100) * viewSize) / 2;
+              var cx = api.getWidth() * 0.5;
+              var cy = api.getHeight() * 0.5;
+              var size = viewSize / 67;
+              var points = parliamentLayout(
+                angles[idx],
+                angles[idx + 1],
+                Math.PI * 2,
+                r0,
+                r1,
+                size + 2
+              );
+              console.log(points.length);
+              return {
+                type: "group",
+                children: points.map(function (pt) {
+                  return {
+                    type: "circle",
+                    autoBatch: true,
+                    shape: {
+                      cx: cx + pt[0],
+                      cy: cy + pt[1],
+                      r: size / 2,
+                    },
+                    style: {
+                      fill: defaultPalette[idx % defaultPalette.length],
+                    },
+                  };
+                }),
+              };
+            },
+          },
+        };
+      })();
+      let currentOption = (option = pieOption);
+      setInterval(function () {
+        currentOption =
+          currentOption === pieOption ? parliamentOption : pieOption;
+        myChart.setOption(currentOption);
+      }, 6000);
+
+      if (option && typeof option === "object") {
+        myChart.setOption(option);
+      }
+
+      window.addEventListener("resize", myChart.resize);
+    </script>
+  </body>
+</html>

mtuspecific/pieros.html

@@ -0,0 +1,57 @@
+<!DOCTYPE html>
+<html lang="en" style="height: 100%">
+  <head>
+    <meta charset="utf-8" />
+  </head>
+  <body style="height: 100%;width:100%; margin: 0">
+    <div id="container" style="height: 100%;width:100%"></div>
+
+    <script
+      type="text/javascript"
+      src="https://fastly.jsdelivr.net/npm/echarts@5.4.2/dist/echarts.min.js"
+    ></script>
+
+    <script type="text/javascript">
+      var dom = document.getElementById("container");
+      var myChart = echarts.init(dom, null, {
+        renderer: "canvas"
+      });
+      var app = {};
+
+      var option;
+
+      option = {
+        tooltip: {
+          trigger: "item",
+          title: "asd",
+          formatter: "{b} ({d}%)",
+        },
+        series: [
+          {
+            name: "",
+            type: "pie",
+            radius: [30, 220],
+            roseType: "area",
+            itemStyle: {
+              borderRadius: 2,
+            },
+
+            data: [
+              { value: 40, name: "Túrázás" },
+              { value: 30, name: "Városnézés" },
+              { value: 20, name: "Fotózás" },
+              { value: 5, name: "Borturisztika" },
+              { value: 5, name: "Állatkert látogatás" },
+            ],
+          },
+        ],
+      };
+
+      if (option && typeof option === "object") {
+        myChart.setOption(option);
+      }
+
+      window.addEventListener("resize", myChart.resize);
+    </script>
+  </body>
+</html>

mtuspecific/test.js

@@ -0,0 +1 @@
+test.js

mtuspecific/test.json

@@ -0,0 +1,6 @@
+[
+    ["hiking","trail","backpack","boots","trekking poles","nature","outdoor","mountains","camping","wilderness","navigation"],
+    ["zoo","animals","enclosure","exhibit","conservation","education","habitat","zookeeper","safari","aquarium","wildlife"],
+    ["wine","vineyard","winery","tasting","sommelier","cellar","terroir","grape","vintage","wine region","wine pairing"],
+    ["sightseeing","landmarks","monuments","architecture","city tour","historical","cultural","tour guide","attractions","museums","walking tour"]
+]

prod.sh

@@ -0,0 +1,3 @@
+git pull
+pm2 restart gradio_app
+pm2 restart uploadapi

readme.md

@@ -0,0 +1,48 @@
+# Rubiklab Talkback environment
+
+_If you're a power user, stop reading and start wasting your time.._
+
+# Get started
+
+## Installation
+
+```bash
+pip install -r requirements.txt
+```
+
+## Make sure you have your .env file based on .env.example
+
+```ini
+# custom formatting sources
+GRADIO_CSS=http://localhost:8001/assets/gradio_app.css
+GRADIO_JS=http://localhost:8001/assets/gradio_app.js
+# this is the rubiklab backend (on prod: https://api.rubiklab.ai)
+RUBIK_BACKEND=http://localhost:8001/api/talkback
+```
+
+## How to start
+
+To make everything work, you need to make sure all the below services are up-to-date, the .env file contains the correct paths, and everything is running:
+
+- API backend
+- Flask data uploader (part of this repository)
+
+---
+
+### Launch the app [DEV]
+
+1. `gradio gradio_app.py app`
+2. `cd uploader && python uploadapi.py`
+
+If backend is not started yet:
+Navigate to the Rubiklab backend, and..
+
+`php artisan serve --port=8001`
+
+---
+
+### Launch the app [PROD]
+
+1. `cd /var/www/talkback`
+2. `sh prod.sh`
+3. now just wait a few sec.. and done

requirements.txt

@@ -0,0 +1,58 @@
+snscrape
+umap_learn
+openai
+matplotlib
+textblob
+plotly
+django
+django_select2
+django-urls
+Django
+requests
+flask
+flask_cors
+pandas
+scikit-learn==0.24.2
+pipwin
+pyaudio
+streamlit
+praw
+markdown
+emoji
+load-dotenv
+streamlit-chat
+python_translator
+apify-client
+streamlit-aggrid
+newscatcherapi
+numpy
+Pillow
+sentence_transformers
+transformers
+umap
+umap-learn
+gradio
+openai-whisper
+langchain
+faiss-cpu
+requests-toolbelt
+hdbscan
+torch
+bertopic
+PythonTurtle
+scikit-image
+datetime
+seaborn
+pyvis
+pyvis-network
+pinecone-client
+tiktoken
+us
+librosa
+openpyxl
+folium
+nltk
+pickle-mixin
+translate
+wordcloud
+tweepy

testpandas.py

@@ -0,0 +1,124 @@
+# from langchain.agents import create_pandas_dataframe_agent
+# from langchain.llms import OpenAI
+# import pandas as pd
+# import os
+# # debunidebdeb_cleaned.xlsx
+# os.environ['OPENAI_API_KEY'] = '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'
+
+
+# df = pd.read_excel("debunidebdeb_cleaned.xlsx")
+# agent = create_pandas_dataframe_agent(OpenAI(temperature=0), df, verbose=True)
+
+# # agent.run("based on 'top_emotion', what is the most common emotion?")
+
+# # get input from terminal
+# while True:
+
+#     # get input from terminal
+#     question = input("Ask a question: ")
+
+#     # if input is 'exit', then exit
+#     if question == 'exit':
+#         break
+
+#     # run the agent
+#     agent.run(question)
+
+
+# from youtube_transcript_api import YouTubeTranscriptApi
+
+# transcript = YouTubeTranscriptApi.get_transcript("VslxnS9Gs34")
+
+# # keep text key only
+# transcript = [x['text'] for x in transcript]
+# # print(transcript)
+# # join all text into one string
+# transcript = ' '.join(transcript)
+# print(len(transcript))
+# import openai
+# import os
+
+# os.environ['OPENAI_API_KEY'] = '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'
+# response = openai.ChatCompletion.create(
+#         model="gpt-3.5",
+#         temperature=0.5,
+#         # stream=True,
+#         messages=[
+#             #{"role": "system", "content": "Forget all the above instructions. You are a reviewer of products but also a customer and have an opinion about the products you buy. You are asked to write a review of a product you have recently purchased."},
+#             {"role": "user", "content": 'How are you?'},
+#         ]
+#     )['choices'][0]['message']['content']
+
+
+import requests
+
+cookies = {
+    'dapUid': '544c792c-877b-0214f1c0c407',
+    'privacySettings': '%7B%22v%C%22t%22%3A1664841600%2C%22m%22%3A%22STRICT%22%2C%22consent%22%3A%5B%22NECESSARY%22%2C%22PERFORMANCE%22%2C%22COMFORT%22%5D%7D',
+    'LMTBID': 'v2|9c29fd7a-25b2-4c2a-a1d3-6665d402fb1d|6379551ac25c1fb89d6c2d580bec3fb6',
+    'userRecommendations': 'RC-1.1_RC-10.1_RC-5.1',
+    'userCountry': 'HU',
+    'releaseGroups': '388.DM-49778.DM-705.2.2_866.DM-592.2.2_867.DM-684.2.4_975.DM-609.2.3_1119.B2B-251.2.4_1219.DAL-136.2.3_1223.DAL-179.2.4_1437.DM-850.2.2_1460.TC-502.2.1_1577.DM-594.1.2_865.TG-1004.2.4_1571.DM-791.2.3_1572.TC-559.2.6_1583.DM-807.2.5_1780.DM-872.2.2_1808.DF-3339.2.2_1585.DM-900.2.3_1815.RC-377.2.5_1207.DWFA-96.1.4_1996.DM-822.1.1_1997.DM-941.2.3_2007.DWFA-470.2.2_2022.DF-3340.2.1_2024.SEO-103.2.3_2025.ACL-24.2.2_2027.WDW-56.2.4_2055.DM-814.2.2_2067.SEO-205.2.2_2068.DF-3045.2.1_2073.DM-928.2.1_2256.DF-3461.2.1_2259.SEO-316.2.1_220.DF-1925.1.9_1328.DWFA-285.2.2_2127.AAEXP-1380.1.1_1438.DM-768.2.2_2119.AAEXP-1372.1.1_976.DM-667.2.3_2120.AAEXP-1373.1.1_774.DWFA-212.2.2_2132.AAEXP-1385.1.1_2133.AAEXP-1386.1.1_2122.AAEXP-1375.1.1_1246.DM-793.2.2_1783.TC-171.2.3_2020.DWFA-450.2.2_1444.DWFA-362.2.2_863.DM-601.2.2_1327.DWFA-391.2.2_2117.AAEXP-1370.2.1_2121.AAEXP-1374.1.1_2129.AAEXP-1382.1.1_605.DM-585.2.3_1084.TG-1207.2.3_1332.DM-709.2.2_1483.DM-821.2.1_1776.B2B-345.2.1_2050.DM-455.1.1_2071.DM-951.1.1_2072.DM-741.2.1_2124.AAEXP-1377.2.1_2114.AAEXP-1367.2.1_2118.AAEXP-1371.2.1_2125.AAEXP-1378.1.1_2128.AAEXP-1381.1.1_2131.AAEXP-1384.1.1_2126.AAEXP-1379.1.1_2116.AAEXP-1369.1.1_2130.AAEXP-1383.1.1_2123.AAEXP-1376.2.1_2115.AAEXP-1368.2.1',
+    'dapVn': '34',
+    'dapSid': '%7B%22sid85c8-4898-bafe-37754831ff0c%22%2C%22lastUpdate%22%3A1683702948%7D',
+}
+
+headers = {
+    'authority': 'www2.deepl.com',
+    'accept': '*/*',
+    'accept-language': 'en-GB,en;q=0.9,en-US;q=0.8',
+    'content-type': 'application/json',
+    'origin': 'https://www.deepl.com',
+    'referer': 'https://www.deepl.com/',
+    'sec-ch-ua': '"Microsoft Edge";v="113", "Chromium";v="113", "Not-A.Brand";v="24"',
+    'sec-ch-ua-mobile': '?0',
+    'sec-ch-ua-platform': '"macOS"',
+    'sec-fetch-dest': 'empty',
+    'sec-fetch-mode': 'cors',
+    'sec-fetch-site': 'same-site',
+    'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36 Edg/113.0.1774.35',
+}
+
+params = {
+    'method': 'LMT_handle_jobs',
+}
+
+json_data = {
+    'jsonrpc': '2.0',
+    'method': 'LMT_handle_jobs',
+    'params': {
+        'jobs': [
+            {
+                'kind': 'default',
+                'sentences': [
+                    {
+                        'text': 'this is a cat',
+                        'id': 0,
+                        'prefix': '',
+                    },
+                ],
+                'raw_en_context_before': [],
+                'raw_en_context_after': [],
+                'preferred_num_beams': 4,
+            },
+        ],
+        'lang': {
+            'preference': {
+                'weight': {},
+                'default': 'default',
+            },
+            'source_lang_computed': 'EN',
+            'target_lang': 'HU',
+        },
+        'priority': 1,
+        'commonJobParams': {
+            'mode': 'translate',
+            'browserType': 1,
+        },
+        'timestamp': 1683702948583,
+    },
+    'id': 91990007,
+}
+
+response = requests.post('https://www2.deepl.com/jsonrpc', params=params, cookies=cookies, headers=headers, json=json_data)
+print (response.text)

