Delete app.py

app.py

@@ -1,159 +0,0 @@
-import streamlit as st
-from transformers import pipeline
-from transformers import AutoTokenizer
-from transformers import AutoModelForSequenceClassification
-import warnings
-warnings.filterwarnings("ignore")
-import nltk
-nltk.download('all')
-import matplotlib.pyplot as plt
-import helper
-import preprocessor
-from mtranslate import translate
-import pandas as pd
-import os
-from gtts import gTTS
-import base64
-import torch
-import seaborn as sns
-st.sidebar.title("Whatsapp Chat analyzer")
-
-uploaded_file= st.sidebar.file_uploader("Choose a file")
-
-if uploaded_file is not None:
-
-    bytes_data = uploaded_file.getvalue()
-    data=bytes_data.decode("utf-8")
-    df_new= preprocessor.preprocess(data)
-
-    user_list= df_new['users'].unique().tolist()
-    user_list.sort()
-    user_list.insert(0,"Group analysis")
-    selected_user=st.sidebar.selectbox("show analysis wrt",user_list)
-    if st.sidebar.button("Show Analysis"):
-        num_messages,words,num_links=helper.fetch_stats(selected_user,df_new)
-        st.title("Top Statistics")
-        col1,col2,col3=st.columns(3)
-
-        with col1:
-            st.header("Total Messages")
-            st.title(num_messages)
-        with col2:
-            st.header("Total Words")
-            st.title(words)
-        with col3:
-            st.header("Links Shared")
-            st.title(num_links)
-            
-        st.title("Timeline")
-        col1, col2 = st.columns(2)
-
-        with col1:
-            st.header("Monthly ")
-            timeline = helper.monthly_timeline(selected_user, df_new)
-            fig, ax = plt.subplots()
-            ax.plot(timeline['time'], timeline['message'])
-            plt.xticks(rotation='vertical')
-            st.pyplot(fig)
-        with col2:
-            st.title("Daily")
-            daily_timeline = helper.Daily_timeline(selected_user, df_new)
-            fig, ax = plt.subplots()
-            ax.plot(daily_timeline['Date'], daily_timeline['message'], color='black')
-            plt.xticks(rotation='vertical')
-            st.pyplot(fig)
-
-        st.title("Activity Map")
-        col1,col2=st.columns(2)
-
-        with col1:
-            st.header("Most busy day")
-            busy_day=helper.week_activity_map(selected_user, df_new)
-            fig,ax=plt.subplots()
-            ax.bar(busy_day.index,busy_day.values,color=('violet','indigo','blue','green','yellow','orange','red'))
-            plt.xticks(rotation='vertical')
-            st.pyplot(fig)
-        with col2:
-            st.header("Most busy Month")
-            busy_day = helper.month_activity_map(selected_user, df_new)
-            fig, ax = plt.subplots()
-            ax.bar(busy_day.index, busy_day.values,color=('indigo','blue','green','red'