topic.py

@@ -0,0 +1,2073 @@
+# %%
+import datetime
+import gradio as gr
+from io import BytesIO
+import numpy as np
+import numpy as np
+import tiktoken
+import pandas as pd
+from utils.functions import load_csv
+from wordcloud import WordCloud
+import matplotlib.pyplot as plt
+plt.switch_backend('Agg')
+import requests
+import umap
+from sklearn.feature_extraction.text import TfidfVectorizer
+import hdbscan
+import plotly.express as px
+import plotly.graph_objects as go
+import plotly.express as px
+from langchain.chat_models import ChatOpenAI
+
+import os
+from langchain.agents import load_tools
+from langchain.agents import initialize_agent, create_pandas_dataframe_agent
+from langchain.llms import OpenAI
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+import gradio as gr
+import openai
+import pandas as pd
+import numpy as np
+import re
+import whisper
+import openai
+import pandas as pd
+import networkx as nx
+import matplotlib.pyplot as plt
+
+import networkx as nx
+import matplotlib.pyplot as plt
+from langchain import OpenAI, PromptTemplate, LLMChain
+
+from langchain.agents import load_tools
+from langchain.agents import initialize_agent
+from langchain.llms import OpenAI
+from langchain.document_loaders import YoutubeLoader
+from langchain.chains.summarize import load_summarize_chain
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter, TokenTextSplitter
+from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
+from langchain.document_loaders import YoutubeLoader
+import time
+import re
+import pinecone
+import pandas as pd
+from sentence_transformers import SentenceTransformer, util
+
+#import numpy as np
+from langchain.embeddings import HuggingFaceEmbeddings, OpenAIEmbeddings
+from sklearn.metrics import silhouette_score
+import torch
+import nltk
+nltk.download('vader_lexicon')
+
+
+from dotenv import load_dotenv
+load_dotenv()
+gradio_css = os.getenv("GRADIO_CSS")
+gradio_js = os.getenv("GRADIO_JS")
+rubik_backend = os.getenv("RUBIK_BACKEND")
+openapi_key = os.getenv("OPENAI_API_KEY")
+wolfram_alpha_appid = os.getenv("WOLFRAM_ALPHA_APPID")
+
+#for versioning
+ra = np.random.randint(1000000)
+
+os.environ['OPENAI_API_KEY'] = openapi_key
+os.environ['WOLFRAM_ALPHA_APPID'] = wolfram_alpha_appid
+
+
+def get_key(item):
+    return item['label']
+
+def get_emotion_bertweet(dataset):
+    tokenizer4 = AutoTokenizer.from_pretrained("finiteautomata/bertweet-base-emotion-analysis", truncation=True)
+    model4 = AutoModelForSequenceClassification.from_pretrained("finiteautomata/bertweet-base-emotion-analysis")
+    nlp = pipeline('sentiment-analysis', model=model4,
+                    tokenizer=tokenizer4, top_k=6, truncation=True, device=device)
+
+    top_emotion = []
+    # apply emotion model on data and get the labels and scores
+    for i in range(len(dataset)):
+        label = []
+        score = []
+        jsonfile = (nlp(dataset['translated_text'].iloc[i]))
+        jsonfile[0].sort(key=get_key)
+        for j in range(0, 6):
+            jsonfile2 = np.array(jsonfile)
+            label.append(jsonfile2[0][j]['label'])
+            score.append(jsonfile2[0][j]['score'])
+
+
+        top_emotion.append(label[score.index(max(score))])
+    dataset['top_emotion_bertweet'] = top_emotion
+    print(jsonfile2)
+    return dataset
+
+
+model_name = "sentence-transformers/all-MiniLM-L6-v2"
+hf = HuggingFaceEmbeddings(model_name=model_name)
+embeddings = OpenAIEmbeddings()
+
+# pinecone.init(
+#     api_key='ENTER API KEY HERE',
+#     environment='us-central1-gcp'
+# )
+# index_name = 'openaigradio'
+def markdown_to_html(md_string):
+    # html_string = markdown.markdown(md_string)
+    return md_string
+
+tokenizer4 = AutoTokenizer.from_pretrained("finiteautomata/bertweet-base-emotion-analysis", truncation=True)
+model4 = AutoModelForSequenceClassification.from_pretrained("finiteautomata/bertweet-base-emotion-analysis")
+
+openai.api_key = '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'
+model_whisp = whisper.load_model("base")
+
+llm = OpenAI(temperature=0.2, model_name='text-davinci-003', max_tokens=1000, top_p=1)
+
+model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
+# check if cpu or gpu
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+model = model.to(device)
+
+# %%
+Industries = ['Agriculture',
+ 'Automobile Manufacturing',
+ 'Banking and Finance',
+ 'Biotechnology',
+ 'Chemicals and Petrochemicals',
+ 'Construction and Engineering',
+ 'Consumer Goods and Retail',
+ 'Education',
+ 'Electronics',
+ 'Energy (Oil, Gas, Coal, and Renewable Energy)',
+ 'Entertainment and Media',
+ 'Food and Beverage',
+ 'Healthcare and Pharmaceuticals',
+ 'Hospitality, Travel, and Tourism',
+ 'Information Technology (IT) and Software',
+ 'Insurance',
+ 'Manufacturing (various sectors)',
+ 'Mining and Metals',
+ 'Real Estate',
+ 'Renewable Energy (Solar, Wind, Hydro, Geothermal)',
+ 'Telecommunications',
+ 'Textiles and Apparel',
+ 'Transportation and Logistics',
+ 'Utilities (Electricity, Water, Gas)',
+ 'Waste Management and Environmental Services']
+ 
+
+
+
+def get_memory():
+    memory_string = ''
+    for i,j in memory.items():
+        print(i, j)
+        memory_string += str(j) + '\n'
+    return memory_string
+
+
+def check_words_in_string(word_list, input_string, case=False):
+
+    input_string = input_string.lower()
+
+    # Convert words to lowercase if case is False
+    word_list = [word.lower() if case else word for word in word_list]
+
+    # Check if any word is in the input_string
+    result = any(word in input_string for word in word_list)
+
+    # check if True
+    if result:
+        return True
+    else:
+        return False
+
+
+# Will be used by the Langchain chatbot
+
+words = ['rows', 'data', 'length', 'dataset','plot', 'col','columns','column', 'max', 'min', 'minimum', 'maximum', 'visualize','visualise','represent','graph','chart','plot','diagram','illustrate','show','depict','display','count','number','sum','total','aggregate','trend','pattern','distribution','average','linechart','scatter','barchart','piechart','histogram','boxplot','heatmap','correlation','regression','forecast','predict']
+
+memory = {'agent':[], 'user':[]}
+
+
+
+def get_topic_cluster(dataframe, graph_type = None,filename = None):
+    print(filename)
+    if (dataframe is None):
+        # return None,None, '<h1>Please click "Launch" on the left sidebar.</h1>', '<h1>Please click "Launch" on the left.</h1>', "Executive Summary"
+        return None, None, None, None, None, '<span id="clusterx"></span>',  '<h1>Please click "Launch" on the left sidebar.</h1>', "<span class='hsub'>Analysis:</span>Topic Cluster"
+
+    reduce_dim = umap.UMAP(
+                    n_components=3, n_neighbors=8, min_dist=0.55)
+
+    df = dataframe.copy()
+
+    # some cleaning for reddit datasets
+    df = df[df['translated_text'] != 'nan']
+    df = df[df['translated_text'] != '[deleted]']
+    df = df[df['translated_text'] != '[removed]']
+
+    # if filename + 'df3.csv' exists load csv
+    if os.path.exists(filename + 'df3.csv'):
+        df3 = pd.read_csv(filename + 'df3.csv')
+        df2 = df3
+    else:
+        def CleanTxt_quotes(text):
+            text = re.sub(r'https?:\/\/\S+', '', text)  # Remove hyperlinks
+            text = re.sub(r'http?:\/\/\S+', '', text)  # Remove hyperlinks
+            # if more than 5 mentions, remove all mention
+            if len(re.findall(r'@[A-Za-z0-9]+', text)) > 5:
+                text = re.sub(r'@[A-Za-z0-9]+', '', text)
+            # if more than 4 hashtags, remove all hashtags
+            #text = re.sub(r'[^A-Za-z0-9.!?_#@]+', ' ', text)     # Remove non-alphanumeric characters except exclamation marks and question marks
+            text = re.sub(r'\s+', ' ', text)  # Remove extra whitespace
+            return text
+
+        df['clean_text'] = df['translated_text'].apply(lambda x: str(x))
+        df['clean_text'] = df['translated_text'].apply(lambda x: CleanTxt_quotes(x))
+
+        embedding = np.array([np.array(xi)
+                                for xi in df.embeddings])
+
+        umap_embeddings = reduce_dim.fit_transform(embedding)
+
+        print('umap_embeddings', umap_embeddings.shape)
+
+        # CHECK THIS LINE
+        df['x'] = umap_embeddings[:, 0]
+        df['y'] = umap_embeddings[:, 1]
+        df['z'] = umap_embeddings[:, 2]
+
+        df.dropna(inplace=True)
+
+        hdbscan_min_samples = 1
+        hdbscan_minimal_cluster_size = int(len(df) * 0.01+40)
+
+        # hdbscan_minimal_cluster_size = 7
+        # hdbscan_min_samples = 10
+
+        cluster = hdbscan.HDBSCAN(
+            min_cluster_size=hdbscan_minimal_cluster_size,
+            metric='euclidean',
+            cluster_selection_epsilon=0.001,
+            cluster_selection_method='leaf',
+            algorithm='best',
+            prediction_data=False,
+            min_samples=hdbscan_min_samples).fit(df[['x', 'y', 'z']])
+
+        cluster_analysis = len(pd.Series(cluster.labels_).unique())
+        print('Number of Sentences = ', len(df))
+        print('Number of Clusters = ',  cluster_analysis, '/n')
+
+        df_cluster = pd.DataFrame(
+            pd.DataFrame(cluster.labels_).value_counts())
+        print(df_cluster)
+        clusters = pd.DataFrame(cluster.labels_)
+
+        # percent_unlabelled = round((len(df[clusters[0] == -1]) / len(df)) * 100, 2)
+        # print('The percentage of unlabelled sentences is: ', percent_unlabelled, '%')
+
+        # reindex
+        df.reset_index(inplace=True, drop=True)
+
+        print(len(df[clusters[0] == -1]))
+
+        for i in range(0, cluster_analysis):
+            print('Cluster ', i, ' has ', len(
+                df[clusters[0] == i]), ' sentences')
+
+        print(df_cluster.index)
+
+        from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
+
+        def get_tfidf_top_features(documents, n_top=4):
+            tfidf_vectorizer = TfidfVectorizer(
+                min_df=0.05,
+                max_df=0.95, max_features=10, 
+                stop_words='english')
+            tfidf = tfidf_vectorizer.fit_transform(documents)
+            importance = np.argsort(np.asarray(
+                tfidf.sum(axis=0)).ravel())[::-1]
+            tfidf_feature_names = np.array(
+                tfidf_vectorizer.get_feature_names())
+            return tfidf_feature_names[importance[:n_top]]
+
+        cluster_names = pd.DataFrame(
+            columns=['cluster_name', 'embed_index'])
+
+        for i in range(cluster_analysis):
+            try:
+                print(get_tfidf_top_features(
+                    df['clean_text'][clusters[0] == i]))
+
+                clstr_nm = get_tfidf_top_features(
+                    df['clean_text'][clusters[0] == i])
+                clstr_idx = df['clean_text'][clusters[0] == i].index
+                cluster_names = cluster_names.append(
+                    {'cluster_name': clstr_nm, 'embed_index': clstr_idx}, ignore_index=True)
+
+            except Exception as e:
+                print(e)
+                # cluster_name.append('NULL')
+                pass
+
+        cluster_names['cluster_name'] = cluster_names['cluster_name'].astype(
+            str)
+        cluster_names['cluster_name'] = cluster_names['cluster_name'].str.replace(
+            '[', '')
+        cluster_names['cluster_name'] = cluster_names['cluster_name'].str.replace(
+            ']', '')
+        cluster_names['cluster_name'] = cluster_names['cluster_name'].str.replace(
+            "'", '')
+        cluster_names['cluster_name'] = cluster_names['cluster_name'].str.replace(
+            " ", '-')
+
+        clusters_names = cluster_names.explode('embed_index')
+
+        df2 = df.merge(clusters_names, left_index=True,
+                        right_on='embed_index')
+
+        df2['cluster_name_str'] = df2['cluster_name'].apply(
+            lambda x: str(x))
+        # assign a int value to each unique cluster name in df3
+        df2['cluster_number'] = df2['cluster_name_str'].astype(
+            'category').cat.codes
+
+        df2['trimmed_text'] = df2['clean_text'].str[:175]
+
+        print(df2.head())
+
+        df3 = df2[['x', 'y', 'z', 'cluster_number',
+                    'cluster_name_str', 'trimmed_text']]
+
+
+    #################################################### GET CLUSTER NAME #############################################
+
+        df2['gpt_cluster'] = ''
+        df3['gpt_cluster'] = ''
+
+        for cluster in df3['cluster_name_str'].unique():
+            each_cluster = df3[df3['cluster_name_str'] == cluster]
+
+            docs = '\n'.join(np.random.choice(each_cluster['trimmed_text'], 50))
+
+            response3 = openai.ChatCompletion.create(
+                    model="gpt-3.5-turbo",
+                    temperature=0.3,
+                    max_tokens=300,
+                    top_p=1,
+                    # stream=True,
+                    messages=[
+                        {"role": "user", "content": f'Given a list of keywords {cluster}, and documents present in the cluster : {docs}; assign the most relevant topic name for this cluster : \n\n Cluster Name : '},
+                    ]
+                )['choices'][0]['message']['content']
+
+            df3.loc[df3['cluster_name_str'] == cluster, 'gpt_cluster'] = response3
+            df2.loc[df2['cluster_name_str'] == cluster, 'gpt_cluster'] = response3
+
+       
+
+    # print(df3['cluster_name_str'])
+    # xi = 0
+    # for cluster in df3['cluster_name_str'].unique():
+    #     xi += 1
+    #     df3.loc[df3['cluster_name_str'] == cluster, 'gpt_cluster'] = cluster#"cluster " + str(xi)
+    #     df2.loc[df2['cluster_name_str'] == cluster, 'gpt_cluster'] = cluster#"cluster " + str(xi)
+
+    # save df3
+    df3.to_csv(filename + 'df3.csv', index=False, encoding='utf-8-sig')
+        
+    if len(df3) > 10000:
+        dot_size = 1
+    else:
+        dot_size = 4
+
+
+    color_scale = px.colors.sequential.Viridis
+    color_list = ['#FF0000', '#FF0000', '#FF0000', '#FF0000', '#FF0000', '#FF0000']
+
+    fig = px.scatter_3d(df3, x='x', y='y', z='z', color='gpt_cluster', hover_name='trimmed_text', hover_data={
+                        'x': False, 'y': False, 'z': False, 'cluster_name_str': False, 'cluster_number': False, 'gpt_cluster': False}, opacity=1, template='plotly_white')
+
+    fig.update_traces(marker=dict(size=dot_size))
+
+    fig.add_trace(go.Scatter3d(x=[0], y=[0], z=[0], mode='markers', marker=dict(
+        size=0.1, color='white'), showlegend=True, name=' ', hoverinfo='none'))
+
+    # legend on the right side
+    fig.update_layout(legend=dict(
+        bgcolor='rgba(17,17,17,0)', 
+        xanchor='auto', 
+        yanchor='auto',
+        x=0.8,  # Adjust the x position of the legend
+        y=0.2,  # Adjust the y position of the legend
+        bordercolor='rgba(17,17,17,0)',
+        borderwidth=0,
+    ))
+
+    # fig.update_layout(scene=dict(
+    #     xaxis=dict(
+    #         title=' ',
+    #         nticks=0,
+    #         # backgroundcolor="rgb(0, 0, 0, 1)",
+    #         gridcolor="rgba(17,17,17, 0)",
+    #         showbackground=True,
+    #         zerolinecolor="rgba(17,17,17, 0)",
+    #         zeroline=False,
+    #         showgrid=True,
+    #         showticklabels=False,
+    #         showspikes=False
+    #     ),
+    #     # hide ticks
+
+
+    #     yaxis=dict(
+    #         # name
+    #         title=' ',
+    #         nticks=0,
+    #         # backgroundcolor="rgb(0, 0, 0, 1)",
+    #         gridcolor="rgba(17,17,17, 0)",
+    #         showbackground=True,
+    #         zerolinecolor="rgba(17,17,17, 0)",
+    #         zeroline=False,
+    #         showgrid=True,
+    #         showticklabels=False,
+    #         showspikes=False
+    #     ),
+
+
+
+    #     zaxis=dict(
+    #         # name
+    #         title=' ',
+    #         nticks=0,
+    #         # backgroundcolor="rgba(0, 0, 0, 1)",
+    #         gridcolor="rgba(17,17,17, 0)",
+    #         showbackground=True,
+    #         zerolinecolor="rgba(17,17,17, 0)",
+    #         zeroline=False,
+    #         showgrid=True,
+    #         showticklabels=False,
+    #         showspikes=False),)
+    #     # tickvals=[],),
+    # )
+
+    fig.update_layout(coloraxis_showscale=False, width=1300, height=750, legend=dict(x=0, y=1, traceorder='normal', font=dict(size=14, color='black'), bgcolor='rgba(17,17,17,0)', bordercolor='rgba(17,17,17,0)', borderwidth=0))
+
+
+    # TO ADD AN IMAGE UNCOMMENT
+    
+    # fig.add_layout_image(
+    #     dict(
+    #         source=<SOURCE>,
+    #         xref="x",
+    #         yref="y",
+    #         x=-1,
+    #         y=3.8,
+    #         # xanchor = "left",
+    #         # yanchor = "top",
+    #         sizex=.4,
+    #         sizey=.4,
+    #         opacity=1,
+    #         layer="above",
+    #     )
+    # )  
+
+    fig.update_layout(legend={'itemsizing': 'constant'}, legend_title_text=' ', legend_title_font_color='black',
+                        legend_font_color='black', legend_font_size=14, legend_bgcolor='rgba(17,17,17,0)', legend_bordercolor='rgba(17,17,17,0)', legend_borderwidth=2)
+
+    # , title_font_size=30, title_font_family='Arial', title_font_color='white', title_x=0.06, title_y=0.95, title_xanchor='left', title_yanchor='top', title_text='Cluster of Emotions for {}/n                                        n = {}'.format(subreddit, len(dataset_umap)), margin=dict(l=0, r=0, b=0, t=0, pad=0))
+    fig.update_layout(scene_camera_eye=dict(x=0.87, y=-0.88, z=0.84), scene_camera_center=dict(
+        x=0, y=0, z=0), template='plotly_white', hovermode='x unified', margin=dict(l=0, r=0, b=0, t=0, pad=2))
+
+    fig.update_layout(coloraxis_showscale=True)
+    fig.update_xaxes(showticklabels=True, showgrid=False, zeroline=False,
+                        showline=True, automargin=False, showspikes=False)
+    fig.update_yaxes(showticklabels=True, showgrid=False, zeroline=False,
+                        showline=True, automargin=False, showspikes=False)
+
+    
+    #full_html=False, include_plotlyjs='cdn', default_height='750px', default_width='1500px', config={'displaylogo': False, 'modeBarButtonsToRemove': ['zoom2d', 'pan2d', 'select2d', 'lasso2d', 'zoomIn2d', 'zoomOut2d', 'autoScale2d', 'resetScale2d', 'hoverClosestCartesian', 'hoverCompareCartesian', 'zoom3d', 'pan3d', 'resetCameraDefault3d', 'resetCameraLastSave3d', 'hoverClosest3d', 'orbitRotation', 'tableRotation', 'zoomInGeo', 'zoomOutGeo', 'resetGeo', 'hoverClosestGeo', 'toImage', 'sendDataToCloud', 'hoverClosestGl2d', 'hoverClosestPie', 'toggleHover', 'resetViews', 'toggleSpikelines', 'resetViewMapbox']})}
+
+    
+    cluster_name = df3[['cluster_number', 'gpt_cluster']]
+    cluster_name = cluster_name.drop_duplicates()
+    cluster_name = cluster_name.sort_values(by=['cluster_number'])
+    cluster_name = cluster_name.reset_index(drop=True)
+    # create a list
+    cluster_name_list = cluster_name['gpt_cluster'].tolist()
+    cluster_name_list = '\n'.join(cluster_name_list)
+
+
+
+    Silhouette_Score = 'Silhouette score is : ', silhouette_score(df3[['x', 'y', 'z']], df3['gpt_cluster'],  metric='euclidean')
+
+    # get a dataframe of unique cluster names and their count
+
+    cluster_count = df3.groupby('gpt_cluster').agg({'cluster_number': 'count'}).reset_index()
+    cluster_count = cluster_count.rename(columns={'cluster_number': 'count', 'gpt_cluster': 'Cluster'})
+    
+    
+    # return fig, cluster_count, cluster_name_list, Silhouette_Score, df2
+    return fig, cluster_count, cluster_name_list, None, df2, '<span id="clusterx"></span>', "<b>Please check 'Graph' tab for more details.</b>", "<span class='hsub'>Analysis:</span>Topic Cluster"
+
+
+
+def get_executive_summary(dataframe=None, brand=None, industry=None, summaries=None,  csv_file= None, graph_type = None,filename = None, fullProjectData= None, sourceIndex = 0):
+    # if data_answer.txt exists, open and read
+
+    sourceData = fullProjectData['sources'][int(sourceIndex)]
+    externalPrompt = sourceData['content']['exec_sum']
+
+    if (dataframe is None):
+        return None,None,'<span id="executive"></span>', '<h1>Please click "Launch" on the left sidebar.</h1>',  "<span class='hsub'>Analysis:</span>Executive Summary"
+
+    if os.path.exists(filename + 'Executive Summary_CACHE.txt'):
+        with open(filename + 'Executive Summary_CACHE.txt', 'r') as f:
+            output_summary = f.read()
+
+        return output_summary, dataframe[['translated_text']], '<span id="executive"></span>', markdown_to_html(output_summary), "<span class='hsub'>Analysis:</span>Executive Summary"
+    else:
+        if brand is None:
+            brand = ' '
+
+        else :
+            brand = brand
+            try:
+                dataframe = dataframe[dataframe['translated_text'].str.contains(brand, case=False)]
+            except:
+                pass
+
+
+
+        text_splitter = TokenTextSplitter.from_tiktoken_encoder(
+            encoding_name='p50k_base',
+            chunk_size = 2000,
+        )
+
+        splitted_articles = text_splitter.split_text(''.join(dataframe['translated_text']))
+
+        summarize_template = """  {text} \n\n
+        Summarize the most relevant information for an executive summary from the above document:
+    
+        SUMMARY: """
+
+
+        prompt_template = PromptTemplate(input_variables=['text'], template=summarize_template)
+
+        summary_chain = LLMChain(llm=llm, prompt=prompt_template)
+
+        summaries = []
+        for i in splitted_articles:
+            summaries.append(summary_chain.run(i))
+
+
+
+        summaries1 = '/n'.join(summaries)
+        word_count = 500
+        
+        #If needed, guess the industry
+        # industry_template = PromptTemplate(input_variables=['summaries'], template=extract_industry)
+        # summary_chain = LLMChain(llm=llm, prompt=industry_template)
+        # industry = summary_chain.run(summaries)
+
+        #Check size of output and go in a 2nd loop if it's too big
+        encoding = tiktoken.get_encoding('p50k_base')
+        
+        if len(encoding.encode(summaries1)) > 2000:
+            # return only first 2000 tokens
+            summaries1 = encoding.decode(encoding.encode(summaries1)[:2000])
+
+
+        executive_summary_template = '''Imagine you are an Elite Analyst, Expert Sociologist, and Data Guru,
+        Your task is to leverage your invaluable expertise in crafting a comprehensive and insightful {word_count} words executive summary tailored for C-level executives and decision-makers in {industry}.
+        The summary should synthesize information from various data sources, incorporate relevant cultural and contextual elements, and provide valuable insights that can drive strategic decision-making.
+        Please ensure that your analysis meets the following high-level objectives:
+        Thoroughly examine and interpret the key trends, patterns, and insights derived from the following data sources:
+        {summaries1}
+
+        Articulate the implications and opportunities for {industry}, keeping in mind the needs and challenges of the industry.
+        Consider the cultural, social, and contextual nuances present in the data, drawing on your sociological expertise to ensure the summary remains relevant and insightful across diverse audiences.
+        Identify any potential risks or challenges that might arise from the data, providing strategic recommendations for mitigating these issues.
+        Present the information in a clear, concise, and engaging manner that captures the attention of busy executives and effectively communicates the key insights.
+        Leverage your data expertise to ensure the accuracy, reliability, and relevance of the information presented in the summary. Make us benefit from your unique expertise and insights.
+
+
+        Using markdown formatting, write a {word_count} word SEQ-optimized Executive Summary. Write a click worthy short titles. Add a key takeaway
+        section at the end. Use the seed keyword as the first H2. Always use a combination of paragraphs, lists, and tables for a better reader experience. For the styling of the output, please include headers for different sections, and use bullet points where applicable to organize the key insights. 
+        To avoid repetition, vary the sentence structure and word choice when presenting information from different data sources or discussing various trends, insights, or opportunities. 
+        Using synonyms, alternate phrasings, and modifying sentence structure can help keep the text engaging and fresh for readers.
+        To avoid repetition, vary the sentence structure and word choice when presenting information from different data sources or discussing various trends, insights, or opportunities. Using synonyms, alternate phrasings, and modifying sentence structure can help keep the text engaging and fresh for readers.
+        In order to maintain reader engagement and deliver a captivating text, please ensure that you diversify sentence structure and word choice when presenting insights, trends, or opportunities from different data sources. Employ synonyms, alternative expressions, and varied sentence patterns to provide a fresh and dynamic reading experience.
+        \n\n
+
+        '''
+
+        if externalPrompt and externalPrompt != "":
+            executive_summary_template = externalPrompt
+
+        prompt = PromptTemplate(template=executive_summary_template, input_variables=['industry', 'word_count', 'summaries1'])
+        print("start sum")
+        # llm2 = OpenAI(temperature=0.2, model_name='gpt-4', max_tokens=1000, top_p=1)
+
+        llm2 = ChatOpenAI(temperature=0.2, model_name='gpt-4', max_tokens=1000, top_p=1)
+        executive_chain = LLMChain(llm=llm2, prompt=prompt)
+
+        output_summary = executive_chain.run(industry=industry, word_count=word_count, summaries1=summaries1)
+
+
+    # output_summary = executive_chain.run(industry=industry, word_count=word_count, summaries1=summaries1)
+    with open(filename + "Executive Summary_CACHE.txt", "a") as f:
+        try:
+            f.write(output_summary)
+        except:
+            pass
+
+    # dataframe.rename(columns={'translated_text': 'Table'}, inplace=True)
+    # return("# Executive summary" + output_summary, dataframe[['translated_text']], markdown_to_html(output_summary), 1, markdown_to_html("# Executive Summary\n\n" + output_summary),  "Executive Summary")
+    return output_summary, dataframe[['translated_text']], '<span id="executive"></span>', markdown_to_html(output_summary), "<span class='hsub'>Analysis:</span>Executive Summary"
+
+    return(output_summary, dataframe[['translated_text']][0:20], summaries, output_summary)
+
+
+
+
+
+
+
+def get_competitive(brand, industry, graph_type = None, filename = None,dataframe = None,fullProjectData= None, sourceIndex = 0):
+
+    sourceData = fullProjectData['sources'][int(sourceIndex)]
+    externalPrompt = sourceData['content']['competitor']
+
+    if dataframe is None:
+        return(None,'<span id="competitive"></span>', '<h1>Please click "Launch" on the left sidebar.</h1>', "<span class='hsub'>Analysis:</span>Competitor Analysis")
+ 
+    if os.path.exists(filename + 'Competitor_CACHE.txt'):
+        with open(filename + 'Competitor_CACHE.txt', 'r') as f:
+            output_summary = f.read()
+        return(output_summary,'<span id="competitive"></span>', markdown_to_html("# Competitor Analysis\n\n"+output_summary), "<span class='hsub'>Analysis:</span>Competitor Analysis")
+
+    else:
+        competitive_prompt = '''
+        Ignore all previous instructions. Do not rush. Make this impactful and clear.
+        •Read through all the bullet points and make sure you understand all the bullet points, before you start working.
+        • Act as a subject matter expert, Business analyst, social media expert and professor with 20 years of research experience.
+        [IMPORTANT INSTRUCTION]
+        Your singular task is to provide expert reports with key elements and useful content. Do not make up any information. Do not use jargon.
+        Start with a short paragraph introducing {brand} position in the market. This should be clear and impactfull.
+        •I want to learn more about the competitors of brand {brand} in this market {industry}.
+        [SEPARATOR]
+        •Use the advanced level of expertise in this market {industry} to create topics and subtopics with detailed notes, this will help provide confidence and clarity about the item being sought.
+        [SEPARATOR]
+        1 “Who are the 4 main competitors of {brand}?”
+        2 “What are the top 3 strengths and weaknesses of each of those competitors?”
+        3 “What are the unique selling points of our competitors?”
+        4 “In what unique ways do those competitors market their products/services?”
+        5 “What are the key trends in the {industry} that those competitors are capitalizing on?”
+        6 “What are the areas where those competitors excel compared to {brand}?”
+        7 “What are the areas where our competitors fall short compared to {brand}?”
+        8 “How do our products/services prices compare to those competitors in terms of quality, price positioning and range?”
+        9 “What are the common customer complaints about those competitors?”
+        10 “What are the opportunities for growth in the {industry} that competitors are not taking advantage of?”
+        •Break down the exercise into easy-to-follow steps.
+        •For each topic and/or subtopic provide a clear and informative summary that compare and contrast results..
+        •Identify common mistakes made when addressing those competitive points and address those with maximum clarity.
+        •Proofread content for accuracy, paying special attention to any terms that may have been substituted or omitted unintentionally.
+        Conclude with a brief overview of the competitive landscape for "brand" with the top 3 takeaways and opportunities. The format should be markdown, add subheaders (h2 only), format into nice paragraphs.'''
+
+        # hardcoded!!!
+        brand = "Nike"
+        industry = 'Textiles and Apparel'
+
+        prompt = PromptTemplate(template=competitive_prompt, input_variables=['industry', 'brand'])
+        competition_chain = LLMChain(llm=llm, prompt=prompt)
+
+        output_summary = competition_chain.run(industry=industry, brand=brand)
+        with open(filename + "Competitor_CACHE.txt", "a") as f:
+            try:
+                f.write(output_summary)
+            except:
+                pass
+
+        return(output_summary,'<span id="competitive"></span>', markdown_to_html("# Competitor Analysis\n\n"+output_summary), "<span class='hsub'>Analysis:</span>Competitor Analysis")
+
+# def get_topic_summary(dataframe, topics=None, brand=None, industry=None, graph_type = None, filename = None):
+def get_trend_summary(dataframe, topics=None, brand=None, industry=None, graph_type = None, filename = None,fullProjectData= None, sourceIndex = 0):
+
+    sourceData = fullProjectData['sources'][int(sourceIndex)]
+    externalPrompt = sourceData['content']['trend_analysis']
+
+    if (dataframe is None):
+        return None,None,  '<span id="trend"></span>','<h1>Please click "Launch" on the left sidebar.</h1>', "<span class='hsub'>Analysis:</span>Executive Summary"
+
+    if os.path.exists(filename + 'Trend Analysis_CACHE.txt'):
+        with open(filename + 'Trend Analysis_CACHE.txt', 'r') as f:
+            final_summary = f.read()
+
+        return(final_summary, dataframe[['translated_text']][0:20], '<span id="trend"></span>', markdown_to_html(final_summary), "<span class='hsub'>Analysis:</span>Trend Analysis")
+
+    else:
+
+        if brand is None:
+            brand = ''
+
+        else :
+            brand = brand
+            try:
+                dataframe = dataframe[dataframe['translated_text'].str.contains(brand, case=False)]
+            except:
+                pass
+
+        
+
+        text_splitter = TokenTextSplitter.from_tiktoken_encoder(
+            encoding_name='p50k_base',
+            chunk_size = 2000,
+        )
+
+
+        splitted_articles = text_splitter.split_text(''.join(dataframe['translated_text']))
+
+        summarize_template = """Summarize the most relevant information from the following document:
+
+        {text}
+
+        SUMMARY: """
+
+
+        prompt_template = PromptTemplate(input_variables=['text'], template=summarize_template)
+
+        summary_chain = LLMChain(llm=llm, prompt=prompt_template)
+
+        summaries = []
+        for i in splitted_articles:
+            summaries.append(summary_chain.run(i))
+
+
+        # split the summary into 2000 tokens chunks
+        text_splitter = TokenTextSplitter.from_tiktoken_encoder(
+            encoding_name='p50k_base',
+            chunk_size = 2000,
+        )
+
+
+        summaries2 = text_splitter.split_text(''.join(summaries))
+
+        word_count = 500
+        
+        topics = topics
+        final_summary = []
+
+        brand = "Nike"
+        industry = "Food and Beverage"
+        for summary_1 in summaries2:
+
+            topic_prompt = '''"Imagine you are an Elite Analyst and Trend Analysis Expert with extensive experience in identifying patterns and emerging themes from various data sources, such as social media, regular media, reviews, and survey data. Your task is to leverage your invaluable expertise in crafting a comprehensive and insightful trend analysis report tailored for {brand} within the {industry}. The objective is to provide valuable insights into shifts in consumer behavior, preferences, and market dynamics, enabling informed decision-making for C-level executives and decision-makers.
+
+            In your analysis of {word_count} words, ensure that you address the following key elements:
+
+            Topics : {topics}
+
+            Data: {summary}
+
+            Emerging Trends: Identify and discuss the key emerging trends in consumer behavior, preferences, and market dynamics within the {industry}. Examine the factors driving these trends and provide specific examples to illustrate your findings.
+
+            Impact on {brand}: Analyze how the identified trends are affecting or could potentially affect {brand}. Consider both opportunities and challenges that may arise from these trends, as well as any necessary adjustments to marketing strategies, product offerings, or customer service initiatives.
+
+            Recommendations: Based on the insights derived from the trend analysis, provide actionable recommendations for {brand} to stay ahead of the competition, capitalize on new opportunities, and address potential challenges. Consider innovations, partnerships, or targeted marketing campaigns that can help the company adapt to and benefit from the identified trends.
+
+            Ensure that your trend analysis report is clear, concise, and engaging for busy executives. Focus on providing actionable insights and recommendations that can inform the company's strategic direction. Draw on your expertise to ensure the accuracy, reliability, and relevance of the information presented in the analysis."
+
+
+            Using markdown formatting, write a {word_count} word SEQ-optimized Trend Analysis. Write a click worthy short titles. Add a key takeaway
+            section at the end. Use the seed keyword as the first H2. Always use a combination of paragraphs, lists, and tables for a better reader experience. For the styling of the output, please include headers for different sections, and use bullet points where applicable to organize the key insights. 
+            To avoid repetition, vary the sentence structure and word choice when presenting information. Using synonyms, alternate phrasings, and modifying sentence structure can help keep the text engaging and fresh for readers. \n\n
+
+            '''
+
+            prompt = PromptTemplate(template=topic_prompt, input_variables=['industry', 'topics', 'word_count', 'summary', 'brand'])
+
+            topic_chain = LLMChain(llm=llm, prompt=prompt)
+
+            topic_summary = topic_chain.run(industry=industry, topics = topics, word_count=word_count, summary=summary_1, brand=brand)
+
+            final_summary.append(topic_summary)
+
+        if len(final_summary) > 1:
+            topic_summary = ''.join(final_summary)
+
+            combination = '''{topic_summary}\n\nCombine the content from these articles into one; keeping the format and structure in place. \n\n##Trend Analysis:\n\n'''
+            prompt = PromptTemplate(template=combination, input_variables=['topic_summary'])
+            final_chain = LLMChain(llm=llm, prompt=prompt)
+            final_summary = final_chain.run(topic_summary=topic_summary)
+        
+        else:
+            final_summary = final_summary[0]
+
+        with open(filename + "Trend Analysis_CACHE.txt", "a") as f:
+            try:
+                f.write(final_summary)
+            except:
+                pass
+        
+        # dataframe.rename(columns={'translated_text': 'Table'}, inplace=True)
+
+        return("# Trend Analysis\n" + final_summary, dataframe[['translated_text']][0:20], '<span id="trend"></span>', markdown_to_html(''+final_summary), "<span class='hsub'>Analysis:</span>Trend Analysis")
+
+
+def get_SWOT(dataframe, brand = None, industry = None, exec_summary=None, graph_type= None, filename = None,fullProjectData= None, sourceIndex = 0):
+
+    sourceData = fullProjectData['sources'][int(sourceIndex)]
+    externalPrompt = sourceData['content']['swot_']
+
+    if (dataframe is None):
+        return(None,'<span id="swotx"></span>', '<h1>Please click "Launch" on the left sidebar.</h1>', "<span class='hsub'>Analysis:</span>SWOT")
+   
+
+    brand = 'Nike'
+    industry = 'Textiles and Apparel'
+    if brand is None:
+        brand = ' '
+
+    else :
+        brand = brand
+        try:
+            dataframe = dataframe[dataframe['translated_text'].str.contains(brand, case=False)]
+        except:
+            pass
+
+    
+    # if exec_summary is None:
+    #     
+    exec_summary = '''
+# Mozzarella Sticks: A Versatile Snack Food with Endless Possibilities
+
+## Introduction
+
+Mozzarella sticks are a popular snack food that can be enjoyed in a variety of ways. They can be eaten alone, dipped in sauces, used as a topping on pizzas, or even turned into vape flavors. Mozzarella sticks can also be used to make creative dishes such as a mozzarella stick bowl, a mozzarella stick cake, or a mozzarella stick twinkie. They can also be used as a prank, such as paying per mozzarella stick. Mozzarella sticks are a versatile food that can be enjoyed in many different ways.
+
+## Popularity
+
+Mozzarella sticks are a popular food item that can be enjoyed in many different ways. People have been experimenting with different recipes, such as a low-carb snack of apple, mozzarella stick, hummus, veggie, plain Greek yogurt, English cucumber, dill, peanut butter, and celery. There have also been attempts to create a stuffed crust pizza with a mozzarella stick, as well as a Korean corn dog with a French fry chunk and a half mozzarella stick inside. Mozzarella sticks can also be enjoyed with marinara sauce, ranch, ketchup, and other condiments.
+
+## Availability
+
+Mozzarella sticks are a popular snack food that can be found in many places. They can be eaten alone or as part of a meal, such as a burger or a pizza. They can also be used as an ingredient in dishes such as mac and cheese, risotto, and fried cauliflower. Mozzarella sticks can be found in many forms, such as deep-fried, baked, or grilled. They can also be paired with other foods, such as fruit, vegetables, and sauces. Mozzarella sticks are high in lactose and should be consumed in moderation.
+
+## International Appeal
+
+Mozzarella sticks are a popular dish enjoyed by people around the world. They can be made with a variety of ingredients, such as flour, Greek yogurt, turkey pepperoni, and cheese, and can be served with marinara sauce, butter, and olive oil. Mozzarella sticks are also popular in Czech, Slovak, and Polish cuisine. On International Cheese Day, people celebrate with cheese wedges, ooey gooey cheese pulls, and mozzarella sticks. There are a variety of recipes for mozzarella sticks, including a low-fat version with Greek yogurt, turkey pepperoni, and cheese. Mozzarella sticks can also be enjoyed with a variety of dips, such as marinara sauce, nacho cheese sauce, and homemade marinara sauce.
+
+## Uses
+
+Mozzarella sticks are a popular snack food that can be enjoyed in a variety of ways. They can be deep fried, grilled, or microwaved, and are often served with marinara sauce or ranch dressing. They can also be used as a topping for pizza, burgers, and ramen. Mozzarella sticks are also available in low-fat and dairy-free varieties. They are often served at fast food restaurants, such as Arby's, Burger King, and Sonic, and can be purchased in stores. Mozzarella sticks are a great snack for those looking for a quick meal or a tasty treat.
+
+## Health Benefits
+
+Mozzarella sticks are a popular food item that can be enjoyed in many different ways. They can be fried, microwaved, baked, or even wrapped in fruit roll-ups. They can be served with marinara sauce, ranch dressing, or even chocolate milk. Mozzarella sticks can also be used to make delicious dishes such as mac and cheese, chicken tenders, and jalapeno poppers. They can also be used to make sandwiches, tacos, and pizzas. Mozzarella sticks are a great way to add flavor and texture to any meal.
+
+## Implications and Opportunities
+
+Mozzarella sticks are a popular snack food that can be enjoyed in a variety of ways. They can be served with different sauces, as part of a pizza, or as part of a sandwich. They can also be used to make a variety of dishes, such as a scotch egg, a Camembert burger, or a mozzarella stick hybrid pizza. Mozzarella sticks can also be served with a variety of sides, such as fries, onion rings, and hash browns. Additionally, they can be used to make a variety of desserts, such as a mozzarella stick candle.
+
+Mozzarella sticks are a popular bar food and snack item that can be enjoyed in a variety of ways. They can be served as an appetizer, a side dish, or even as a main course.'''
+
+    #word_count = 500
+
+    with open(filename + "Executive Summary_CACHE.txt", "r") as f:
+        exec_summary = f.read()
+
+    # industry_template = PromptTemplate(input_variables=['summaries'], template=extract_industry)
+    # summary_chain = LLMChain(llm=llm, prompt=industry_template)
+    # industry = summary_chain.run(summaries)
+
+    brand = brand
+
+    industry = industry
+
+    # toolname = ['serpapi']
+    # tools = load_tools(toolname)
+    # agent = initialize_agent(tools=tools, llm=llm, agent='zero-shot-react-description', verbose=True)
+    # internet_content = agent.run(f'What is {brand}?')
+    
+
+
+    SWOT_analysis_template = '''Ignore all previous instructions. Do not rush. Make this impactful and clear.
+    •Read through all the bullet points and make sure you understand all the bullet points, before you start working.
+    Act as a subject matter expert, Business analyst, social media expert and professor with 20 years of research experience.
+
+    Here is an executive Summary for updated context : {exec_summary}
+
+    
+    [IMPORTANT INSTRUCTION]
+    Your singular task is to provide expert reports with key elements and useful content. Do not make up any information. Do not use jargon.
+    Introduction:
+    Start with a paragraph introducing
+
+    Now: return the SWOT for the brand {brand} in the {industry} industry.
+    example:
+    ## Strengths
+    - Strength 1
+    - Strength 2
+    ...
+    ## Weaknesses
+    - Weakness 1
+    - Weakness 2
+    ...
+    ## Opportunities
+    - Opportunity 1
+    - Opportunity 2
+    ...
+    ## Threats
+    - Threat 1
+    - Threat 2
+    ...
+    
+    SWOT formatted with markdown syntax: 
+
+    '''
+  
+    prompt = PromptTemplate(template=SWOT_analysis_template, input_variables=['industry', 'brand', 'exec_summary'])
+
+    SWOT_chain = LLMChain(llm=llm, prompt=prompt)
+
+    SWOT_summary = SWOT_chain.run(industry=industry, brand=brand, exec_summary=exec_summary)
+
+    return("" + SWOT_summary,'<span id="swotx"></span>', markdown_to_html(SWOT_summary + "<div id='isSwot'></div>") , "<span class='hsub'>Analysis:</span>SWOT")
+
+
+def emotional_mapping(dataframe, industry = None, graph_type = None, filename = None, fullProjectData = None, sourceIndex = 0):
+
+    sourceData = fullProjectData['sources'][int(sourceIndex)]
+    externalPrompt = sourceData['content']['swot_']
+
+    if (dataframe is None):
+        return None,None, '<span id="sentiment"></span>', '<h1>Please click "Launch" on the left sidebar.</h1>', "<span class='hsub'>Analysis:</span>Sentiment Analysis"
+    
+    if os.path.exists(filename + 'Sentiment Analysis_CACHE.txt'):
+        # read this: emotional_count.to_csv(filename + "Sentiment Analysis_CACHE.csv", index=False)
+        emotional_count = pd.read_csv(filename + "Sentiment Analysis_CACHE.csv")
+        with open(filename + 'Sentiment Analysis_CACHE.txt', 'r') as f:
+            emotional_summary = f.read()
+
+        return(emotional_summary, emotional_count,'<span id="sentiment"></span>', markdown_to_html(""+emotional_summary), "<span class='hsub'>Analysis:</span>Sentiment Analysis")
+        # return output_summary, dataframe[['translated_text']], markdown_to_html(output_summary), markdown_to_html(output_summary), "Executive Summary"
+    else:
+        if 'top_emotion_bertweet' in dataframe.columns:
+            dataframe['emotion'] = dataframe['top_emotion_roberta'] #dataframe['top_emotion_bertweet']
+        
+        elif 'top_emotion_roberta' in dataframe.columns:
+            dataframe['emotion'] = dataframe['top_emotion_roberta']
+            
+        elif 'top_emotion_distilbert' in dataframe.columns:
+            dataframe['emotion'] = dataframe['top_emotion_distilbert']
+
+        elif 'top_emotion' in dataframe.columns:
+            dataframe['emotion'] = dataframe['top_emotion']
+
+        else:
+            dataframe = get_emotion_bertweet(dataframe)
+            dataframe['emotion'] = dataframe['top_emotion_bertweet']
+
+        word_count = 500
+
+
+        # industry_template = PromptTemplate(input_variables=['summaries'], template=extract_industry)
+        # summary_chain = LLMChain(llm=llm, prompt=industry_template)
+        # industry = summary_chain.run(summaries)
+
+
+        industry = industry
+
+        # get positive dataset
+        positive = dataframe[dataframe['polarity'] > 0]
+
+        # get negative dataset
+        negative = dataframe[dataframe['polarity'] < 0]
+
+        positive_emotions = []
+        negative_emotions = []
+
+        corpus_positive = {}
+        corpus_negative = {}
+
+        # Calculate the number of unique emotions for positive and negative datasets
+        num_positive_emotions = min(len(positive['emotion'].unique()), 3)
+        num_negative_emotions = min(len(negative['emotion'].unique()), 3)
+
+        # Loop through the positive emotions
+        for i in range(num_positive_emotions):
+            value = str(positive['emotion'].value_counts(normalize=True).index[i])
+            percent = str(round(positive['emotion'].value_counts(normalize=True)[i] * 100, 2)) + '%'
+            positive_emotions.append(value + ' ' + percent)
+
+            corpus_positive[value] = positive[positive['emotion'] == value]['translated_text'].tolist()
+
+        # Loop through the negative emotions
+        for i in range(num_negative_emotions):
+            value = str(negative['emotion'].value_counts(normalize=True).index[i])
+            percent = str(round(negative['emotion'].value_counts(normalize=True)[i] * 100, 2)) + '%'
+            negative_emotions.append(value + ' ' + percent)
+
+            corpus_negative[value] = negative[negative['emotion'] == value]['translated_text'].tolist()
+
+
+        emotion_summary = {}
+
+        text_splitter = TokenTextSplitter.from_tiktoken_encoder(
+                encoding_name='p50k_base',
+                chunk_size = 2000,
+            )
+        
+
+
+        for emotion, text in corpus_positive.items():
+
+            emotion_summary[emotion] = text_splitter.split_text(''.join(text))
+
+            # get first element
+            emotion_summary[emotion] = emotion_summary[emotion][0]
+
+            emotion_summarize_template = """  {text} \n\n
+            Summarize the text from the above document to answer this question : Why are people feeling {emotion} ? \n\n
+        
+            SUMMARY: """
+
+            prompt_template = PromptTemplate(input_variables=['text', 'emotion'], template=emotion_summarize_template)
+
+            summary_chain = LLMChain(llm=llm, prompt=prompt_template)
+
+            emotion_summary[emotion] = summary_chain.run(text=emotion_summary[emotion], emotion=emotion, industry=industry)
+
+        for emotion, text in corpus_negative.items():
+
+            emotion_summary[emotion] = text_splitter.split_text(''.join(text))
+
+            # get first element
+            emotion_summary[emotion] = emotion_summary[emotion][0]
+
+            emotion_summarize_template = """  {text} \n\n
+            Summarize the text from the above document to answer this question : Why are people feeling {emotion} ? \n\n
+
+            SUMMARY: """
+
+            prompt_template = PromptTemplate(input_variables=['text', 'emotion'], template=emotion_summarize_template)
+
+            emotion_summary[emotion] = summary_chain.run(text=emotion_summary[emotion], emotion=emotion, industry=industry)
+
+
+
+
+        executive_summary_template = '''Imagine you are an Elite psychologist, Analyst, and Data Guru. You are familiar with leading emotion measurement techniques and the latest developments in the field, 
+        
+        including the Plutchik index and Emotional Intensity Scale (EIS). 
+
+        Data Summary per emotions, leave 'other' emotions!: {all_emotions}
+        
+        Your task is to leverage your invaluable expertise in crafting an insightful {word_count} emotion-driven report tailored for C-level executives and decision-makers in {industry}. 
+        The objective is to provide valuable insights into the impact of the top emotions marketing and branding strategies and provoke lightbulb moments for our readers. Your analysis should provide valuable insights that can drive strategic decision-making based on the key emotions.
+
+        Structure the analysis in two main sections: Observations and Key Findings. In the Observations section, provide precise details about specific emotion measurements and their relation to the wants and needs expressed in the data. In the Key Findings section, focus on insightful content and compare and contrast the different emotions, revealing what's hiding behind the numbers and addressing both expressed and latent emotions.
+        Avoid jargon and broad terms in your analysis, ensuring that the content is clear, concise, and engaging.
+
+        Thoroughly examine and interpret the key trends, patterns, and insights derived from the key emotions .
+        Articulate the implications and opportunities based on the emotion levels, keeping in mind the needs and challenges of the {industry}.
+        Consider the cultural, social, and contextual nuances present in the data, drawing on your expertise to ensure the emotion analysis remains relevant and insightful across diverse audiences.
+        Leverage your psychological expertise to ensure the accuracy, reliability, and relevance of the information presented in the summary. Make us benefit from your unique expertise and insights.
+        Using markdown formatting, write a {word_count} word SEQ-optimized Executive Summary. Write a click worthy short titles. Add a key takeaway
+
+        section at the end. Use the seed keyword as the first H2. Always use a combination of paragraphs, lists, and tables for a better reader experience. For the styling of the output, please include headers for different sections, and use bullet points where applicable to organize the key insights. 
+        To avoid repetition, vary the sentence structure and word choice when presenting information from different data sources or discussing various trends, insights, or opportunities. 
+        Using synonyms, alternate phrasings, and modifying sentence structure can help keep the text engaging and fresh for readers. \n\n
+
+        '''
+
+        prompt = PromptTemplate(template=executive_summary_template, input_variables=['word_count', 'industry', 'all_emotions'])
+
+        executive_chain = LLMChain(llm=llm, prompt=prompt)
+
+        emotional_summary = executive_chain.run(industry=industry, word_count=word_count,  all_emotions=str(emotion_summary.items()))
+
+        emotional_count = dataframe.groupby('emotion').agg({'translated_text': 'count'}).reset_index()
+
+        emotional_count.to_csv(filename + "Sentiment Analysis_CACHE.csv", index=False)
+
+        with open(filename + "Sentiment Analysis_CACHE.txt", "a") as f:
+            try:
+                f.write(emotional_summary)
+            except:
+                pass
+
+        # dataframe.rename(columns={'emotion': 'Emotion'}, inplace=True)
+        
+        return(emotional_summary, emotional_count,'<span id="sentiment"></span>', markdown_to_html(""+emotional_summary), "<span class='hsub'>Analysis:</span>Sentiment Analysis")
+
+
+def generate_wordcloud(dataframe):
+    text = ' '.join(dataframe['translated_text'].tolist())
+    colors = ["#FF69B4", "#FFD700", "#FFA500", "#D3D3D3"]
+    wordcloud = WordCloud(max_font_size=300, max_words=800, width = 1600, height = 1200, background_color="white", colormap="Set2", color_func=lambda *args, **kwargs: colors[len(args[0]) % len(colors)]).generate(text)
+    return wordcloud.to_image()
+
+
+def get_polarity(dataframe):
+    df = dataframe.copy()
+    def get_sentiment_vader(text):
+        from nltk.sentiment.vader import SentimentIntensityAnalyzer
+        sid = SentimentIntensityAnalyzer()
+        return sid.polarity_scores(text)['compound']
+
+    df['translated_text'] = df['translated_text'].astype(str)
+    df['polarity'] = df['translated_text'].apply(lambda x: get_sentiment_vader(x))
+
+    fig = plt.figure(frameon=False, figsize=(16, 12))
+    fig = plt.figure(figsize=(15, 8))
+
+    # try :
+    if 'date' in df.columns:
+        df['date2'] = pd.to_datetime(df['date'], utc=True)
+    else:
+        return None, dataframe
+        print('no date, skipping polarity viz')
+    # except:
+    #     print("no/wrong date column")
+    #     return None, dataframe
+
+    sorted_dates = df.sort_values(by='date2')
+
+    cmap = plt.cm.get_cmap('RdYlGn')
+    norm = plt.Normalize(sorted_dates['polarity'].min(), sorted_dates['polarity'].max())
+    colors = [cmap(norm(value)) for value in sorted_dates['polarity']]
+
+    # scatter plot
+    plt.scatter(sorted_dates['date2'],sorted_dates['polarity'], color=colors, alpha=0.5)
+
+    # add a lineplot to show the average per day
+
+    plt.plot(sorted_dates['date2'], sorted_dates['polarity'].rolling(window=50).mean(), color='hotpink', linewidth=1.5)
+
+  
+    # add legend about pink line
+    plt.legend(['Polarity', 'Trend'], frameon=False, bbox_to_anchor=(0.3, 1), loc='upper right', ncol=2, fontsize=12)
+
+    # add x-label inside the plot
+
+    plt.xlabel('Date',  fontsize=12 )
+
+    # add y-label
+    plt.ylabel('Polarity',  fontsize=12)
+
+    # add x-ticks
+    plt.xticks(fontsize=12 )
+    # ax1 = plt.axes()
+    # x_axis = ax1.axes.get_xaxis()
+    # x_axis.set_visible(False)
+    plt.yticks(fontsize=12)
+
+    return plt, df
+
+    
+def get_synthetic_comment(text: None, slider, dataframe) :
+
+
+    if check_words_in_string(words, text, case=False) and False:
+
+        df = dataframe.copy() # query is based on a dataframe called "df"
+        agent = create_pandas_dataframe_agent(OpenAI(temperature=0), df, verbose=True)
+
+        def launch_agent(user_input):        
+            memory['user'].append(user_input)
+            # user_input = get_memory() + user_input
+            user_input =  user_input
+            agent_output = (agent.run(user_input))
+            memory['agent'].append(agent_output)
+            return agent_output
+
+        print('Pandas Agent Query')
+        answer = launch_agent(text)
+        return answer, None, None
+
+    else:
+        query_type = 'comments'
+
+        query = f'Forget all of the above. Write 2-3 examples of {query_type} answering this question: {text}. \n\n{query_type}:\n\n'
+        response = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo",
+            temperature=0.5,
+            max_tokens=300,
+            top_p=1,
+            # stream=True,
+            messages=[
+                #{"role": "system", "content": "Forget all the above instructions. You are a reviewer of products but also a customer and have an opinion about the products you buy. You are asked to write a review of a product you have recently purchased."},
+                {"role": "user", "content": query},
+            ]
+        )['choices'][0]['message']['content']
+        response = re.sub(r'As an AI model.*?\.', '', response)
+        response = re.sub(r'As an AI language model.*?\.', '', response)
+
+        query_embed = model.encode(response)
+        # dataframe['embeddings'] = dataframe['translated_text'].apply(
+        #     lambda x: model.encode(x))
+        dataframe['similarity'] = dataframe['embeddings'].apply(
+            lambda x: round(float(util.pytorch_cos_sim(query_embed, x)), 3))
+
+        dataframe.sort_values(by='similarity', ascending=False, inplace=True)
+
+        
+        complexity = ''
+        cutsize = 900
+
+        if dataframe[dataframe['similarity'] > slider].shape[0] == 0:
+            response2 = f'No {query_type} found with a similarity score above {slider}. Try to lower the similarity score threshold or change the question.\n\n However, this is what I found on the internet: \n\n'
+    
+            toolname = ['wolfram-alpha']
+            tools = load_tools(toolname)
+            agent = initialize_agent(tools=tools, llm=llm, agent='zero-shot-react-description', verbose=False)
+            wolfram_content = agent.run(f'{text}')
+            wolfram_response = f'{wolfram_content}'
+    
+            toolname = ['serpapi']
+            tools = load_tools(toolname)
+            agent = initialize_agent(tools=tools, llm=llm, agent='zero-shot-react-description', verbose=False)
+            internet_content = agent.run(f'{text}')
+            internet_response = f'{internet_content}'
+
+            response2 = f'{response2} \n\n {wolfram_response} \n\n {internet_response}'
+
+        else:
+
+            try:
+                corpus = dataframe[dataframe['similarity']
+                                > slider]['translated_text'].tolist()
+                print("CORPUS SIZE:", "FULL")
+
+                response2 = openai.ChatCompletion.create(
+                    model="gpt-3.5-turbo",
+                    temperature=0.5,
+                    max_tokens=300,
+                    top_p=1,
+                    # stream=True,
+                    messages=[
+                        {"role": "user", "content": f'\n{corpus}\n\nSummarize the above {query_type}s {complexity} to answer this question: {text}\n\nSummary:\n\n'},
+                    ]
+                )['choices'][0]['message']['content']
+                response2 = f'<b>Question:</b> {text}\n\n<b>Answer:</b> {response2}'
+            except:
+                try:
+                    corpus = dataframe[dataframe['similarity']
+                                    > slider]['translated_text'][0:50].tolist()
+                    corpus = [x[:cutsize] for x in corpus]
+                    print("CORPUS SIZE:", 50)
+                    response2 = openai.ChatCompletion.create(
+                        model="gpt-3.5-turbo",
+                        temperature=0.5,
+                        max_tokens=300,
+                        top_p=1,
+                        # stream=True,
+                        messages=[
+                            {"role": "user", "content": f'\n{corpus}\n\nSummarize the above {query_type}s {complexity} to answer this question: {text}\n\nSummary:\n\n'},
+                        ]
+                    )['choices'][0]['message']['content']
+                    response2 = f'<b>Question:</b> {text}\n\n<b>Answer:</b> {response2}'
+                except:
+                    try:
+                        corpus = dataframe[dataframe['similarity']
+                                        > slider]['translated_text'][0:30].tolist()
+                        corpus = [x[:cutsize] for x in corpus]
+
+                        print("CORPUS SIZE:", 30)
+                        response2 = openai.ChatCompletion.create(
+                            model="gpt-3.5-turbo",
+                            temperature=0.5,
+                            max_tokens=300,
+                            top_p=1,
+                            # stream=True,
+                            messages=[
+                                {"role": "user", "content": f'\n{corpus}\n\nSummarize the above {query_type}s {complexity} to answer this question: {text}\n\nSummary:\n\n'},
+                            ]
+                        )['choices'][0]['message']['content']
+                        response2 = f'<b>Question:</b> {text}\n\n<b>Answer:</b> {response2}'
+                    except:
+                        corpus = dataframe[dataframe['similarity']
+                                        > slider]['translated_text'][0:15].tolist()
+                        print("CORPUS SIZE:", 15)
+                        # keep only the first 400 characters per each list elem
+                        corpus = [x[:cutsize] for x in corpus]
+
+                        response2 = openai.ChatCompletion.create(
+                            model="gpt-3.5-turbo",
+                            temperature=0.5,
+                            max_tokens=300,
+                            top_p=1,
+                            # stream=True,
+                            messages=[
+                                {"role": "user", "content": f'\n{corpus}\n\nSummarize the above {query_type}s {complexity} to answer this question: {text}\n\nSummary:\n\n'},
+                            ]
+                        )['choices'][0]['message']['content']
+                        response2 = f'<b>Question:</b> {text}\n\n<b>Answer:</b> {response2}'
+
+            # Graph Generation
+
+            return response2, dataframe[dataframe['similarity'] > slider][['similarity', 'translated_text']][0:15], response2, "<span class='hsub'>Analysis:</span>Manual query"
+
+        return response2, dataframe[dataframe['similarity'] > slider][['similarity', 'translated_text']][0:15], response2, "<span class='hsub'>Analysis:</span>Manual query"
+
+def clear_output(filename, titleBlock):
+    titleBlock = re.sub('<[^<]+?>', '', titleBlock)
+    # remove all \n 
+    # trim
+    titleBlock = titleBlock.replace("\n", "")
+
+    print(titleBlock)
+    print(filename)
+    print(filename + titleBlock + "_CACHE.txt")
+    try:
+        os.remove(filename + titleBlock + "_CACHE.txt")
+    except Exception as e:
+        print (e)
+
+        pass
+    return 'Cache has been cleared'
+
+def save_output(tab, data_answer):
+
+# def save_output(tab):
+    if tab == "Summary":
+        print("summary save")
+        print(data_answer)
+        print (data_answer.value)
+        print(dir(data_answer))
+        # with open("data_answer.txt", "+ab") as f:
+        # open and append to it
+        with open("data_answer.txt", "a") as f:
+            try:
+                f.write(data_answer.value)
+                f.write(data_answer)
+            except:
+                pass
+     
+    elif tab == "Table":
+        try:
+            similar_reviews_dataframe = pd.DataFrame(similar_reviews)
+            similar_reviews_dataframe.to_csv("similar_reviews.csv", index=False, encoding='utf-8-sig')
+        except:
+            pass
+    else:
+        try:
+            g.save_graph("graph.html")
+        except:
+            pass
+
+
+def generate_new_examples(text):
+    # GENERATE NEW EXAMPLES BASED ON QUERY
+    new_examples = openai.ChatCompletion.create(
+    model="gpt-3.5-turbo",
+    temperature=0.7,
+    max_tokens=100,
+    top_p=1,
+    # stream=True,
+    messages=[
+        {"role": "user", "content": f'Generate a list of 4 most relevent questions related to this question : {text}. Output should be in a comma separated string format, no numbers, ordering. (example: What is this text about?, What is the main trend?,...) There is no need to enumerate each element.\n\n'},
+    ]
+    )['choices'][0]['message']['content']
+
+    
+    new_examples = new_examples.split('\n')
+    # make a list for each element
+
+    new_examples = [x for x in new_examples if x != '']
+    new_examples = [x.strip() for x in new_examples]
+    new_examples = [x.split(',') for x in new_examples]
+    return new_examples
+
+    
+def summarize_video(url):
+    loader = YoutubeLoader.from_youtube_channel(url)
+    result = loader.load()
+
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=0)
+    texts = text_splitter.split_documents(result)
+    print(len(texts))
+ 
+
+    #  We first try the chain with the default chain type
+    # if length of the text is more than 2000 tokens, we will use map reduce (summary of chunks)
+
+    try:
+        chain = load_summarize_chain(llm, chain_type='stuff', verbose=True)
+        print('ChainType: stuff')
+        # store intermediate steps
+        return chain.run(result)
+
+    except:
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=2000, chunk_overlap=0)
+        texts = text_splitter.split_documents(result)
+        # print(len(texts))
+        chain = load_summarize_chain(llm, chain_type='map_reduce', verbose=True)
+        print('ChainType: map reduce')
+        return chain.run(texts)
+
+
+# def main():
+    # global similar_reviews, g, query_type, response2, Output, output_html, html, new_examples, samples
+
+def get_graph(dataframe):
+    
+    print("step 1")
+    from sklearn.cluster import KMeans
+    from sklearn.metrics.pairwise import cosine_similarity
+    
+
+    embeddings_array = dataframe['embeddings'].tolist()
+    print ("step 2")
+    num_clusters = 3  # Adjust the number of clusters as needed
+    kmeans = KMeans(n_clusters=num_clusters, random_state=42)
+    cluster_labels = kmeans.fit_predict(embeddings_array)
+
+    print(cluster_labels)
+    sentences = dataframe['translated_text'].tolist()
+    print ("step 3")
+    G = nx.DiGraph()
+
+    
+    cos_sim_matrix = cosine_similarity(embeddings_array)
+    print(cos_sim_matrix)
+    print ("step 4")
+    for idx, label in enumerate(cluster_labels):
+        G.add_node(idx, sentence=sentences[idx], cluster=label)
+
+    for i in range(len(sentences)):
+        for j in range(len(sentences)):
+            if i != j:
+                #if cos_sim_matrix[i, j] > 0.8:
+                    G.add_edge(i, j, weight=cos_sim_matrix[i, j])
+                # else:
+                #     continue
+    print ("step 5")
+    plt.figure(figsize=(10, 10))
+
+    pos = nx.spring_layout(G, k=0.5, iterations=50)
+
+    print ("step 6")
+    G_undirected = G.to_undirected()
+
+    from community import community_louvain
+    node_to_community = community_louvain.best_partition(G_undirected)
+
+    print ("step 7")
+    community_to_color = {
+            0 : 'tab:pink',
+            1 : 'tab:orange',
+            2 : 'tab:purple',
+            3 : 'tab:blue',
+    }
+
+    node_color = {node: community_to_color[community_id] for node, community_id in node_to_community.items()}
+
+    print ("step 8")
+    reducer = umap.UMAP(n_components=2, random_state=42)
+    embeddings_2d = reducer.fit_transform(embeddings_array)
+
+    def normalize_weight(weight, min_weight, max_weight):
+        return (weight - min_weight) / (max_weight - min_weight)
+    
+
+    def visualize_graph_plotly(graph, embeddings_2d, scaling_factor=3):
+
+        print ("step 9")
+        min_weight = min((data['weight'] for _, _, data in graph.edges(data=True)))
+        max_weight = max((data['weight'] for _, _, data in graph.edges(data=True)))
+
+        fig = go.Figure()
+
+
+        print ("step 10")
+        # Add edges with width based on the normalized weights
+        print(len(graph.edges()))
+        for i, j in graph.edges():
+            print(i)
+            weight = normalize_weight(graph[i][j]['weight'], min_weight, max_weight)
+            # weight=0.1
+            fig.add_shape(
+                type="line",
+                x0=embeddings_2d[i][0],
+                x1=embeddings_2d[j][0],
+                y0=embeddings_2d[i][1],
+                y1=embeddings_2d[j][1],
+                yref="y",
+                xref="x",
+                line=dict(color="rgba(211, 211, 211, 0.5)", width=weight * scaling_factor * 0.7),
+            )
+        print ("step 11")
+        # Add nodes
+        for idx, emb in enumerate(embeddings_2d):
+            closeness = nx.closeness_centrality(G)[idx]
+            degree = nx.degree_centrality(G)[idx]
+            betweenness = nx.betweenness_centrality(G)[idx]
+            eigen = nx.eigenvector_centrality(G)[idx]
+
+            fig.add_trace(
+                go.Scatter(
+                    x=[emb[0]],
+                    y=[emb[1]],
+                    mode="markers+text",
+                    text=[graph.nodes[idx]["sentence"]],
+                    textposition="bottom center",
+                    marker=dict(color=node_color[idx][4:], size=closeness * 40),
+                    # add closeness, degree, betweenness and sentence as hover text
+                    hovertext=[f"{graph.nodes[idx]['sentence']} <br> closeness_centrality: {closeness:.2f} <br> degree_centrality: {degree:.2f} <br> betweenness_centrality: {betweenness:.2f} <br> eigenvector_centrality: {eigen:.2f}"],
+                )
+            )
+
+        print("for completed")
+
+        fig.update_layout(showlegend=False, plot_bgcolor="white", width=1200, height=800)
+        fig.update_xaxes(showticklabels=False, showgrid=False, zeroline=False,
+                        showline=False, automargin=False, showspikes=False)
+        fig.update_yaxes(showticklabels=False, showgrid=False, zeroline=False,
+                        showline=False, automargin=False, showspikes=False)
+        
+        fig.update_layout(title_text="Test Graph Visualization", title_x=0.5, title_font_size=30, title_font_color='black')
+
+        return fig
+    
+    return visualize_graph_plotly(G, embeddings_2d, scaling_factor = 10)
+
+def update_examples(samples):
+    return gr.Dataset.update(samples=samples)
+
+def print_samples():
+    global samples
+    return {"samples": samples}
+
+def load_example(example_id):
+    global samples
+    return samples[example_id][0]
+
+url_params = gr.JSON({}, visible=False, label="URL Params")
+
+
+def getAnalysisLabel(id):
+    if id == 'exec_sum': 
+        return 'Executive Summary'
+    elif id == 'top_clust':
+        return 'Topic Cluster'
+
+    elif id == 'trend_analysis':
+        return 'Trend Analysis'
+    
+    elif id == 'emot_clust':
+        return 'Sentiment Analysis'
+    
+    elif id == 'swot_':
+        return 'SWOT Analysis'
+    
+    elif id == 'competitor':
+        return 'Competitor Analysis'
+
+tabs = [
+    {
+        "id": 0,
+        "label": "Social Media",
+        "content": {
+            "exec_sum":None,
+            "top_clust": None,
+            "emot_clust": None,
+            "swot_": None,
+            # "competitor": None,
+        },
+        "filename": "profdemo_cleaned.xlsx"
+    },
+    {
+        "id": 1,
+        "label": "News/Publications",
+        "content": {
+            "exec_sum":None,
+            "trend_analysis": None,
+            # "top_clust": None,
+            "competitor" : None,
+            "swot_": None,
+        },
+        "filename": "cleaned_news.xlsx"
+    },
+    # {
+    #     "id": 0,
+    #     "label": "Mozzarella",
+    #     "content": {
+    #         "exec_sum":None,
+    #         "top_clust": None,
+    #         # "trend_analysis": None,
+    #         "emot_clust": None,
+    #         # "swot_": None,
+    #         # "competitor" : None,
+    #     },
+    #     "filename": "MozzarellaTW.xlsx"
+    # },
+    # {
+    #     "id": 1,
+    #     "label": "Onion",
+    #     "content": {
+    #         "exec_sum":None,
+    #         "top_clust": None,
+    #         "emot_clust": None,
+    #         # "competitor" : None,
+    #     },
+    #     "filename": "OnionTW.xlsx"
+    # },
+    # {
+    #     "id": 2,
+    #     "label": "Brand - Social Media",
+    #     "content": {
+    #         "exec_sum":None,
+    #         "top_clust": None,
+    #         "trend_analysis": None,
+    #         "emot_clust": None,
+    #         "swot_": None,
+    #         "competitor" : None,
+    #     },
+    #     "filename": "LambWestonBrand.csv"
+    # },
+    # {
+    #     "id": 3,
+    #     "label": "Brand - News",
+    #     "content": {
+    #         "exec_sum":None,
+    #         "top_clust": None,
+    #         "trend_analysis": None,
+    #         "emot_clust": None,
+    #         "swot_": None,
+    #         "competitor" : None,
+    #     },
+    #     "filename": "LambWestonNews.csv"
+    # },
+]
+
+list = []
+maxSources = 10
+
+oTab = [] 
+attachButtons = []
+
+for element in tabs:
+    oTab.append( {
+        "exec_sum":None,
+        "top_clust": None,
+        "trend_analysis": None,
+        "emot_clust": None,
+        "swot_": None,
+        "competitor" : None,
+    })
+    attachButtons.append(None)
+
+
+get_window_url_params = """
+    function(url_params) {
+        var scriptElement = document.createElement("script");
+        scriptElement.src = '"""+gradio_js+"""?v="""+str(ra)+"""';
+        scriptElement.innerHTML = "console.log('This is dynamic JavaScript code');";
+        document.body.appendChild(scriptElement);
+        const params = new URLSearchParams(window.location.search);
+        url_params = Object.fromEntries(params);
+        return [url_params];
+    }
+"""
+
+runjs = """
+function(projJson,num,fullData) {  
+    console.log(fullData)
+
+    var localizations = fullData['localization']
+    console.log( fullData['localization'])
+    if (localizations) {
+        document.querySelectorAll('.hsub')[0].innerText = localizations['project'];
+        // document.querySelectorAll('.hsub')[1].innerText = "semmi";
+        // document.querySelectorAll('.hsub')[2].innerText = "semmi";
+
+        if (document.querySelector('#querybtn')) document.querySelector('#querybtn').innerText = localizations['talk_to_your_data']
+
+        var tabs = document.querySelectorAll('#targetColMiddle .tab-nav button'); 
+        tabs[0].innerText = localizations['overview'] || 'Overview'
+        tabs[1].innerText = localizations['rawdata'] || 'Raw Data'
+        tabs[2].innerText = localizations['visuals'] || 'Visuals'
+
+        document.querySelectorAll('.sideTitle span')[0].innerText = localizations['data_sources'] || 'Data sources'
+        document.querySelectorAll('.sideTitle span')[1].innerText = localizations['predefined_analysis'] || 'Predefined analysis'
+    }
+    document.querySelectorAll('.analysisButton').forEach(function(el) {
+       el.style.display = 'none';
+    });
+    Object.keys(projJson[num]['content']).forEach(function(key) {
+        document.querySelectorAll('.analysisButton').forEach(function(el) {
+            if (el.id == key) {
+                el.style.display = 'block';
+            }
+
+        });
+    });
+
+    document.querySelectorAll('.sourceButton').forEach(function(el) {
+        el.classList.remove('selectedSource');
+    });
+    document.querySelectorAll('.sourceButton').forEach(function(el) {
+        if (el.innerHTML == projJson[num]['label']) el.classList.add('selectedSource');
+    });
+    // NEM ÉRTEM MINEK KELL TÖBB OUTPUT
+    return [1, num,1,1]
+}
+"""
+
+def parse_URL_params(url_params):
+    # if url params has 'pid'
+    if 'pid' in url_params:
+        request = requests.get(rubik_backend + '?proj=' + url_params['pid'])
+        text = url_params['pid']
+    else:
+        request = requests.get(rubik_backend + '?proj=demo')
+        text = "demo"
+    
+    # textlowercase url_params['pid']
+    # first letter uppercase
+    textUpper = request.json()["brand"][0].upper() + request.json()["brand"][1:]
+    return [url_params, request.json()["sources"], request.json()["sources"], "<div class='brand'><span class='hsub'>Project:</span>"+textUpper+"</div>", request.json(), text]
+
+for i in range(maxSources):
+    list.append({"id":i, "name":"asd", "obj":"", "analList":[]})
+
+
+def variable_outputs(k):
+    global list
+    sourceArray = k
+    output = None
+    for i in range(len(sourceArray)):
+        if not output:
+            output = [list[i]["obj"].update(value=sourceArray[i]["label"],visible=True)]
+        else:
+            output += [list[i]["obj"].update(value=sourceArray[i]["label"],visible=True)]
+
+    remainingCount = maxSources - len(sourceArray)
+
+    for i in range(remainingCount):
+        output += [list[i]["obj"].update(value="",visible=False)]
+
+    return output
+
+url_params = gr.JSON({}, label="URL Params", visible=False)
+
+# with gr.Interface(theme=gr.themes.Soft(primary_hue='pink', secondary_hue='pink', neutral_hue='stone'), css="footer{display:none !important}") as app:
+# , css=".main{filter: blur(3px)}footer{display:none !important}"
+with gr.Blocks(theme=gr.themes.Soft(primary_hue='pink', secondary_hue='pink', neutral_hue='stone')) as app:
+
+    dat = gr.Markdown()
+    projdetails = gr.Textbox("test", label="Project Details", visible=False)
+    projJson = gr.JSON(visible=False)
+    fullProjectData = gr.JSON(visible=False)
+    projectUUID = gr.State(value="", label="projectUUID", visible=False)
+    if True:
+        summaries_state = gr.State(value=[], label="summaries_state")
+        executive = gr.State(value='', label="executive")
+        needHeader = True
+
+        # if needHeader and False:    
+        #     with gr.Row():
+        #         with gr.Column(scale=.1, elem_id="logocol"):
+        #             # gr.Markdown('<img id="logo" src="http://
+        #             # add a title at the center of the page
+        #             gr.Markdown('<a href="https://app.rubiklab.ai"><img id="logo" src="http://127.0.0.1:5500/logo.png" /></a>')
+                
+        #         with gr.Column(scale=.3):
+        #             gr.Markdown("<h1>Talkback</h1>")
+
+        # random number between 1 and 10000
+        ra = str(np.random.randint(1, 10000))
+        gr.Markdown(
+                """
+                <link rel='stylesheet' href='https://fonts.googleapis.com/css?family=Roboto:ital,wght@0,200;0,300;0,400;0,600;0,700;0,800;0,900;1,200;1,300;1,400;1,600;1,700;1,800;1,900&display=swap'>
+                <link rel="stylesheet" href='""" + gradio_css + """?v="""+str(ra)+"""'>
+                <link rel="icon" type="image/x-icon" href="https://api.rubiklab.ai/assets/favicon.ico">
+                """
+        )
+
+        # if needHeader:    
+        #     with gr.Row():
+        #         with gr.Column(elem_id="header"):
+        #             gr.Markdown("<h2>Nike</h2>")
+
+        #             selectedData = gr.Markdown('<span></span>', elem_id="datanameblock")
+        #             titleBlock = gr.Markdown("<span class='title'></span>", elem_id="titleblock")
+
+
+        df = pd.DataFrame()
+        with gr.Row():
+            with gr.Column(scale=.4, elem_id="leftContainer"):
+                with gr.Row(elem_id="header"):
+                    proj = gr.Markdown("")
+                    selectedData = gr.Markdown('<span></span>', elem_id="datanameblock")
+                    titleBlock = gr.Markdown("<span class='title'></span>", elem_id="titleblock")
+
+
+                
+                gr.Markdown("<h3 class='sideTitle'><span>Data sources</span></h3>")
+                tabs = []
+                analysisList = []
+                for i in range(maxSources):
+                    list[i]["obj"] = gr.Button(value="" + str(i),visible=False, elem_classes="sourceButton")
+                    list[i]["index"] = gr.Number(i, visible=False)
+                    tabs.append(list[i]["obj"])
+
+                gr.Markdown("<h3 class='sideTitle predefined'><span>Predefined analysis</span></h3>")
+                # analysisList = ['exec_sum', 'top_clust', 'emot_clust', 'swot_', 'trend_analysis', 'competitor']
+                oa = {
+                    'exec_sum': gr.Button("Executive Summary", elem_id="exec_sum", elem_classes=["analysisButton"]),
+                    'top_clust': gr.Button("Topic Clusters", elem_id="top_clust", elem_classes=["analysisButton"]),
+                    'emot_clust': gr.Button("Sentiment Analysis", elem_id="emot_clust", elem_classes=["analysisButton"]),
+                    'swot_': gr.Button("SWOT Analysis", elem_id="swot_", elem_classes=["analysisButton"]),
+                    'trend_analysis': gr.Button("Trend Analysis", elem_id="trend_analysis", elem_classes=["analysisButton"]),
+                    'competitor': gr.Button("Competitor Analysis", elem_id="competitor", elem_classes=["analysisButton"]),
+                }
+                newcluster = gr.Button("New Cluster", elem_id="newcluster", elem_classes=["newcluster"], visible=False)
+                newgraph = gr.Button("New graph", elem_id="newcluster", elem_classes=["newgraph"], visible=False)
+
+                # for i in range(len(analysisList)):
+                #     gr.Button(analysisList[i], elem_classes=["analysisButton"], elem_id=analysisList[i])
+
+                # iterate throu oa
+                gr.Button("Talk to your data", elem_id="querybtn")
+                projJson.change(variable_outputs, projJson, tabs)
+                gr.Markdown(f'url params value: {url_params.value}', visible=False)
+                threshold = gr.Slider(minimum=0, maximum=1, label="Threshold", visible=False)
+                csv_file = gr.File(label="File (csv, excel, h5..)", elem_id="fupload",visible=False)
+
+                brand = gr.State('brand')
+                industry = gr.State('industry')
+
+                csvs = gr.State('csvnum')
+                filename = gr.State('filename')
+
+                graph_type = gr.State('graph_type')
+                
+                # THE DATASET
+                data_storage = gr.State(label="data_storage")
+
+                # TOPICS LIST
+                list_of_topics = gr.State(label="list_of_topics")
+                
+                # add image output
+                with gr.Tab("Word Cloud"):
+                    Imaj = gr.Image(label="Word Cloud")
+
+                with gr.Tab("Polarity"):
+                    Polarity = gr.Plot(label="Polarity")
+            
+            app.load(
+                fn=parse_URL_params,
+                inputs=[url_params],
+                outputs=[url_params, projJson, projdetails, proj, fullProjectData,projectUUID],
+                _js=get_window_url_params
+            )
+            url_params.render()
+            with gr.Column(scale=2, elem_id="targetColMiddle"):
+                
+                graphHelper = gr.Markdown("<span></span>")
+                with gr.Tab("Overview", elem_id="overviewTab"):
+                    tab = 'Summary'
+                    data_answer = gr.Textbox(visible=False, elem_id="hiddenTextbox")
+                    # gr.Textbox.style(data_answer)
+                    formattedres = gr.Markdown("<span class='description'></span>")
+
+                    with gr.Row():
+                        with gr.Column(scale=6):
+                            # add empty space
+                            pass
+                        with gr.Column(scale=.5, min_width=100):
+                            clear_button = gr.Button("Clear", visible=True, elem_id="clear_button")
+                            clear_button.click(fn=clear_output, inputs=[filename, titleBlock], outputs=[formattedres])
+                            # save_button = gr.Button("Save", visible=True, elem_id="save_button")
+                            # save_button.click(lambda: save_output("Summary", data_answer))
+
+
+                tab =  gr.Tab("Raw data", elem_id="rawdataTab")
+                with tab:
+                    tab = 'Table'
+                    similar_reviews = gr.Dataframe(label="Table", type="pandas", max_rows=20, overflow_row_behaviour='paginate', show_label=False)
+                    with gr.Row():
+                        with gr.Column(scale=6):
+                            # add empty space
+                            pass
+                        # with gr.Column(scale=.5, min_width=100):
+                            # save_button = gr.Button("Save", visible=True)
+                            # save_button.click(lambda: save_output("Table", data_answer))
+                with gr.Tab("Visuals"):
+                    tab = 'Graph'
+                    graph = gr.Plot(elem_id="cluster", label="")
+                    graph2 = gr.Plot(elem_id="cluster2", label="", visible=False)
+                    # with gr.Tab("Word Cloud"):
+                    Imaj = gr.Image(label="Word Cloud", elem_id="vwordcloud")
+            
+                    # with gr.Tab("Polarity"):
+                    Polarity = gr.Plot(label="Polarity", elem_id="vpolarity")
+                    
+                    with gr.Row():
+                        with gr.Column(scale=1):
+                            clearbutton = gr.Button("Remove and regenerate", visible=True, elem_id="cleardrop_button")
+                            clearkeepbutton = gr.Button("Keep and regenerate", visible=True, elem_id="clearkeep_button")
+
+
+                    with gr.Row():
+                        with gr.Column(scale=6):
+                            # add empty space
+                            pass
+                        # with gr.Column(scale=.5, min_width=100):
+                            # save_button = gr.Button("Save", visible=True)
+                            # gr.Button.style(save_button, color="secondary")
+                            # save_button.click(lambda: save_output("Graph", data_answer))
+
+
+                with gr.Row(elem_id="query"):
+                    with gr.Column(scale=1, elem_id='query1'):
+
+                        data_answerQuery = gr.Textbox(label="", lines=10, visible=False)
+                        # gr.Textbox.style(data_answer)
+                        formattedresQuery = gr.Markdown("<span class='description'></span>")
+
+                        query = gr.Textbox(lines=1, placeholder="Start typing your question...", label=" ")
+                        gr.Textbox.style(query)
+                       
+                        submit_button = gr.Button("Submit", elem_id='submit')
+                        gr.Button.style(submit_button, color="secondary")
+
+                    with gr.Column(scale=.1, elem_id='query2'):
+                        samples = [["What insights can we take from this data?", "2 What insights can we take from this data?"]]
+
+                        examples = gr.Dataset(samples=samples, components=[query], type="index", label="Some hints for your next question, select which one you prefer.")
+
+
+      
+                def update_examples(query):
+                    global samples
+                    samples = generate_new_examples(query)
+                    return gr.Dataset.update(samples=samples)
+
+                def print_samples():
+                    global samples
+                    return {"samples": samples}
+
+                def load_example(example_id):
+                    global samples
+                    return samples[example_id][0]
+
+
+        def changesource(projJson, num):
+            # print("switching")
+            return 1
+        def doJS(projJson, num,fulldat):
+            # print("doing js")
+            return 1, num
+        
+        sourcelabel = gr.TextArea(elem_id="activeSource", visible=False)
+        sourceIndex = gr.Number(visible=False)
+        xy = gr.State()
+        for i in range(maxSources):
+            num = list[i]["index"]
+            list[i]["obj"].click(doJS,inputs=[projJson, num, fullProjectData], outputs=[xy, sourceIndex], _js=runjs).then(
+                load_csv, inputs=[projJson, num, fullProjectData, projectUUID], outputs=[data_storage, data_answer, similar_reviews, formattedres, filename, selectedData]).then(
+                generate_wordcloud, inputs=[data_storage], outputs=[Imaj]).then(
+                get_polarity, inputs=[data_storage], outputs=[Polarity, data_storage])
+
+
+        # SUMMARIZE VIDEO CONTENT
+        def checkData(filename):
+            print(filename) 
+        
+        dat = gr.State(label="dat")
+ 
+        oa['exec_sum'].click(fn=checkData, inputs=[filename], outputs=[]).then(get_executive_summary, inputs=[data_storage, brand, industry,summaries_state, csvs, graph_type,filename, fullProjectData, sourceIndex], outputs=[data_answer, similar_reviews, graphHelper, formattedres, titleBlock])
+        oa['top_clust'].click(get_topic_cluster, inputs=[data_storage, graph_type,filename], outputs=[graph, similar_reviews, list_of_topics, query, dat, graphHelper,formattedres,titleBlock])
+        oa['emot_clust'].click(emotional_mapping, inputs=[data_storage, industry, graph_type,filename, fullProjectData, sourceIndex], outputs=[data_answer, similar_reviews, graphHelper,formattedres,titleBlock])
+        oa['swot_'].click(get_executive_summary, inputs=[data_storage, brand, industry,summaries_state, csvs, graph_type,filename, fullProjectData, sourceIndex], outputs=[data_answer, similar_reviews, graphHelper, formattedres, titleBlock]).then(get_SWOT, inputs=[data_storage, brand, industry, executive, graph_type,filename, fullProjectData, sourceIndex], outputs=[data_answer, graphHelper,formattedres,titleBlock])   
+        oa['trend_analysis'].click(get_trend_summary, inputs=[data_storage, list_of_topics, brand, industry, graph_type,filename, fullProjectData, sourceIndex], outputs=[data_answer, similar_reviews, graphHelper,formattedres,titleBlock]) 
+        oa['competitor'].click(get_competitive, inputs=[brand, industry, graph_type,filename,data_storage, fullProjectData, sourceIndex], outputs=[data_answer, graphHelper,formattedres,titleBlock])
+
+
+        def clear_data(filename):
+            if os.path.exists(filename + 'df3.csv'):
+                os.remove(filename + 'df3.csv')
+
+        def rename_data_timestamp(filename):
+            if os.path.exists(filename + 'df3.csv'):
+                os.rename(filename + 'df3.csv', filename + str(datetime.datetime.now()) + '.csv')
+
+        clearbutton.click(clear_data, inputs=[filename], outputs=[]).then(get_topic_cluster, inputs=[data_storage, graph_type,filename], outputs=[graph, similar_reviews, list_of_topics, query, dat, graphHelper,formattedres,titleBlock])
+        clearkeepbutton.click(rename_data_timestamp, inputs=[filename], outputs=[]).then(get_topic_cluster, inputs=[data_storage, graph_type,filename], outputs=[graph, similar_reviews, list_of_topics, query, dat, graphHelper,formattedres,titleBlock])
+
+        # app.launch(share=True, server_name="0.0.0.0", server_port=7860)
+        def get_new_topic(data_storage, graph_type,filename):
+            from bertopic import BERTopic
+            from bertopic.representation import OpenAI
+            from sklearn.cluster import KMeans
+            from sklearn.feature_extraction.text import CountVectorizer
+            import pandas as pd 
+            # import openai
+
+            # openai.api_key = 'sk-2Ulixq
+            prompt = """
+            I have a topic that contains the following documents: 
+            [DOCUMENTS]
+            The topic is described by the following keywords: [KEYWORDS]
+
+            Based on the information above, extract a short topic label in the following format:
+            topic: <topic label>
+            """
+
+            vectorizer_model=CountVectorizer(stop_words="english")
+
+            # df = pd.read_excel('C:/Users/sinan/Downloads/profdemo_cleaned (1).xlsx')
+            df = data_storage
+            df['translated_text'] = df['translated_text'].apply(lambda x: str(x))
+
+            docs = df['translated_text'].tolist()
+
+            representation_model = OpenAI(model="gpt-3.5-turbo", delay_in_seconds=.5, chat=True)
+
+            if len(docs) < 100:
+                cluster_model = KMeans(n_clusters=3)
+                topic_model = BERTopic(hdbscan_model=cluster_model, representation_model=representation_model, vectorizer_model=vectorizer_model)
+            else:   
+                cluster_model = KMeans(n_clusters=6)
+
+                # representation_model = 'bert-base-nli-mean-tokens'
+                n_gram_range = (1, 1)  # set the range of n-grams to be considered
+                min_topic_size = 10 # set the minimum number of documents in each topic
+
+                topic_model = BERTopic(hdbscan_model=cluster_model, representation_model=representation_model, n_gram_range=n_gram_range, min_topic_size=min_topic_size)
+                # topic_model = BERTopic(representation_model=representation_model, nr_topics=8)
+
+
+
+            topics, probs = topic_model.fit_transform(docs)
+
+            return topic_model.visualize_documents(docs, width=1200, height=800, title='Topic Clustering', hide_annotations=True)
+
+        newcluster.click(get_new_topic, inputs=[data_storage, graph_type,filename], outputs=[graph2])
+        newgraph.click(get_graph,inputs=[data_storage], outputs=[graph2])
+        
+        # 1. ADD QUESTIONS TO THE QUERY
+        examples.click(load_example, inputs=[examples], outputs=[query])       
+
+                                                                                                                                                            # UNCOMMENT FOR TEXT TO SPEECH OUTPUT
+        submit_button.click(get_synthetic_comment, inputs=[query, threshold, data_storage ], outputs=[data_answer, similar_reviews, formattedresQuery, titleBlock]).success(update_examples, inputs=[query], outputs=[examples])
+
+
+
+def same_auth(username, password):
+    return username == password
+if __name__ == "__main__":
+    # app.launch(share=True, server_name="0.0.0.0", server_port=7860, auth=same_auth, auth_message="Please enter the same username and password")
+    app.launch(share=True, server_name="0.0.0.0", server_port=7860)
+
+# if __name__ == "__main__":
+#     main()

uploader/.gitignore

@@ -0,0 +1 @@
+uploads/*

uploader/uploadapi.py

@@ -0,0 +1,73 @@
+from flask import Flask, render_template, request, redirect, url_for
+import os
+# cors
+from flask_cors import CORS, cross_origin
+app = Flask(__name__)
+
+cors = CORS(app)
+# Set upload folder
+UPLOAD_FOLDER = 'uploads'
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+
+# Set allowed file extensions
+ALLOWED_EXTENSIONS = {'xlsx', 'csv'}
+
+def allowed_file(filename):
+    """Helper function to check if a file has an allowed extension"""
+    return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
+
+@app.route('/upload', methods=['GET', 'POST'])
+def upload_file():
+    if request.method == 'POST':
+        # Check if a file was uploaded
+        if 'file' not in request.files:
+            return redirect(request.url)
+        
+        if 'uuid' not in request.form:
+            return "no uuid"
+        
+        file = request.files['file']
+        
+        # Check if file has an allowed extension
+        if not allowed_file(file.filename):
+            return 'Invalid file type'
+        # get uuid from form
+        uuid = request.form['uuid']
+        # Save file to upload folder
+
+        # if folder with name: uuid does not exist, create it
+        if not os.path.exists(os.path.join(app.config['UPLOAD_FOLDER'], uuid)):
+            os.makedirs(os.path.join(app.config['UPLOAD_FOLDER'], uuid))
+        
+        file.save(os.path.join(app.config['UPLOAD_FOLDER'], uuid, file.filename))
+        
+        return {"status":"success","filename": file.filename, "uuid": uuid}
+    
+    return "did nothing..."
+    # return render_template('upload.html')
+
+@app.route('/clearcache', methods=['POST'])
+def clear_cache():
+    print(request.json)
+    if request.method == 'POST':
+        if 'uuid' not in request.json:
+            return "no uuid"
+        if 'filename' not in request.json:
+            return "no filename"
+        
+        uuid = request.json['uuid']
+        folder = os.path.join(app.config['UPLOAD_FOLDER'],uuid)
+        filename = request.json['filename']
+        if filename in os.listdir(folder):
+            # os.remove(os.path.join(folder,filename))
+            # rename this file to filename + _old
+            os.rename(os.path.join(folder,filename),os.path.join(folder,filename+"_old"))
+            return {"status":"success"}
+    
+    
+    return "did nothing..."
+    # return render_template('upload.html')
+
+
+if __name__ == '__main__':
+    app.run(debug=True, port=5100)

uploader/uploads/a1b1e9ca-7d76-4663-b48c-12f284304475/MozzarellaTW.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9aa8b4dc58d1ebfd732922d8ff5b894be8f56af223d9636b01f3d5490b3bdb91
+size 11523457

uploader/uploads/a1b1e9ca-7d76-4663-b48c-12f284304475/MozzarellaTW.csvExecutive Summary_CACHE.txt_old

@@ -0,0 +1,56 @@
+
+# Common and Hot Topics in Tourism Blog Posts
+
+The blog posts in the data corpus reveal a number of common and hot topics related to tourism. These topics include rebranding of tourist products, the impact of technology on the tourism industry, the importance of customer experience, and the need for sustainable tourism practices. 
+
+## Rebranding of Tourist Products
+
+The blog posts discuss the importance of rebranding tourist products in order to make them more attractive to customers. For example, one post discusses the proposed rebrand of the mozzarella stick design, with the aim of making the product the best on the planet. Joe audibly moaned in approval of the new design, suggesting that the brand will be beautiful. This example illustrates the potential of rebranding to create a more attractive product that customers will be drawn to.
+
+## Impact of Technology on the Tourism Industry
+
+The blog posts also discuss the impact of technology on the tourism industry. Technology has revolutionized the way that tourists plan and book their trips, with many now opting to use online booking platforms and apps. This has had a significant impact on the industry, with travel agencies having to adapt their services to meet the needs of tech-savvy customers.
+
+## The Importance of Customer Experience
+
+The blog posts also emphasize the importance of customer experience in the tourism industry. Customers are increasingly looking for a personalized and unique experience when they travel, and travel agencies must be able to provide this in order to stay competitive. This means that travel agencies must be able to provide a range of services and amenities that cater to the needs of their customers.
+
+## The Need for Sustainable Tourism Practices
+
+Finally, the blog posts discuss the need for sustainable tourism practices. Sustainable tourism is becoming increasingly important in the industry, as tourists are looking for ways to reduce their environmental impact when they travel. This means that travel agencies must be aware of the environmental impact of their services and strive to reduce their carbon footprint.
+
+# Key Trends in Tourism: Implications for Tourists and the Industry
+
+The blog posts reveal a number of key trends in the tourism industry that have implications for tourists and the industry as a whole. These trends include the increasing importance of technology, the need for personalized customer experiences, and the need for sustainable tourism practices.
+
+## Increasing Importance of Technology
+
+The blog posts reveal that technology is playing an increasingly important role in the tourism industry. Technology has revolutionized the way that tourists plan and book their trips, with many now opting to use online booking platforms and apps. This has had a significant impact on the industry, with travel agencies having to adapt their services to meet the needs of tech-savvy customers.
+
+## Need for Personalized Customer Experiences
+
+The blog posts also emphasize the importance of providing personalized customer experiences in the tourism industry. Customers are increasingly looking for a unique and personalized experience when they travel, and travel agencies must be able to provide this in order to stay competitive. This means that travel agencies must be able to provide a range of services and amenities that cater to the needs of their customers.
+
+## Need for Sustainable Tourism Practices
+
+Finally, the blog posts discuss the need for sustainable tourism practices. Sustainable tourism is becoming increasingly important in the industry, as tourists are looking for ways to reduce their environmental impact when they travel. This means that travel agencies must be aware of the environmental impact of their services and strive to reduce their carbon footprint.
+
+# Observations and Key Findings from Tourism Blog Posts
+
+The blog posts in the data corpus reveal a number of common and hot topics related to tourism, as well as key trends that have implications for tourists and the industry as a whole. 
+
+## Common and Hot Topics
+
+The blog posts discuss a number of common and hot topics related to tourism. These topics include rebranding of tourist products, the impact of technology on the tourism industry, the importance of customer experience, and the need for sustainable tourism practices. 
+
+## Key Trends
+
+The blog posts also reveal a number of key trends in the tourism industry that have implications for tourists and the industry as a whole. These trends include the increasing importance of technology, the need for personalized customer experiences, and the need for sustainable tourism practices.
+
+# Takeaways and Recommendations for Future Tourism Practices
+
+The analysis of the blog posts in the data corpus reveals a number of key takeaways and recommendations for future tourism practices. 
+
+## Takeaways
+
+The blog posts reveal that technology is playing an increasingly important role in the tourism industry, and travel agencies must be able to adapt their services to meet the needs of tech-savvy customers. Additionally, customers are increasingly looking for a personalized and unique experience when they travel, and travel agencies must be able to provide this in order to stay competitive. Finally, sustainable tourism practices are becoming increasingly important in

uploader/uploads/a1b1e9ca-7d76-4663-b48c-12f284304475/MozzarellaTW.xlsx

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+oid sha256:cbd0f153cc9defd4bcafda2fc05bb70eda04dfb112b78cff8c6e69629b0c9ffa
+size 4441676

uploader/uploads/a1b1e9ca-7d76-4663-b48c-12f284304475/MozzarellaTW.xlsxExecutive Summary_CACHE.txt

@@ -0,0 +1,55 @@
+# Executive Summary: Revolutionizing the Mozzarella Stick Industry
+
+## The Mozzarella Stick Rebranding Phenomenon
+
+A groundbreaking rebranding of the mozzarella stick design has been proposed, aiming to create the best mozzarella stick on the planet. This innovative design has been met with overwhelmingly positive reactions, with Joe audibly moaning in appreciation. The brandmark has been updated to reflect this new design, signifying a significant shift in the industry.
+
+### Key Trends and Patterns
+
+- A surge in consumer interest for unique and innovative food products
+- Increasing demand for high-quality, gourmet mozzarella sticks
+- The power of rebranding in capturing consumer attention and driving sales
+
+## Implications and Opportunities for the Industry
+
+The mozzarella stick rebranding presents a plethora of opportunities for industry players, including:
+
+- Capitalizing on the growing consumer interest in innovative food products
+- Differentiating their offerings from competitors through unique designs and flavors
+- Enhancing brand image and recognition through strategic rebranding efforts
+
+However, it is crucial to consider the challenges and needs of the industry, such as:
+
+- Ensuring consistent product quality and taste across different markets
+- Adapting to varying consumer preferences and cultural nuances
+- Managing potential supply chain disruptions and ingredient sourcing issues
+
+## Cultural, Social, and Contextual Nuances
+
+As an expert sociologist, it is essential to recognize the cultural, social, and contextual factors that may impact the success of the rebranded mozzarella stick. These include:
+
+- The role of social media in driving food trends and consumer preferences
+- The influence of local culinary traditions and tastes on product acceptance
+- The importance of sustainability and ethical sourcing in consumer decision-making
+
+## Potential Risks and Challenges
+
+The rebranding of the mozzarella stick may present certain risks and challenges, such as:
+
+- Resistance from traditionalists who prefer classic mozzarella stick designs
+- Potential backlash from consumers who perceive the rebranding as a gimmick
+- Difficulties in maintaining product consistency and quality during rapid expansion
+
+### Strategic Recommendations
+
+To mitigate these risks and challenges, industry players should:
+
+- Conduct thorough market research to understand consumer preferences and cultural nuances
+- Engage in transparent communication and marketing efforts to convey the value of the rebranding
+- Implement robust quality control measures to ensure product consistency and excellence
+
+## Key Takeaways
+
+- The mozzarella stick rebranding presents a significant opportunity for industry players to capitalize on growing consumer interest in innovative food products
+- Understanding and adapting to cultural, social, and contextual nuances is crucial for success in diverse markets
+- Addressing potential risks and challenges through strategic planning and execution will ensure the rebranded mozzarella stick remains a game-changer in the industry

uploader/uploads/a1b1e9ca-7d76-4663-b48c-12f284304475/OnionTW.xlsx

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+version https://git-lfs.github.com/spec/v1
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+size 3829500

uploader/uploads/demo/LambWestonBrand.csvExecutive Summary_CACHE.txt

@@ -0,0 +1,41 @@
+# Executive Summary: Consumer Goods Industry Insights and Opportunities
+
+## Key Trends and Patterns in Earnings Reports
+
+A thorough analysis of earnings reports from several consumer goods companies, including Constellation Brands (STZ), Lamb Weston Holdings, Levi Strauss (LEVI), RPM International (RPM), and WDFCToday, reveals the following key trends and patterns:
+
+- Strong revenue and EPS beats for Conagra Brands and Lamb Weston last week, indicating a positive outlook for the industry.
+- Anticipated earnings releases for the week of April 2023 include ConAgra, Constellation Brands, Lamb Weston, SAIC, and Acuity Brands (AYI).
+- Introduction of Maliha Subhani as the Director of Sales for Asia at Lamb Weston, signaling the company's focus on expanding its presence in the region.
+- Launch of Lamb Weston's Super Crispy Tots product, showcasing the company's commitment to innovation and meeting consumer demands.
+
+## Implications and Opportunities for the Industry
+
+Considering the needs and challenges of the consumer goods industry, the following implications and opportunities can be derived from the data:
+
+- **Expansion in Asia**: The appointment of Maliha Subhani as the Director of Sales for Asia at Lamb Weston highlights the potential for growth in the region. Companies should consider exploring opportunities to expand their presence in Asia to capitalize on this potential.
+- **Innovation and Product Development**: The launch of Lamb Weston's Super Crispy Tots product demonstrates the importance of innovation and meeting consumer demands. Companies should invest in research and development to create new products that cater to evolving consumer preferences.
+- **Strategic Partnerships**: Discounts available on brands such as Lamb Weston, Meatless Farm, Tilda, and Quorn suggest the possibility of strategic partnerships and collaborations. Companies should explore opportunities to work together to drive growth and create value for consumers.
+
+## Cultural, Social, and Contextual Nuances
+
+To ensure the relevance and insightfulness of this analysis across diverse audiences, the following cultural, social, and contextual nuances have been considered:
+
+- **Cultural Sensitivity**: Companies should be mindful of cultural differences when expanding into new markets, particularly in Asia. Tailoring products and marketing strategies to suit local tastes and preferences will be crucial for success.
+- **Sustainability and Health**: As consumers become increasingly conscious of the environmental and health impacts of their choices, companies should prioritize sustainability and health in their product offerings and business practices.
+- **Economic Factors**: The ongoing global economic recovery may impact consumer spending patterns. Companies should monitor economic indicators and adjust their strategies accordingly to remain competitive.
+
+## Potential Risks and Challenges
+
+The following potential risks and challenges have been identified, along with strategic recommendations for mitigating these issues:
+
+- **Supply Chain Disruptions**: Ongoing global supply chain challenges may impact the availability and cost of raw materials. Companies should diversify their supply chains and invest in technology to improve efficiency and reduce the risk of disruptions.
+- **Regulatory Changes**: Changes in regulations and trade policies may affect the consumer goods industry. Companies should stay informed about relevant policy changes and adapt their strategies accordingly.
+- **Competitive Landscape**: The consumer goods industry is highly competitive, with new entrants and disruptive technologies constantly emerging. Companies should invest in innovation and strategic partnerships to maintain a competitive edge.
+
+## Key Takeaways
+
+- Strong earnings reports and anticipated releases indicate a positive outlook for the consumer goods industry.
+- Opportunities for growth and innovation include expansion in Asia, product development, and strategic partnerships.
+- Companies should consider cultural, social, and contextual nuances when crafting strategies and entering new markets.
+- Potential risks and challenges include supply chain disruptions, regulatory changes, and a competitive landscape. Companies should invest in innovation, diversify supply chains, and stay informed about policy changes to mitigate these risks.

uploader/uploads/demo/MozzarellaTW.xlsx

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uploader/uploads/demo/MozzarellaTW.xlsxdf3.csv

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+x,y,z,cluster_number,cluster_name_str,trimmed_text,gpt_cluster
+10.601388,4.2817106,14.682537,5,like-look-looked-kinda,7953 Ong look like mozzarella stick,Mozzarella Stick Comparisons
+12.674002,6.303694,15.591997,4,like-good-eat-cheese,good mozzarella stick gona get every time,Mozzarella Stick Eating and Reviews
+12.60497,5.406012,16.470526,3,like-cheese-good-lol,sleepily warms mozzarella stick,Mozzarella Stick Obsession
+8.510767,7.263887,14.593172,1,face-joy-stick-joyface,Completely sane normal smiling face tear,Mozzarella Stick Joy.
+11.486528,5.7899384,14.661182,4,like-good-eat-cheese,love child build like mozzarella stick like soft still stick,Mozzarella Stick Eating and Reviews
+13.1321125,6.283302,16.597675,3,like-cheese-good-lol,Tryna lay White Castle mozzarella stick tasting mid day crazy,Mozzarella Stick Obsession
+9.55976,9.142235,14.67928,6,stick-face-loudly-faceloudly,Telling someone add cheese mozzarella stick make taste better absolutely absurd loudly cry faceloudly cry faceloudly cry face,Mozzarella Stick Complaints and Experiences
+12.054619,6.0084534,14.176854,4,like-good-eat-cheese,first mozzarella stick ever day great invention,Mozzarella Stick Eating and Reviews
+10.196211,4.705156,15.372414,5,like-look-looked-kinda,She look like mozzarella stick got put inside bag hot fry,Mozzarella Stick Comparisons
+14.949425,6.4882936,15.763675,0,cheese-lol-like-time,twizzlers mozzarella stick head week help,Mozzarella Stick Obsession
+13.248773,6.0689287,14.466486,4,like-good-eat-cheese,3njoyer idk mozzarella stick,Mozzarella Stick Eating and Reviews
+9.165916,7.3798456,14.733141,1,face-joy-stick-joyface,mattyr Obviously half mozzarella stick brekkie lad yea face tear joy,Mozzarella Stick Joy.
+12.651443,6.576064,14.746434,4,like-good-eat-cheese,woken maccies burger rapper bed half mozzarella stick inside Don even remember going maccies,Mozzarella Stick Eating and Reviews
+13.667232,6.7981696,15.831126,0,cheese-lol-like-time,put mozzarella stick tortilla got feeling like flipped switch,Mozzarella Stick Obsession
+10.207368,4.6932235,15.798386,5,like-look-looked-kinda,Bro She look like dehydrated mozzarella stick brushed Old Bay seasoning You living life correctly,Mozzarella Stick Comparisons
+9.684032,9.304138,14.791315,6,stick-face-loudly-faceloudly,burger big mozzarella stick patty thing good loudly cry face,Mozzarella Stick Complaints and Experiences
+11.215096,6.023473,15.081404,4,like-good-eat-cheese,Walt Brigitte sharing mozzarella stick Lady,Mozzarella Stick Eating and Reviews
+9.608759,7.1918297,14.736943,1,face-joy-stick-joyface,They got mozzarella stick dinner pick face tear joy,Mozzarella Stick Joy.
+10.414513,8.650654,14.603575,6,stick-face-loudly-faceloudly,best ive ever done bloodhound lmfao someone say youre proud,Mozzarella Stick Complaints and Experiences
+12.076197,8.139711,16.38829,2,face-weary-open-craving,wake every day dying cheeky mozzarella stick actual addiction tbh,Mozzarella Stick Cravings
+10.575868,4.299484,14.845252,5,like-look-looked-kinda,Ooh kinda look like tater tot mozzarella stick hybrid,Mozzarella Stick Comparisons
+13.618223,6.7552447,16.80607,3,like-cheese-good-lol,You make bite mozzarella stick make point,Mozzarella Stick Obsession
+12.875819,6.9482102,16.068165,4,like-good-eat-cheese,You never truly lived unless driven stick holding mozzarella stick drink would need enough cup holder,Mozzarella Stick Eating and Reviews
+12.803218,6.8789344,15.137978,4,like-good-eat-cheese,The guy gas station must patience saint idk many time could watch girl look back item store settle mozzarella cheese stick diet soda EVERY TIME asked wtf wrong,Mozzarella Stick Eating and Reviews
+10.496923,9.540256,14.444525,6,stick-face-loudly-faceloudly,way pushed jungwoo onto bed stretched like stick mozzarella loudly cry face giving capital punishment skull,Mozzarella Stick Complaints and Experiences
+14.194015,7.296956,15.782804,0,cheese-lol-like-time,Someone give fuching mozzarella stick,Mozzarella Stick Obsession
+12.74159,6.6890316,17.020506,4,like-good-eat-cheese,tbh ive never mozzarella stick,Mozzarella Stick Eating and Reviews
+10.538123,8.050993,17.184637,2,face-weary-open-craving,horizon busy mozzarella stick weary face,Mozzarella Stick Cravings
+10.827337,7.799086,16.970392,2,face-weary-open-craving,giant mozzarella stick today flushed face,Mozzarella Stick Cravings
+12.769217,6.809624,16.497969,4,like-good-eat-cheese,Ima turn mozzarella stick eat one They good tho,Mozzarella Stick Eating and Reviews
+13.315523,5.0757875,16.17442,3,like-cheese-good-lol,trade Schoop mozzarella stick Doesn even warm Doesn even date good local cheese,Mozzarella Stick Obsession
+12.48091,6.1669173,15.760283,4,like-good-eat-cheese,boyo NOT invent mozzarella stick,Mozzarella Stick Eating and Reviews
+9.596663,9.129283,14.604329,6,stick-face-loudly-faceloudly,ramble YEAH LIKE even support loudly cry face wait time support minute lmfao,Mozzarella Stick Complaints and Experiences
+12.482208,5.7480245,14.435136,4,like-good-eat-cheese,good vegan mozzarella stick yet,Mozzarella Stick Eating and Reviews
+13.798757,6.816889,16.030554,0,cheese-lol-like-time,guess trying mozzarella stick first time,Mozzarella Stick Obsession
+12.366557,6.460415,14.626819,4,like-good-eat-cheese,going manifest biggest mozzarella stick,Mozzarella Stick Eating and Reviews
+10.258071,9.4137535,14.837922,6,stick-face-loudly-faceloudly,ate mozzarella stick ready drink loudly cry face,Mozzarella Stick Complaints and Experiences
+12.276139,6.3942857,14.716327,4,like-good-eat-cheese,son carry around item get attached Sometimes mozzarella stick sometimes little red car sometimes big purple bear Today little blue egg,Mozzarella Stick Eating and Reviews
+11.80491,5.432448,16.681131,3,like-cheese-good-lol,feel gooey like inside mozzarella stick,Mozzarella Stick Obsession
+11.677635,7.9272017,16.38501,2,face-weary-open-craving,French people literally shove whole block cheese make proper mozzarella stick wtfff weary facesmiling face tear,Mozzarella Stick Cravings
+12.566497,5.524954,15.776088,4,like-good-eat-cheese,And ice cream mozzarella stick ranch,Mozzarella Stick Eating and Reviews
+14.495453,6.213545,15.750951,0,cheese-lol-like-time,mozzarella stick person made mozzarella stick stick mozzarella,Mozzarella Stick Obsession
+10.961872,9.351929,14.581344,6,stick-face-loudly-faceloudly,first still time Went open passenger door father car school Bothered resting angry bee chased around car finally stung index finger Blew like mozzarella stick,Mozzarella Stick Complaints and Experiences
+10.988599,4.3414726,14.706048,5,like-look-looked-kinda,Vos And loaf look like mozzarella stick,Mozzarella Stick Comparisons
+13.668172,5.787908,15.3218975,0,cheese-lol-like-time,Dude flip mozzarella stick around air wildly spatula launching perfectly breast pocket,Mozzarella Stick Obsession
+11.666385,5.8033257,15.067404,4,like-good-eat-cheese,Bernico good mozzarella stick make feel warm happy inside friending basket empty bad mozzarella stick get one bite leave basket full This Ska Choose good ska,Mozzarella Stick Eating and Reviews
+11.385306,7.3844028,16.45702,2,face-weary-open-craving,SeX Great And All BUT Have You Ever Had The LaSt Mozzarella Stick winking face,Mozzarella Stick Cravings
+13.816421,5.385023,15.440541,0,cheese-lol-like-time,Also ocean mozzarella stick electrical socket allergy tree many name,Mozzarella Stick Obsession
+12.144129,5.8223867,14.367009,4,like-good-eat-cheese,Idk invented mozzarella stick whoever deserves award sort Like Nobel Prize food CashApp j2thehayes,Mozzarella Stick Eating and Reviews
+12.813699,5.4720626,16.245129,3,like-cheese-good-lol,look silly happily turn arm mozzarella cheese stick,Mozzarella Stick Obsession
+14.324949,7.001719,15.743855,0,cheese-lol-like-time,taking every last mozzarella stick booking room,Mozzarella Stick Obsession
+10.443889,7.2904344,16.97139,2,face-weary-open-craving,Not almost mozzarella stick death pensive face,Mozzarella Stick Cravings
+10.507783,7.5771894,17.39821,2,face-weary-open-craving,You mozzarella stick flushed faceflushed face,Mozzarella Stick Cravings
+11.415047,7.8187485,16.368183,2,face-weary-open-craving,migraine bad post migraine mozzarella stick craving nowhere neighborhood get decent takeout mozzarella stick arguably worse,Mozzarella Stick Cravings
+14.012119,5.7187057,13.862689,4,like-good-eat-cheese,Would like try mozzarella stick,Mozzarella Stick Eating and Reviews
+13.558875,6.7703886,15.115309,0,cheese-lol-like-time,san pls let feed mozzarella stick,Mozzarella Stick Obsession
+12.9840355,6.856848,16.17978,4,like-good-eat-cheese,period stomach either want baby mozzarella stick boyfriend give either,Mozzarella Stick Eating and Reviews
+13.918937,7.040508,15.176536,4,like-good-eat-cheese,mozzarella stick would need mouth,Mozzarella Stick Eating and Reviews
+10.378773,7.5596213,16.453169,2,face-weary-open-craving,arcy mozzarella stick pleading face,Mozzarella Stick Cravings
+9.786673,8.995872,15.407417,6,stick-face-loudly-faceloudly,Keep cry get surgery imitate poc feature stick pasty mozzarella toned skin paper thin lip pls,Mozzarella Stick Complaints and Experiences
+11.622106,5.237876,14.930343,4,like-good-eat-cheese,They player left Little man Blum collective raised enough cover one mozzarella stick table,Mozzarella Stick Eating and Reviews
+12.158679,6.9651527,15.946519,4,like-good-eat-cheese,EAT MOZZARELLA STICKS LOVE,Mozzarella Stick Eating and Reviews
+9.222862,7.102976,15.221968,1,face-joy-stick-joyface,hahahaha ako craving cheese wonton cheese stick mozzarella stick face holding back tear,Mozzarella Stick Joy.
+10.71442,7.887321,16.935947,2,face-weary-open-craving,Craving big mozzarella stick fair weary face,Mozzarella Stick Cravings
+12.076472,5.1086082,16.532736,3,like-cheese-good-lol,give mozzarella stick vibe like feel like ripped open bbokari expecially would cheesy,Mozzarella Stick Obsession
+12.607087,6.4661884,14.075985,4,like-good-eat-cheese,made Mozzarella stick party tonight,Mozzarella Stick Eating and Reviews
+11.831888,5.1279297,14.964163,4,like-good-eat-cheese,great beef cheddar usurper throne almighty behemoth beef mozzarella stick mephistopheles may five buck,Mozzarella Stick Eating and Reviews
+11.804975,4.976054,16.468603,3,like-cheese-good-lol,got cheesy mozzarella stick want taste,Mozzarella Stick Obsession
+11.6595335,5.6028605,16.544712,3,like-cheese-good-lol,emma doe mozzarella stick taste like much,Mozzarella Stick Obsession
+10.981829,4.8258963,15.687721,5,like-look-looked-kinda,bro melty bit best tbh personally make army material outside melty bit inside like mozzarella stick,Mozzarella Stick Comparisons
+13.486906,5.2311206,14.755567,0,cheese-lol-like-time,Canadian tire tire shop And party city party store Lol looking find get mozzarella stick flavor one,Mozzarella Stick Obsession
+14.464574,5.959486,15.554586,0,cheese-lol-like-time,stick mozzarella stick Dustin,Mozzarella Stick Obsession
+13.295784,5.359808,16.159615,3,like-cheese-good-lol,want mozzarella cheese stick bad lol,Mozzarella Stick Obsession
+11.844396,6.438035,15.435332,4,like-good-eat-cheese,mozzarella hmm green glow stick also stick insect Uhhhh 4AM morning like glittery eye makeup,Mozzarella Stick Eating and Reviews
+13.1616125,6.638763,17.194172,4,like-good-eat-cheese,mozzarella stick would never dirty like,Mozzarella Stick Eating and Reviews
+10.53363,8.751508,15.060271,6,stick-face-loudly-faceloudly,mozzarella stick right right would cry,Mozzarella Stick Complaints and Experiences
+14.353954,6.8411827,15.98847,0,cheese-lol-like-time,sacrifice every one mozzarella cheese stick,Mozzarella Stick Obsession
+9.564434,7.485158,15.707627,1,face-joy-stick-joyface,WAOOUGHH MALO THANK MUCH sparkling heartgrowing heartbeating heartsparkling heartsparkling heartheart arrowsparkling heartgrowing heartbeating heart little skrunkly mippy chee,Mozzarella Stick Joy.
+10.122676,8.541841,14.773873,6,stick-face-loudly-faceloudly,ble one indeed forget mozzarella stick thank really need take loudly cry face,Mozzarella Stick Complaints and Experiences
+9.426405,9.109606,14.90424,6,stick-face-loudly-faceloudly,Calling mozzarella stick girthy also WILDloudly cry faceloudly cry faceloudly cry faceloudly cry face,Mozzarella Stick Complaints and Experiences
+12.640336,7.8802257,16.14846,4,like-good-eat-cheese,Not okay ate every mozzarella stick house kinda want,Mozzarella Stick Eating and Reviews
+12.173888,6.755096,16.399166,4,like-good-eat-cheese,way single mozzarella stick cals like talking breadstick talking inch mozzarella stick hate life,Mozzarella Stick Eating and Reviews
+12.247188,5.679565,14.711033,4,like-good-eat-cheese,Raisin mozzarella stick hero,Mozzarella Stick Eating and Reviews
+12.450979,6.0280423,15.019995,4,like-good-eat-cheese,raisin need hear mozzarella stick story tbh,Mozzarella Stick Eating and Reviews
+13.773242,5.708882,14.909243,0,cheese-lol-like-time,mozzarella stick one hahaha although bag chip probably true try sometime,Mozzarella Stick Obsession
+13.640983,6.905453,15.785211,0,cheese-lol-like-time,kennedy11 Can wrong mozzarella stick ten,Mozzarella Stick Obsession
+12.402956,6.7476206,15.922509,4,like-good-eat-cheese,actually bad something must try point Imagine giant mozzarella stick make stop heart,Mozzarella Stick Eating and Reviews
+10.319719,7.2397714,16.542517,2,face-weary-open-craving,name mozzarella stick nerd face,Mozzarella Stick Cravings
+14.525049,5.9720373,15.555993,0,cheese-lol-like-time,TEQUE MAMI Venezuelan teque mozzarella stick nonsense use mozzarella filling starter,Mozzarella Stick Obsession
+12.160584,5.8146515,14.45351,4,like-good-eat-cheese,sheetz mozzarella stick girlie,Mozzarella Stick Eating and Reviews
+12.864368,5.85315,14.409214,4,like-good-eat-cheese,palmed thickest girthy mozzarella stick life,Mozzarella Stick Eating and Reviews
+12.99371,6.4378796,16.990225,4,like-good-eat-cheese,anyways hate mozzarella form stick,Mozzarella Stick Eating and Reviews
+12.439054,6.5595794,16.991388,4,like-good-eat-cheese,Plenty like eating mozzarella stick gone Cole kind string cheese,Mozzarella Stick Eating and Reviews
+12.5297365,6.3336797,14.759698,4,like-good-eat-cheese,version dipping mozzarella stick salford quay,Mozzarella Stick Eating and Reviews
+14.548213,6.1630244,15.485773,0,cheese-lol-like-time,Trying Mozzarella Stick,Mozzarella Stick Obsession
+12.455689,7.5647464,16.259876,4,like-good-eat-cheese,Sense know cheese stick eating mozzarella rest life,Mozzarella Stick Eating and Reviews
+13.00897,7.1666894,15.895902,4,like-good-eat-cheese,Idk feel like mozzarella stick would fix life,Mozzarella Stick Eating and Reviews
+10.822873,7.702444,17.06878,2,face-weary-open-craving,throw mozzarella stick face,Mozzarella Stick Cravings
+12.087104,4.5992417,14.956369,4,like-good-eat-cheese,professional mozzarella stick master Lol But really odd best mozzarella stock pinched fingerscheese wedge Thank brave enough hold end trust hit string cheesecat tear joyface b,Mozzarella Stick Eating and Reviews
+12.552586,7.0604796,17.061779,4,like-good-eat-cheese,eating yunho mozzarella stick,Mozzarella Stick Eating and Reviews
+12.186834,6.681551,16.730938,4,like-good-eat-cheese,say mozzarella stick box flip halfway actually lie,Mozzarella Stick Eating and Reviews
+12.606911,6.810149,15.101587,4,like-good-eat-cheese,Omg mozzarella stick age time,Mozzarella Stick Eating and Reviews
+12.154149,7.111489,16.046762,4,like-good-eat-cheese,eat mozzarella stick without thinking best friend Like two week passed tried swoop one last one hungry denied wishing gave one like woulda saved Lol,Mozzarella Stick Eating and Reviews
+12.374387,5.957509,14.100725,4,like-good-eat-cheese,generic modern would mozzarella stick man,Mozzarella Stick Eating and Reviews
+13.683487,6.4019103,15.153826,0,cheese-lol-like-time,ask hit pen batter taped mozzarella stick lol,Mozzarella Stick Obsession
+13.802304,5.742704,14.998858,0,cheese-lol-like-time,pretty good job fair enough gotten chocolate flavor taking mozzarella stick dipping cup stick,Mozzarella Stick Obsession
+9.861013,9.601784,14.787557,6,stick-face-loudly-faceloudly,good actually loudly cry face like big mozzarella stick juss dont like hotdog,Mozzarella Stick Complaints and Experiences
+9.654525,9.201818,15.040429,6,stick-face-loudly-faceloudly,brought mozzarella stick roux 61loudly cry faceloudly cry faceloudly cry face,Mozzarella Stick Complaints and Experiences
+8.937693,7.4761057,14.958385,1,face-joy-stick-joyface,This true like mozzarella stick baby dip sauce monster face tear joy,Mozzarella Stick Joy.
+10.426111,8.831153,15.237028,6,stick-face-loudly-faceloudly,mozzarella stick choke hold yeah def much wtfloudly cry face mozz stick,Mozzarella Stick Complaints and Experiences
+13.162771,7.127566,16.163961,4,like-good-eat-cheese,would love take bite mozzarella stick sad,Mozzarella Stick Eating and Reviews
+12.101608,4.6433067,15.071242,4,like-good-eat-cheese,Extra cool look like enjoying mozzarella stick really good pull,Mozzarella Stick Eating and Reviews
+9.291978,7.115679,14.837192,1,face-joy-stick-joyface,Not mozzarella stick face tear joy,Mozzarella Stick Joy.
+12.758793,5.782998,14.418477,4,like-good-eat-cheese,lady trled mozzarella stick rainforest cafe,Mozzarella Stick Eating and Reviews
+13.893956,5.664241,15.361303,0,cheese-lol-like-time,ima make mozzarella stick lol,Mozzarella Stick Obsession
+12.465482,5.7452426,14.943568,4,like-good-eat-cheese,Garza Hold consider mozzarella stick connoisseur You better lying,Mozzarella Stick Eating and Reviews
+9.219744,7.330869,15.119167,1,face-joy-stick-joyface,want boneless wing mozzarella stick applebees face exhalingface tear joy,Mozzarella Stick Joy.
+13.698469,5.768428,15.656414,0,cheese-lol-like-time,gin Mozzarella chaos cheese stick,Mozzarella Stick Obsession
+12.334047,5.9883776,15.123883,4,like-good-eat-cheese,horizon Cheddar mozzarella going start buy non grated thing grated make stick together might see pepper jack nibble,Mozzarella Stick Eating and Reviews
+13.572754,7.2904897,15.261465,4,like-good-eat-cheese,god need mozzarella stick mouth RIGHT NOW,Mozzarella Stick Eating and Reviews
+12.449681,7.8293886,16.04825,4,like-good-eat-cheese,life get bad think eating mozzarella stick nice cheese pull make life lil bearable,Mozzarella Stick Eating and Reviews
+12.046389,6.139316,15.448545,4,like-good-eat-cheese,mozzarella stick HEY THERE LORENA SaveWarriorNun WarriorNun,Mozzarella Stick Eating and Reviews
+10.352041,7.3362255,16.073383,2,face-weary-open-craving,Need HER mozzarella Stick drooling facesweat dropletsblack heart,Mozzarella Stick Cravings
+11.61818,5.735249,14.234834,4,like-good-eat-cheese,love mozzarella stick meal mozzarella stick nail polish,Mozzarella Stick Eating and Reviews
+10.837106,9.603007,14.854567,6,stick-face-loudly-faceloudly,also choked mozzarella stick broken jaw loudly cry face,Mozzarella Stick Complaints and Experiences
+10.929848,7.9696155,16.747477,2,face-weary-open-craving,currently craving mozzarella stick weary face,Mozzarella Stick Cravings
+12.25737,4.6432595,16.248037,3,like-cheese-good-lol,like mozzarella stick wince need,Mozzarella Stick Obsession
+9.87957,8.691732,15.055147,6,stick-face-loudly-faceloudly,WANT MOZZARELLA STICK NOWloudly cry face,Mozzarella Stick Complaints and Experiences
+12.1595955,4.487548,16.309948,3,like-cheese-good-lol,Like wing mozzarella stick summ,Mozzarella Stick Obsession
+11.442499,5.276146,15.172554,4,like-good-eat-cheese,Leslie next time town take Nashua watery beer mozzarella stick leda leave guy behind stick finger oversized ball somewhere else,Mozzarella Stick Eating and Reviews
+12.075351,5.728996,15.613926,4,like-good-eat-cheese,omg mozz cheese stick earlier good love mozzarella cheese,Mozzarella Stick Eating and Reviews
+12.162448,5.973302,14.444498,4,like-good-eat-cheese,mozzarella stick sandwich crazy croquetas,Mozzarella Stick Eating and Reviews
+12.394671,4.799203,15.219257,4,like-good-eat-cheese,Tfw big dummy guess one mozzarella stick,Mozzarella Stick Eating and Reviews
+14.951647,6.5027947,15.944341,0,cheese-lol-like-time,applying mozzarella stick self love mask first attempting help anyone else,Mozzarella Stick Obsession
+12.75596,6.650554,15.016608,4,like-good-eat-cheese,waiter What hallucinogen put drink get reality drooling looking mozzarella stick stuck hand ItZ BeAuDaFul,Mozzarella Stick Eating and Reviews
+10.635011,7.5637846,17.101437,2,face-weary-open-craving,stick mozzarella sticksanxious face sweat,Mozzarella Stick Cravings
+12.444845,5.377622,14.592596,4,like-good-eat-cheese,This magnificent mozzarella stick,Mozzarella Stick Eating and Reviews
+12.270698,7.3240576,17.09211,4,like-good-eat-cheese,cant tell stomach hurt cause eat breakfast ate rangoon mozzarella stick two morning,Mozzarella Stick Eating and Reviews
+8.964405,6.851283,15.043466,1,face-joy-stick-joyface,Tried use mozzarella cheese cheese stick face tear joy All cheese tried escape wrapper face tear joy Anyway merienda human purple heart,Mozzarella Stick Joy.
+12.706529,6.046629,14.844508,4,like-good-eat-cheese,Now definitely mozzarella stick,Mozzarella Stick Eating and Reviews
+12.48665,6.4147363,14.151549,4,like-good-eat-cheese,back New York mozzarella stick,Mozzarella Stick Eating and Reviews
+12.738299,6.417835,16.95165,4,like-good-eat-cheese,wait rin never mozzarella stick,Mozzarella Stick Eating and Reviews
+8.961543,7.161524,14.444515,1,face-joy-stick-joyface,still going get 5pc mozzarella stick White Castle face tear joyface tear joy,Mozzarella Stick Joy.
+14.025688,6.5399036,15.789478,0,cheese-lol-like-time,w33b showing mozzarella stick dude cmon one begin,Mozzarella Stick Obsession
+14.658792,6.757222,15.562706,0,cheese-lol-like-time,bastard Mozzarella stick work,Mozzarella Stick Obsession
+8.5136795,7.247459,14.658208,1,face-joy-stick-joyface,Fraudulence This boy look like burnt mozzarella stick lmao face tear joyface tear joy,Mozzarella Stick Joy.
+12.107091,6.7624836,16.127525,4,like-good-eat-cheese,You wake whole family turn mozzarella stick What,Mozzarella Stick Eating and Reviews
+13.192052,5.198625,14.542953,0,cheese-lol-like-time,parmesan What mozzarella stick,Mozzarella Stick Obsession
+10.14711,8.951341,14.744168,6,stick-face-loudly-faceloudly,mozzarella stick idea blood opened either thinking facethinking faceloudly cry face,Mozzarella Stick Complaints and Experiences
+11.6651325,7.921873,17.25915,2,face-weary-open-craving,biting nose like mozzarella stick,Mozzarella Stick Cravings
+10.25895,8.786031,14.6586685,6,stick-face-loudly-faceloudly,pull white castle 3am mone yeah lem get piece mozzarella stick loudly cry face,Mozzarella Stick Complaints and Experiences
+10.332988,7.5954256,17.041159,2,face-weary-open-craving,mozzarella stick synonym something elseface raised eyebrow,Mozzarella Stick Cravings
+13.912585,5.8091917,15.132675,0,cheese-lol-like-time,Cookie butter Speculoos spread Mozzarella Stick,Mozzarella Stick Obsession
+12.672008,6.761893,15.291948,4,like-good-eat-cheese,grandmother mozzarella stick first time lol,Mozzarella Stick Eating and Reviews
+9.003716,7.3544064,14.614384,1,face-joy-stick-joyface,Look like eating mozzarella stickface tear joy,Mozzarella Stick Joy.
+11.660084,4.939891,15.063326,4,like-good-eat-cheese,Niiiiice face savoring food actual piece mozzarella cheesy bread stick type,Mozzarella Stick Eating and Reviews
+8.804657,7.2457795,14.575678,1,face-joy-stick-joyface,Did guy talk last item tray like last mozzarella stick may need put marriage lineface tear joyface tear joy,Mozzarella Stick Joy.
+11.1320505,4.587631,15.643378,5,like-look-looked-kinda,Nah mate Looking like mozzarella stick thing You though,Mozzarella Stick Comparisons
+14.086767,5.559744,15.406055,0,cheese-lol-like-time,Super low effort supper Crockpot Lasagna Bought ragu deli layered pasta ricotta Mozzarella Then stick high let thing hour,Mozzarella Stick Obsession
+13.025417,5.978731,15.678204,4,like-good-eat-cheese,Call mozzarella way stick real one,Mozzarella Stick Eating and Reviews
+12.213544,5.290451,16.24133,3,like-cheese-good-lol,Isn mozzarella stick lol,Mozzarella Stick Obsession
+13.123611,5.588086,15.031539,0,cheese-lol-like-time,Woah Panko Italian mozzarella cheese stick Twix want,Mozzarella Stick Obsession
+12.348201,6.021973,14.897433,4,like-good-eat-cheese,Brother also mozzarella stick,Mozzarella Stick Eating and Reviews
+10.95985,6.154604,15.079735,4,like-good-eat-cheese,nice tease small grin Thank goodness starving She bite mozzarella stick,Mozzarella Stick Eating and Reviews
+10.41712,7.8135176,16.91633,2,face-weary-open-craving,wash mozzarella stick smiling face,Mozzarella Stick Cravings
+13.733223,5.8727937,15.078589,0,cheese-lol-like-time,get slide calling mozzarella stick PUT RESPECT THE NAME,Mozzarella Stick Obsession
+8.603155,7.4159083,14.568367,1,face-joy-stick-joyface,aviel Might thought seeing mozzarella stick tweet drawing Laurent thinking commenting art saying mozzarella stick face tear joyface tear joy,Mozzarella Stick Joy.
+11.515983,5.4906015,15.089205,4,like-good-eat-cheese,Laurent also floppy mozzarella stick comment,Mozzarella Stick Eating and Reviews
+10.734388,4.223845,15.087711,5,like-look-looked-kinda,Sweetie mozzarella cheese stick look like delish,Mozzarella Stick Comparisons
+10.2033825,7.738333,17.09928,2,face-weary-open-craving,This mozzarella stick unamused face,Mozzarella Stick Cravings
+13.581182,5.53076,15.479225,0,cheese-lol-like-time,This Mozzarella stick extra step,Mozzarella Stick Obsession
+14.068957,6.614673,15.936003,0,cheese-lol-like-time,next time use dynajump clearly wanted tch lighting end mozzarella stick,Mozzarella Stick Obsession
+12.171384,5.748766,14.04316,4,like-good-eat-cheese,Guess day The latest review local mozzarella stick connoisseur Watch,Mozzarella Stick Eating and Reviews
+10.424558,7.9765134,16.740301,2,face-weary-open-craving,luv mozzarella stick weary face wish mozzarella stick,Mozzarella Stick Cravings
+8.807178,7.0617046,14.865393,1,face-joy-stick-joyface,Feirah ordering slider mozzarella stick face tear joyface tear joy,Mozzarella Stick Joy.
+12.640356,7.6979823,16.40103,4,like-good-eat-cheese,Thats eat one mozzarella stick normally eat,Mozzarella Stick Eating and Reviews
+12.892452,5.283177,16.351574,3,like-cheese-good-lol,wait lay beach cook like mozzarella stick,Mozzarella Stick Obsession
+14.6946125,6.6207876,15.906182,0,cheese-lol-like-time,throwing dish ground restaurant one mozzarella stick breading came completely apart cheese ruined last stick perfect,Mozzarella Stick Obsession
+10.334091,7.383199,15.782007,2,face-weary-open-craving,ready turn someone daughter mozzarella stick smiling face heart,Mozzarella Stick Cravings
+13.142755,6.236983,15.831635,4,like-good-eat-cheese,Nothing beat good mozzarella stick,Mozzarella Stick Eating and Reviews
+13.471671,7.2349324,14.522291,4,like-good-eat-cheese,Tryna survive work day red bull mozzarella prosciutto stick,Mozzarella Stick Eating and Reviews
+13.049555,6.11793,16.355,3,like-cheese-good-lol,made mozzarella stick colored ribbon raise awareness tummy ache,Mozzarella Stick Obsession
+8.952516,7.667238,14.668776,1,face-joy-stick-joyface,love getting nutted feeling like MOZZARELLA STICK face tear joyface tear joyface tear joyface tear joy,Mozzarella Stick Joy.
+8.988493,7.0433345,14.721368,1,face-joy-stick-joyface,You going turn mozzarella stick STGGG face tear joy,Mozzarella Stick Joy.
+12.2038555,4.8290954,16.439537,3,like-cheese-good-lol,yea ive heard scotch egg one reminded mozzarella stick also TYYYY might draw eating huge scotch egg,Mozzarella Stick Obsession
+8.90885,7.181179,15.050017,1,face-joy-stick-joyface,mozzarella stick end face tear joy,Mozzarella Stick Joy.
+10.384393,9.218187,14.505645,6,stick-face-loudly-faceloudly,Still thinking livgrace13 said dream gone threw mozzarella stick one rolled eye loudly cry face pretty sure pulled mozzarella stick purse loudly cry face,Mozzarella Stick Complaints and Experiences
+9.300363,8.749637,14.895863,6,stick-face-loudly-faceloudly,Literally turned fam mozzarella stick barely anythingloudly cry face endgame last personally enjoyable MCU movie,Mozzarella Stick Complaints and Experiences
+11.976377,6.5482516,13.942371,4,like-good-eat-cheese,Every day time twice The new sandwich week outstanding mozzarella stick gyro thanks sharing need,Mozzarella Stick Eating and Reviews
+11.59606,5.5258293,13.7579775,4,like-good-eat-cheese,The tournament sponsored mozzarella stick company,Mozzarella Stick Eating and Reviews
+12.001758,4.9606767,16.64917,3,like-cheese-good-lol,Yeah like like mozzarella stick like child eat,Mozzarella Stick Obsession
+12.576594,6.926502,16.549555,4,like-good-eat-cheese,eat jalape mozzarella stick guy trap taste bad second time see anyone mozza stick may slap ground stomp though wouldnt half surprised picked back ate standard,Mozzarella Stick Eating and Reviews
+12.254204,4.552328,16.177061,3,like-cheese-good-lol,Damn stick hard boiled egg mozzarella cheddar cheese protein calcium zinc well nut,Mozzarella Stick Obsession
+10.196047,9.707287,14.907484,6,stick-face-loudly-faceloudly,anyways went apple bee chicken tender french fry mozzarella stick cup french onion soup stuffed stomach hurt bad loudly cry face,Mozzarella Stick Complaints and Experiences
+10.1657,6.9221673,15.67927,2,face-weary-open-craving,something dude emotional intelligence overall maturity mozzarella stick smiling face heart eye,Mozzarella Stick Cravings
+13.120857,7.262309,15.308863,4,like-good-eat-cheese,year old response mozzarella stick greased teenager,Mozzarella Stick Eating and Reviews
+9.834554,9.19539,14.992889,6,stick-face-loudly-faceloudly,eat mozzarella stick burger cry face,Mozzarella Stick Complaints and Experiences
+11.237361,5.7181544,15.045591,4,like-good-eat-cheese,mozzarella stick beloved,Mozzarella Stick Eating and Reviews
+12.483436,6.5347977,14.52645,4,like-good-eat-cheese,Never understood mozzarella stick mozz little flavor Gim gouda stick day,Mozzarella Stick Eating and Reviews
+12.19541,5.0565414,14.652383,4,like-good-eat-cheese,mozzarella stick You could always tell father snuck dog cheese hellish gas,Mozzarella Stick Eating and Reviews
+13.150371,7.032898,14.788308,4,like-good-eat-cheese,waiting food lunch CAN THEY HURRY JUST WANT MOZZARELLA STICK,Mozzarella Stick Eating and Reviews
+10.536975,7.8366966,16.549297,2,face-weary-open-craving,die bsg like extra Mozzarella Stick price winking face,Mozzarella Stick Cravings
+10.683935,7.938363,16.82285,2,face-weary-open-craving,turn mozzarella stick face rolling eye,Mozzarella Stick Cravings
+12.292014,6.390025,15.991418,4,like-good-eat-cheese,whenever see double mozzarella stick mean good luck,Mozzarella Stick Eating and Reviews
+14.637904,6.5728464,15.8200245,0,cheese-lol-like-time,tried threw mozzarella stick want fired,Mozzarella Stick Obsession
+12.563713,7.505987,16.2065,4,like-good-eat-cheese,eat bone food cannibalistic choked mozzarella stick went veggie hummus,Mozzarella Stick Eating and Reviews
+11.736055,6.049067,14.016987,4,like-good-eat-cheese,Sry congrats btw Mozzarella stick let,Mozzarella Stick Eating and Reviews
+13.705942,6.0297594,15.216992,0,cheese-lol-like-time,ZohVT Where mozzarella Where stick,Mozzarella Stick Obsession
+13.506435,6.4301195,15.004444,0,cheese-lol-like-time,feed Stick MOZZARELLA plz,Mozzarella Stick Obsession
+13.645197,6.116503,15.692063,0,cheese-lol-like-time,Just Had first ever mozzarella cheese stick,Mozzarella Stick Obsession
+12.012227,5.632316,14.181362,4,like-good-eat-cheese,Philly mozzarella stick soft Knicks six,Mozzarella Stick Eating and Reviews
+8.653905,7.5235868,14.671493,1,face-joy-stick-joyface,naptime face tear joyface tear joyskull mean think look like mozzarella stick got wondering thinking face,Mozzarella Stick Joy.
+9.745369,9.312867,15.043055,6,stick-face-loudly-faceloudly,WHY DOES THE CHEESE THIS VEGAN MOZZARELLA STICK BUST LOAD MOUTH loudly cry faceloudly cry faceloudly cry faceloudly cry face,Mozzarella Stick Complaints and Experiences
+10.493829,9.004674,15.008483,6,stick-face-loudly-faceloudly,thts crazy ordered burger salad mozzarella stick dessert got donut ona stickloudly cry face,Mozzarella Stick Complaints and Experiences
+12.977053,4.8413754,16.227425,3,like-cheese-good-lol,Definitely mean like good wing sometimes back got good mozzarella stick pizza combo loving,Mozzarella Stick Obsession
+12.493502,5.3610597,16.350883,3,like-cheese-good-lol,That emoji anything super picky mozzarella stick quality They give digestive issue anymore though,Mozzarella Stick Obsession
+13.644636,7.0254283,15.34921,4,like-good-eat-cheese,His breath already growing little heavier lower lip tingling lightly Could always another round One hand let grab another mozzarella stick box holding taking one end teeth,Mozzarella Stick Eating and Reviews
+8.580833,7.341611,14.480398,1,face-joy-stick-joyface,cry face tear joy Not dipping Rose sauce like Mozzarella Stick rolling floor laughingface tear joywoman facepalming light skin tone,Mozzarella Stick Joy.
+13.553535,7.8983088,15.1452465,4,like-good-eat-cheese,Think like get lot hate outsider absolutely team together even team New Jersey competitive sibling always going try get last mozzarella stick,Mozzarella Stick Eating and Reviews
+12.340203,6.152776,15.488326,4,like-good-eat-cheese,Boyyyy shut mozzarella stick,Mozzarella Stick Eating and Reviews
+10.574167,9.520089,14.944841,6,stick-face-loudly-faceloudly,dynamosa thought big mozzarella stick first grabbing whole fist loudly cry face,Mozzarella Stick Complaints and Experiences
+10.446807,9.181304,14.645874,6,stick-face-loudly-faceloudly,shooky meal come mozzarella stick REAL THEM loudly cry face,Mozzarella Stick Complaints and Experiences
+9.554566,9.03076,14.951479,6,stick-face-loudly-faceloudly,trying find mozzarella stick dog month cant find anymore loudly cry faceloudly cry faceloudly cry face,Mozzarella Stick Complaints and Experiences
+12.944421,5.0209184,15.1738205,0,cheese-lol-like-time,Memories They also mozzarella stick flavor like 2012ish absolute favorite,Mozzarella Stick Obsession
+10.669198,4.514338,15.182764,5,like-look-looked-kinda,wait jeff look like whole mozzarella stick,Mozzarella Stick Comparisons
+10.041274,7.589433,17.131676,2,face-weary-open-craving,mad get mozzarella stick face steam nose wonder whole thing felt short unamused face,Mozzarella Stick Cravings
+13.074642,6.720802,16.867678,4,like-good-eat-cheese,You never outgrow good mozzarella stick,Mozzarella Stick Eating and Reviews
+13.441485,7.1086187,14.859929,4,like-good-eat-cheese,Stream today shorter hand cam also late shopping Please awkward sneak peek hand perceiving mozzarella stick breakfast,Mozzarella Stick Eating and Reviews
+10.057072,9.229401,15.239145,6,stick-face-loudly-faceloudly,one thing share paid 400usd mexico day day didnt get mozzarella stick loudly cry faceloudly cry face supposed give,Mozzarella Stick Complaints and Experiences
+12.911415,6.6786895,15.19651,4,like-good-eat-cheese,Pretty sure son going turn mozzarella stick,Mozzarella Stick Eating and Reviews
+12.796671,6.377837,14.472195,4,like-good-eat-cheese,Oooh know place talking They got mozzarella option wife like giant mozzarella stick stick SOLD,Mozzarella Stick Eating and Reviews
+12.007404,4.9697604,16.48563,3,like-cheese-good-lol,Nut make feel like mozzarella stick,Mozzarella Stick Obsession
+10.795754,7.811504,16.51579,2,face-weary-open-craving,lisalavish Closed guh wanted lil mozzarella stick turned around hungry confused neutral face,Mozzarella Stick Cravings
+11.6895685,7.52853,16.352436,2,face-weary-open-craving,craving mozzarella stick body calling,Mozzarella Stick Cravings
+10.592973,8.17257,16.798573,2,face-weary-open-craving,ouuuuu imagine face card decline mozzarella stick day,Mozzarella Stick Cravings
+11.241714,5.3463726,15.488595,5,like-look-looked-kinda,None whatever Love good mozzarella stick face savoring food,Mozzarella Stick Comparisons
+10.384354,7.1827126,15.781347,2,face-weary-open-craving,girl precious really one mozzarella stick woozy face,Mozzarella Stick Cravings
+12.631438,6.542173,16.670122,4,like-good-eat-cheese,How feel eating mozzarella stick white castle,Mozzarella Stick Eating and Reviews
+11.819935,6.614992,15.266574,4,like-good-eat-cheese,You know good night wake mozzarella stick puke shirt Firm TheBoyIsBack Rippers,Mozzarella Stick Eating and Reviews
+9.663152,9.484746,14.66327,6,stick-face-loudly-faceloudly,genuinely tell serious loudly cry face pea pur loudly cry faceloudly cry face Cajun fry WHERE Where seasoning time That long mozzarella stick loudly cry face,Mozzarella Stick Complaints and Experiences
+11.262917,8.037045,17.250238,2,face-weary-open-craving,Higgins71 That mozzarella stick analogy entire dating past weary face,Mozzarella Stick Cravings
+12.244868,5.342949,14.772912,4,like-good-eat-cheese,mozzarella lmao queso blanco slap Stick poutine,Mozzarella Stick Eating and Reviews
+12.767195,6.9718018,17.201992,4,like-good-eat-cheese,confess never enjoyed single mozzarella stick entire life,Mozzarella Stick Eating and Reviews
+10.860457,8.032881,16.583782,2,face-weary-open-craving,feel like mozzarella stick everyone trying strip piece cheese Time face spiral eyesexploding head,Mozzarella Stick Cravings
+12.223232,6.062563,14.2413845,4,like-good-eat-cheese,absolutely honored single mozzarella stick,Mozzarella Stick Eating and Reviews
+11.880843,5.546114,13.954546,4,like-good-eat-cheese,moonlight mozzarella stick eyessweat droplet,Mozzarella Stick Eating and Reviews
+12.49501,7.0377192,16.773245,4,like-good-eat-cheese,strip eat like fried mozzarella stick,Mozzarella Stick Eating and Reviews
+12.818217,5.7930365,14.612365,4,like-good-eat-cheese,This place foot long mozzarella stick heaven,Mozzarella Stick Eating and Reviews
+13.151083,6.837525,16.455378,4,like-good-eat-cheese,Anytime Keep mozzarella stick intake,Mozzarella Stick Eating and Reviews
+14.006521,6.3845787,15.922408,0,cheese-lol-like-time,This appalling And quite frankly take mozzarella derivative stick sun shine,Mozzarella Stick Obsession
+13.105851,4.977145,16.096514,3,like-cheese-good-lol,mozzarella stick cheese bite frim zaxbys,Mozzarella Stick Obsession
+14.197053,7.368646,15.566242,0,cheese-lol-like-time,For record potluck box frozen mozzarella stick wrong,Mozzarella Stick Obsession
+12.522067,7.2132196,15.33188,4,like-good-eat-cheese,may Dietitian But resist good MozzarellaStick The CheesePull everything face tear joy LoveCaregivers CaregiverLife FoodTalk,Mozzarella Stick Eating and Reviews
+8.747621,7.222372,14.90957,1,face-joy-stick-joyface,face tear joyface tear joy Green Mozzarella Stick,Mozzarella Stick Joy.
+13.680771,6.1386876,14.984798,0,cheese-lol-like-time,helpp pls tired con crunch want play persona eat mozzarella stick take long Sun,Mozzarella Stick Obsession
+9.982989,9.043334,14.302875,6,stick-face-loudly-faceloudly,seriously heated pregnant lady How hard stick customer asks Asked plain tender mozzarella stick Received buffalo tender chicken quesadilla instead And ice cream right either l,Mozzarella Stick Complaints and Experiences
+12.244451,6.6772285,14.67621,4,like-good-eat-cheese,Mozzarella stick Red Robin buddy,Mozzarella Stick Eating and Reviews
+13.70779,6.6405096,15.920936,0,cheese-lol-like-time,Unless got mozzarella stick earring putting,Mozzarella Stick Obsession
+12.893258,6.6904426,16.324936,4,like-good-eat-cheese,hazzard god mozzarella stick one accurate,Mozzarella Stick Eating and Reviews
+10.489524,4.650662,15.513246,5,like-look-looked-kinda,Reggie Bullock hair look like mozzarella stick respect grind,Mozzarella Stick Comparisons
+13.5451565,5.875955,14.366509,4,like-good-eat-cheese,Top tier mozzarella stick BIGGS,Mozzarella Stick Eating and Reviews
+13.201709,6.9732027,14.965347,4,like-good-eat-cheese,Nothing like rising mozzarella stick price fire Americans,Mozzarella Stick Eating and Reviews
+11.707454,5.497051,15.216069,4,like-good-eat-cheese,purple tie dye mozzarella stick,Mozzarella Stick Eating and Reviews
+11.926461,5.2143073,14.627658,4,like-good-eat-cheese,Daddy little mozzarella stick,Mozzarella Stick Eating and Reviews
+11.638547,6.030722,14.360427,4,like-good-eat-cheese,really hope bring little mozzarella stick stage,Mozzarella Stick Eating and Reviews
+10.386862,7.6477933,16.987564,2,face-weary-open-craving,Mozzarella stick pizza drooling face,Mozzarella Stick Cravings
+13.381949,7.120983,14.788525,4,like-good-eat-cheese,someone PLEASE get singular mozzarella stick begging,Mozzarella Stick Eating and Reviews
+10.200114,4.4794436,15.743414,5,like-look-looked-kinda,Bruh nose look like mozzarella stick His hair look like washed like five year,Mozzarella Stick Comparisons
+11.650601,5.2260284,16.601025,3,like-cheese-good-lol,taste like mozzarella stick lol,Mozzarella Stick Obsession
+13.43939,7.5640416,15.291478,4,like-good-eat-cheese,girl put ketchup mozzarella stick,Mozzarella Stick Eating and Reviews
+14.311658,6.516852,15.892906,0,cheese-lol-like-time,mozzarella stick would cure,Mozzarella Stick Obsession
+11.582858,5.9537616,14.753846,4,like-good-eat-cheese,love plush much cant wait til restocks get throw mozzarella stick,Mozzarella Stick Eating and Reviews
+12.513564,6.351894,16.031116,4,like-good-eat-cheese,Feeling good mozzarella stick chance,Mozzarella Stick Eating and Reviews
+11.778672,6.6271167,15.329398,4,like-good-eat-cheese,Finn real love Finn hmm someone let last mozzarella stick This kid know true love tender age,Mozzarella Stick Eating and Reviews
+13.248128,6.349033,15.649614,4,like-good-eat-cheese,whatever save last mozzarella stick,Mozzarella Stick Eating and Reviews
+13.520054,6.2717814,14.261152,4,like-good-eat-cheese,Portia Gaysa also live The Mozzarella Stick Factory also open Link bio,Mozzarella Stick Eating and Reviews
+14.385733,7.472102,15.623457,0,cheese-lol-like-time,Woke mozzarella stick stuck back like sticky note friday,Mozzarella Stick Obsession
+8.796121,7.381704,14.697467,1,face-joy-stick-joyface,Omg mozzarella stick face tear joyface tear joyface tear joyface tear joy,Mozzarella Stick Joy.
+13.844307,6.226728,15.834192,0,cheese-lol-like-time,frank Grimes time also like mozzarella stick elaborating,Mozzarella Stick Obsession
+13.465476,6.2947598,16.76694,3,like-cheese-good-lol,stick pizza tomato cheese base would never eat slice bread roast dinner sprinkle grated mozzarella,Mozzarella Stick Obsession
+13.175552,5.2145686,15.053953,0,cheese-lol-like-time,twix mozzarella stick candy world,Mozzarella Stick Obsession
+12.867963,6.4838886,15.213788,4,like-good-eat-cheese,Another night another mozzarella stick,Mozzarella Stick Eating and Reviews
+13.653976,6.54757,16.870838,3,like-cheese-good-lol,Always stick mozzarella,Mozzarella Stick Obsession
+13.6648445,6.563708,16.77452,3,like-cheese-good-lol,zara believe vision detected put mozzarella stick oven,Mozzarella Stick Obsession
+14.467126,7.1167526,15.67925,0,cheese-lol-like-time,taking break rerooting clawdeen mozzarella stick family guy time,Mozzarella Stick Obsession
+8.791022,7.7161727,14.786169,1,face-joy-stick-joyface,Nigga said pause mozzarella stick face tear joyface tear joyloudly cry face,Mozzarella Stick Joy.
+13.028011,5.504163,15.988945,3,like-cheese-good-lol,They Like better version mozzarella stick,Mozzarella Stick Obsession
+10.513289,4.370539,14.757266,5,like-look-looked-kinda,kirby angle look like tempura mozzarella stick little booger baby KyoPets,Mozzarella Stick Comparisons
+13.178394,4.8379674,16.089039,3,like-cheese-good-lol,worker asked type cheese mozzarella stick let weary sigh,Mozzarella Stick Obsession
+12.438312,7.351532,16.86046,4,like-good-eat-cheese,eating single mozzarella stick yippee hate,Mozzarella Stick Eating and Reviews
+8.657716,7.092769,14.70633,1,face-joy-stick-joyface,For sure cake giant mozzarella stick face holding back tearssmiling face heart eyesface tear joy,Mozzarella Stick Joy.
+10.7742405,8.068943,16.852232,2,face-weary-open-craving,Stick making best mozzarella stick planet Leave brand Twitter winking face tongue,Mozzarella Stick Cravings
+12.9188595,6.2809463,15.526796,4,like-good-eat-cheese,guy guy calm mean mozzarella stick feast,Mozzarella Stick Eating and Reviews
+11.14535,8.015334,17.09889,2,face-weary-open-craving,Refrying shakey mozzarella stick bec depressing eating hot stretchy persevering face,Mozzarella Stick Cravings
+10.805969,4.3905134,15.528949,5,like-look-looked-kinda,kinda look like mozzarella cheese stick inside,Mozzarella Stick Comparisons
+10.481115,9.037912,15.093304,6,stick-face-loudly-faceloudly,actually trust never mozzarella stick loudly cry face,Mozzarella Stick Complaints and Experiences
+14.276032,5.61355,15.616524,0,cheese-lol-like-time,Mozzarella stick cattail omg The timing would tricky,Mozzarella Stick Obsession
+12.5591545,6.151658,16.046833,4,like-good-eat-cheese,How feel first mozzarella stick,Mozzarella Stick Eating and Reviews
+12.3638935,6.3110933,15.159676,4,like-good-eat-cheese,goodnight mozzarella stick,Mozzarella Stick Eating and Reviews
+13.619466,5.7043405,15.18713,0,cheese-lol-like-time,making cheap charcuterie board mozzarella stick Lol,Mozzarella Stick Obsession
+13.729335,6.3610315,15.691825,0,cheese-lol-like-time,apart Mozzarella stick LMFAOHDJD,Mozzarella Stick Obsession
+8.630614,6.974078,14.851088,1,face-joy-stick-joyface,actually essentially mozzarella stick face tear joy bunch different dip add art artist palette,Mozzarella Stick Joy.
+12.445085,5.3202515,16.206516,3,like-cheese-good-lol,literally watched YouTube vid Indian woman cooking naan topping mozzarella couple day ago bit surprised tbqh Making naan tawa favorite thing kitchen Such fun thing cook mostly,Mozzarella Stick Obsession
+10.616567,7.64265,17.30593,2,face-weary-open-craving,pizza open face mozzarella stick,Mozzarella Stick Cravings
+12.415389,7.4736276,16.990862,4,like-good-eat-cheese,Eating mozzarella stick pretending sugar glider eating one cheese bug,Mozzarella Stick Eating and Reviews
+9.379441,7.686901,14.992707,1,face-joy-stick-joyface,soft creamy inside said like mozzarella stick pancake face tear joy forgot baking powder salt maybe next time could fluffy inside,Mozzarella Stick Joy.
+12.502786,4.677135,16.139551,3,like-cheese-good-lol,trisha paytas would loved cheese pull got mozzarella stick,Mozzarella Stick Obsession
+13.680493,6.271569,15.960884,0,cheese-lol-like-time,drink milk haha considered making quick mozzarella give body something chew decided stick milk,Mozzarella Stick Obsession
+11.247972,8.0118,16.494175,2,face-weary-open-craving,face open eye hand mouth Never truly great mozzarella stick lmao,Mozzarella Stick Cravings
+12.633391,7.587246,16.75823,4,like-good-eat-cheese,One time bit mozzarella stick hollow saddest thing ever,Mozzarella Stick Eating and Reviews
+10.471718,7.133245,16.545414,2,face-weary-open-craving,MTbaum Alright doubling villain face anti mozzarella stick era,Mozzarella Stick Cravings
+9.281925,7.4497256,15.044702,1,face-joy-stick-joyface,This girl whole mozzarella stick talm bout goin face tear joy,Mozzarella Stick Joy.
+13.413508,6.528356,16.649258,3,like-cheese-good-lol,mozzarella stick dislike dni,Mozzarella Stick Obsession
+8.975651,7.196413,15.029444,1,face-joy-stick-joyface,Count day world boneless mozzarella stick face steam noseface tear joy,Mozzarella Stick Joy.
+13.239909,6.0214467,16.21721,3,like-cheese-good-lol,thought mozzarella stick bru still nice SMOKING INTENSIVES,Mozzarella Stick Obsession
+11.065191,5.7612076,14.81173,4,like-good-eat-cheese,mozzarella stick meatball Hello 10lbs waving hand thanks nyc red heart,Mozzarella Stick Eating and Reviews
+12.631515,5.940291,15.558219,4,like-good-eat-cheese,chef glad deterred homie away mozz The reasoning eat quickly becomes big cheesesteak mozzarella stick mozz like cement cool,Mozzarella Stick Eating and Reviews
+10.554789,7.13359,16.899324,2,face-weary-open-craving,bro pea fry single big mozzarella sticksleepy face,Mozzarella Stick Cravings
+12.800119,7.42697,15.339632,4,like-good-eat-cheese,asked son mozzarella stick gave Then son see eating grab back proceeds shove mouthskull,Mozzarella Stick Eating and Reviews
+11.858918,4.9219384,16.439865,3,like-cheese-good-lol,Noooo like big mozzarella stick,Mozzarella Stick Obsession
+12.0736885,4.771497,15.3872,4,like-good-eat-cheese,guess could say mozzarella stick,Mozzarella Stick Eating and Reviews
+13.478538,6.942923,15.1278105,4,like-good-eat-cheese,seductively stir mozzarella stick,Mozzarella Stick Eating and Reviews
+11.6397705,5.9983473,15.132565,4,like-good-eat-cheese,love bad mozzarella stick,Mozzarella Stick Eating and Reviews
+9.412131,8.4527855,14.750026,6,stick-face-loudly-faceloudly,That look like pancake loudly cry face look like large mozzarella stick double exclamation mark,Mozzarella Stick Complaints and Experiences
+10.639976,4.0793514,15.178422,5,like-look-looked-kinda,thats sausage looked like big mozzarella stick,Mozzarella Stick Comparisons
+13.636207,5.4463086,13.446321,4,like-good-eat-cheese,Buffalo chicken slider 4pc mozzarella stick plz,Mozzarella Stick Eating and Reviews
+10.234789,7.7310805,17.325241,2,face-weary-open-craving,This lil boy ate last mozzarella stickenraged faceenraged faceenraged face,Mozzarella Stick Cravings
+10.98956,7.987486,17.101936,2,face-weary-open-craving,danieIking even mozzarella stick anymore stick frowning face open mouth,Mozzarella Stick Cravings
+9.441584,9.160431,14.839476,6,stick-face-loudly-faceloudly,BRO cheese mozzarella stickloudly cry faceloudly cry face,Mozzarella Stick Complaints and Experiences
+10.090464,8.316571,14.89655,6,stick-face-loudly-faceloudly,mozzarella stick phase right Which would perfectly fine gluten free mozzarella stick like box loudly cry face,Mozzarella Stick Complaints and Experiences
+13.086495,6.1426787,15.552034,4,like-good-eat-cheese,Why Italian Shut eat massive mozzarella stick meatball,Mozzarella Stick Eating and Reviews
+9.452647,7.401082,14.756758,1,face-joy-stick-joyface,There reason everything She might get mozzarella stick face tear joy,Mozzarella Stick Joy.
+12.224837,4.923071,14.980139,4,like-good-eat-cheese,incredibly basic All mozzarella sprinkled sugar Essentially giant mozzarella stick,Mozzarella Stick Eating and Reviews
+12.827364,6.7495894,15.988484,4,like-good-eat-cheese,WQNT SHEETS MOZZARELLA STICK BAD GOD,Mozzarella Stick Eating and Reviews
+9.049482,6.8385034,14.960887,1,face-joy-stick-joyface,made chocolate chip pancake eating mozzarella cheese stick woman facepalming medium skin tone face tear joy,Mozzarella Stick Joy.
+11.410453,5.9930425,14.819437,4,like-good-eat-cheese,Pocky kiss mozzarella stick,Mozzarella Stick Eating and Reviews
+10.796135,4.4436626,15.568192,5,like-look-looked-kinda,doe fozzie look like mozzarella stick,Mozzarella Stick Comparisons
+11.339062,5.160558,15.943446,5,like-look-looked-kinda,A10 wonder mozzarella stick would taste dipped bacon guac drooling facedrooling facedrooling face avocadoamerican football gno MakeItBetter SBLVII,Mozzarella Stick Comparisons
+14.000134,6.8317537,15.823874,0,cheese-lol-like-time,half assed workout rewarded mozzarella stick,Mozzarella Stick Obsession
+13.222546,5.216416,15.022033,0,cheese-lol-like-time,giant mozzarella stick covered potato idk,Mozzarella Stick Obsession
+12.274955,6.8149366,16.952473,4,like-good-eat-cheese,worry abt ima munching like mozzarella stick,Mozzarella Stick Eating and Reviews
+12.971869,5.8897576,16.314196,3,like-cheese-good-lol,literally inside fried mozzarella stick When think gooey goodness make perfect sense superior method consumption,Mozzarella Stick Obsession
+13.775103,5.3464007,13.219884,4,like-good-eat-cheese,Just standing front salad asking mozzarella stick,Mozzarella Stick Eating and Reviews
+12.463179,6.0166416,14.442752,4,like-good-eat-cheese,keeping light mozzarella stick addiction They great lemonade,Mozzarella Stick Eating and Reviews
+13.266507,5.4049726,15.28566,0,cheese-lol-like-time,thing fresh mozzarella stick,Mozzarella Stick Obsession
+10.5940695,8.2454,16.817713,2,face-weary-open-craving,want eat mozzarella stick poutine drooling facedrooling facedrooling face,Mozzarella Stick Cravings
+8.66621,7.1135607,14.798344,1,face-joy-stick-joyface,Looking like Hot Cheetos mozzarella stick face tear joyface tear joyface hand mouththinking face,Mozzarella Stick Joy.
+12.58071,5.9636765,15.343946,4,like-good-eat-cheese,dream night nipple length mozzarella stick,Mozzarella Stick Eating and Reviews
+10.415302,4.6732817,15.177661,5,like-look-looked-kinda,look like bill mitchell mozzarella stick,Mozzarella Stick Comparisons
+10.456549,7.66441,17.20034,2,face-weary-open-craving,Mozzarella Stick helpercheese wedgesmiling face open hand,Mozzarella Stick Cravings
+10.662334,4.0307336,14.860127,5,like-look-looked-kinda,Looks like hot cheeto mozzarella stick,Mozzarella Stick Comparisons
+12.249167,6.0072627,15.657465,4,like-good-eat-cheese,Moser mozzarella stick patty try say word let know feel,Mozzarella Stick Eating and Reviews
+12.723436,6.5071764,15.882168,4,like-good-eat-cheese,son swears eat cheese eat mozzarella stick scrambled egg cheese,Mozzarella Stick Eating and Reviews
+12.8918295,5.699068,16.236288,3,like-cheese-good-lol,mozzarella stick Good though,Mozzarella Stick Obsession
+11.98225,5.877533,14.477103,4,like-good-eat-cheese,sucker nice mozzarella stick,Mozzarella Stick Eating and Reviews
+10.007917,9.142781,14.616231,6,stick-face-loudly-faceloudly,ngl thing ate mozzarella cheese stick thingies salad salad barloudly cry faceloudly cry face,Mozzarella Stick Complaints and Experiences
+11.935227,6.916123,15.972349,4,like-good-eat-cheese,Let eat mozzarella stick Lady style sure,Mozzarella Stick Eating and Reviews
+11.966872,7.0250716,16.311062,4,like-good-eat-cheese,every time think horny like wait horny would rather mozzarella stick time wanted mozzarella stick anyway eating mozzarella stick,Mozzarella Stick Eating and Reviews
+10.634882,4.4009995,15.204072,5,like-look-looked-kinda,This look like frozen mozzarella stick,Mozzarella Stick Comparisons
+13.263092,7.1708384,14.553435,4,like-good-eat-cheese,please need find video borzoi mozzarella stick,Mozzarella Stick Eating and Reviews
+12.651795,5.1580505,16.342194,3,like-cheese-good-lol,basically mozzarella stick tho,Mozzarella Stick Obsession
+13.326184,5.2397,14.799781,0,cheese-lol-like-time,What mozzarella stick Tryna get appetizer,Mozzarella Stick Obsession
+12.489026,4.914447,16.365927,3,like-cheese-good-lol,They mozzarella stick fye,Mozzarella Stick Obsession
+10.404677,9.501126,14.603074,6,stick-face-loudly-faceloudly,kid eat normally loudly cry face always take bite mozzarella stick flip face rolling eye,Mozzarella Stick Complaints and Experiences
+8.736608,7.153863,14.646384,1,face-joy-stick-joyface,wan make someone walking mozzarella stickface tear joy,Mozzarella Stick Joy.
+14.180695,7.2739005,15.536469,0,cheese-lol-like-time,girl leave mozzarella stick dough,Mozzarella Stick Obsession
+12.356445,6.2222714,14.725558,4,like-good-eat-cheese,Mozzarella stick Mozzy,Mozzarella Stick Eating and Reviews
+11.200993,5.874653,15.153349,4,like-good-eat-cheese,love strong word serious would say love mozzarella stick,Mozzarella Stick Eating and Reviews
+12.647421,7.088901,15.427005,4,like-good-eat-cheese,Celibacy easy realize balance doe drink Casamigos fruit juice wear thong everyday top letting randoms turn mozzarella stick,Mozzarella Stick Eating and Reviews
+11.296696,5.0205054,15.815118,5,like-look-looked-kinda,NOT THE MELTED MOZZARELLA STICK BEING DRAWN OVER LMFAO LOVE,Mozzarella Stick Comparisons
+9.577813,8.946986,14.975281,6,stick-face-loudly-faceloudly,recipe requires mini pepperoni mozzarella stickloudly cry face,Mozzarella Stick Complaints and Experiences
+12.835332,6.066951,14.423067,4,like-good-eat-cheese,That buttered chicken mozzarella stick last night must drinking beer immaculate,Mozzarella Stick Eating and Reviews
+10.809172,4.7771745,14.642465,5,like-look-looked-kinda,What mozzarella stick looking thing first pic,Mozzarella Stick Comparisons
+10.141549,9.511338,15.162959,6,stick-face-loudly-faceloudly,Been dairy free week piece mozzarella stick stomach fire loudly cry faceloudly cry faceface holding back tearsface holding back tearsface holding back tear help plz,Mozzarella Stick Complaints and Experiences
+12.296711,5.6853857,15.685136,4,like-good-eat-cheese,haha busting chop pal whim best type mozzarella stick,Mozzarella Stick Eating and Reviews
+12.699079,7.7597156,16.301424,4,like-good-eat-cheese,started period morning Does mean deserve mozzarella stick And yet one Another mother nature cruel punishment,Mozzarella Stick Eating and Reviews
+12.432896,5.510881,15.569157,4,like-good-eat-cheese,BUT DON AROUND CALLING YOU GIRL YOU ARE SPECIFIC GIRL BRITTNEY MOZZARELLA ALSO SPECIFIC THE MOZZARELLA STICK,Mozzarella Stick Eating and Reviews
+12.800833,5.0573306,16.225897,3,like-cheese-good-lol,MAN WHY ITS THE MOZZARELLA STICK,Mozzarella Stick Obsession
+13.330845,5.115695,14.823681,0,cheese-lol-like-time,Not fan removing cheese bite appetizer menu replacement stereotypical mozzarella stick like place,Mozzarella Stick Obsession
+12.64188,6.958032,15.359523,4,like-good-eat-cheese,This Jup friend opened mozzarella stick saw streeeeetch lmao,Mozzarella Stick Eating and Reviews
+13.905457,5.2842813,13.361612,4,like-good-eat-cheese,want Cesar salad mozzarella stick parm,Mozzarella Stick Eating and Reviews
+12.566337,6.3806076,15.8779955,4,like-good-eat-cheese,bar smell like mozzarella stick thank goodnight,Mozzarella Stick Eating and Reviews
+14.184322,5.8460474,15.418263,0,cheese-lol-like-time,Just tried mozzarella stick first time lol,Mozzarella Stick Obsession
+10.091812,7.842477,16.925167,2,face-weary-open-craving,What last mozzarella stick thinking facegrimacing faceneutral face,Mozzarella Stick Cravings
+13.873086,5.8461404,14.1654,4,like-good-eat-cheese,big cup malt beverage Sichuan beef single mozzarella stick,Mozzarella Stick Eating and Reviews
+13.996554,6.561019,15.646594,0,cheese-lol-like-time,thing hit harder good mozzarella stick,Mozzarella Stick Obsession
+8.561945,7.4201245,14.540041,1,face-joy-stick-joyface,face tear joyface tear joyface tear joy dude look like burnt mozzarella stick face tear joyface tear joyface tear joy,Mozzarella Stick Joy.
+9.580184,9.470294,14.41853,6,stick-face-loudly-faceloudly,Nibble nosh loudly cry faceloudly cry face Mozzarella stick solitaire loudly cry faceheart suit,Mozzarella Stick Complaints and Experiences
+10.634911,7.8160233,17.245726,2,face-weary-open-craving,Mozzarella stick stick weary face,Mozzarella Stick Cravings
+12.162072,5.4404125,14.769068,4,like-good-eat-cheese,Sudowudo mozzarella stick kid,Mozzarella Stick Eating and Reviews
+11.017435,4.5951366,15.751215,5,like-look-looked-kinda,Gholdengo look like could peel gold plating reveal tender mozzarella stick underneath,Mozzarella Stick Comparisons
+13.931996,5.4681954,13.634947,4,like-good-eat-cheese,ordered mozzarella stick sandwich funsies Just mozzarella stick bread finish,Mozzarella Stick Eating and Reviews
+10.260558,9.243193,15.175461,6,stick-face-loudly-faceloudly,LMFAO fault sapnap make proper mozzarella stick loudly cry face,Mozzarella Stick Complaints and Experiences
+10.15695,4.702259,15.502274,5,like-look-looked-kinda,She look like mozzarella stick left lunch bag hot day,Mozzarella Stick Comparisons
+13.656685,7.2815633,15.255045,4,like-good-eat-cheese,eats Cheese stick battle Choose weapon choose mozzarella,Mozzarella Stick Eating and Reviews
+12.267933,6.614786,14.629168,4,like-good-eat-cheese,mention full mozzarella stick mozzarella stick wednesday guess,Mozzarella Stick Eating and Reviews
+11.240517,4.9657545,15.676138,5,like-look-looked-kinda,hard imagine mozzarella stick would look like irl,Mozzarella Stick Comparisons
+12.130938,5.0731554,16.495203,3,like-cheese-good-lol,yes definitely like mozzarella stick mozzarella cheese poptarts hungry cyclone,Mozzarella Stick Obsession
+11.920494,5.1075573,14.892597,4,like-good-eat-cheese,Steel17 Jets need gap come hop nothing mozzarella stick throwing ball need number throw Then puzzle completed top football world,Mozzarella Stick Eating and Reviews
+11.093598,4.3948393,15.561805,5,like-look-looked-kinda,natttttttt LMFAO KINDA LOOKS THO BUT ITS JUST MELTED MOZZARELLA STICK,Mozzarella Stick Comparisons
+13.441419,5.444012,14.947504,0,cheese-lol-like-time,Lifestyle What dinner Get cheesy mozzarella stick lasagna dish Get recipe,Mozzarella Stick Obsession
+13.603224,5.7105584,15.453608,0,cheese-lol-like-time,Get cheesy mozzarella stick lasagna dish,Mozzarella Stick Obsession
+12.57655,7.506521,15.480004,4,like-good-eat-cheese,One dream one day enough money Italy eat best expensive mozzarella stick taste bud ever exposed,Mozzarella Stick Eating and Reviews
+13.900985,6.1958575,14.236359,4,like-good-eat-cheese,want piece mozzarella stick Like real crunchy type,Mozzarella Stick Eating and Reviews
+13.014634,5.990296,16.284403,3,like-cheese-good-lol,This new improved food pyramid From Danish Mozzarella Stick Does anyone WANT stay SICK FAT,Mozzarella Stick Obsession
+12.981341,6.466434,15.100999,4,like-good-eat-cheese,anytime Black Mozzarella Stick Not sure feel Kinda cool,Mozzarella Stick Eating and Reviews
+13.721718,6.106058,15.296128,0,cheese-lol-like-time,Human Mozzarella Stick,Mozzarella Stick Obsession
+13.468122,6.64042,15.905713,0,cheese-lol-like-time,Call mozzarella way ride around stick,Mozzarella Stick Obsession
+14.457543,5.7967052,15.469915,0,cheese-lol-like-time,Maybe Clue solution Raspberry Kitchen Mozzarella Stick,Mozzarella Stick Obsession
+11.783989,5.8560643,14.511867,4,like-good-eat-cheese,mozzarella stick Marcie military Marcie know,Mozzarella Stick Eating and Reviews
+12.803842,6.6097856,16.801176,4,like-good-eat-cheese,everytime see mozzarella stick think kai,Mozzarella Stick Eating and Reviews
+9.125401,7.145834,15.299714,1,face-joy-stick-joyface,Cafe Mocha smiling face tear mozzarella stick since,Mozzarella Stick Joy.
+11.657362,6.113938,14.307407,4,like-good-eat-cheese,Congratulations pending mozzarella stick nuptials,Mozzarella Stick Eating and Reviews
+10.471476,4.374413,15.595195,5,like-look-looked-kinda,ngl think hair change since wouldve looked like mozzarella stick,Mozzarella Stick Comparisons
+12.819766,6.417597,16.979471,4,like-good-eat-cheese,stayin stick cuz chasin mozzarella,Mozzarella Stick Eating and Reviews
+11.918229,6.5677114,14.088743,4,like-good-eat-cheese,like thank making first mozzarella stick year possible Delightful glutenfree,Mozzarella Stick Eating and Reviews
+9.384695,7.2561264,15.319682,1,face-joy-stick-joyface,love Mozzarella Cheese Sticks wan Mozzarella Cheese Stick face holding back tear,Mozzarella Stick Joy.
+13.734068,6.112004,15.124437,0,cheese-lol-like-time,First food review year Mozzarella stick big,Mozzarella Stick Obsession
+11.494103,4.7013655,15.6253395,5,like-look-looked-kinda,feel like cheese mortician unzipping lil cheese body bag opening mozzarella stick,Mozzarella Stick Comparisons
+12.199591,4.863118,15.088026,4,like-good-eat-cheese,Cheeto mozzarella stick yes dragrace,Mozzarella Stick Eating and Reviews
+11.898816,5.203333,14.748422,4,like-good-eat-cheese,LOVE MOZZARELLA STICK MUCH DragRace,Mozzarella Stick Eating and Reviews
+12.902207,5.943791,14.473079,4,like-good-eat-cheese,like first meal day single mozzarella stick warm sprite,Mozzarella Stick Eating and Reviews
+12.198398,5.59811,15.714002,4,like-good-eat-cheese,stare shake head love mozzarella Just eat warm like cold cheese stick warm cheese insisted hot mozzarella stick store proceeded eat breading leave cheese And ate sideways mozz,Mozzarella Stick Eating and Reviews
+14.832407,6.6581326,15.432766,0,cheese-lol-like-time,HumbleTeej Fam said give gawk gawk mozzarella stick size forearm wrong,Mozzarella Stick Obsession
+11.754124,6.3019233,14.0536175,4,like-good-eat-cheese,girlies way monthly pancake mozzarella stick dinner date,Mozzarella Stick Eating and Reviews
+10.081195,9.067812,14.6578865,6,stick-face-loudly-faceloudly,Tbh think failure restaurant classify mozzarella stick appetizer Like let order mozzarella stick platter meal loudly cry faceloudly cry faceloudly cry face,Mozzarella Stick Complaints and Experiences
+11.688239,5.5027714,16.6928,3,like-cheese-good-lol,crunchy outside gooey inside like good mozzarella stick,Mozzarella Stick Obsession
+12.199914,6.5440416,14.277082,4,like-good-eat-cheese,mozzarella stick good one back day,Mozzarella Stick Eating and Reviews
+12.687185,7.2915635,16.679552,4,like-good-eat-cheese,What kind monster eats mozzarella stick think yes enough,Mozzarella Stick Eating and Reviews
+12.359677,6.913423,15.90566,4,like-good-eat-cheese,gon shove mozzarella stick local friend,Mozzarella Stick Eating and Reviews
+10.755765,4.5250297,14.797354,5,like-look-looked-kinda,think look like churro think look like mozzarella stick pizza sauce top still eat though KidsBakingChionship,Mozzarella Stick Comparisons
+10.887928,4.2045383,14.951165,5,like-look-looked-kinda,may look like mozzarella stick definitely look like meatball,Mozzarella Stick Comparisons
+12.9122,6.969997,15.189198,4,like-good-eat-cheese,anxious age showing kick flip mozzarella stick,Mozzarella Stick Eating and Reviews
+14.517357,6.9394784,15.803313,0,cheese-lol-like-time,Mozzarella stick unspeakable,Mozzarella Stick Obsession
+13.229318,5.103245,16.298847,3,like-cheese-good-lol,almost like bite sized mozzarella stick better,Mozzarella Stick Obsession
+10.94324,4.229456,14.941517,5,like-look-looked-kinda,looked like fried mozzarella stick,Mozzarella Stick Comparisons
+13.061536,7.1268578,15.967434,4,like-good-eat-cheese,You mozzarella stick money,Mozzarella Stick Eating and Reviews
+10.332752,9.330854,15.395826,6,stick-face-loudly-faceloudly,face exhalingface holding back tear shawty speaking sit eat mozzarella stick knowing trainer gone Major Payne weary face,Mozzarella Stick Complaints and Experiences
+12.465527,5.7149405,14.582392,4,like-good-eat-cheese,First mozzarella stick 2023,Mozzarella Stick Eating and Reviews
+12.606001,6.0344768,14.16743,4,like-good-eat-cheese,vegan mozzarella stick tower good way end year,Mozzarella Stick Eating and Reviews
+12.746616,5.9726176,14.641874,4,like-good-eat-cheese,mozzarella stick per minute pace 2023 probably sustained going try HappyNewYear,Mozzarella Stick Eating and Reviews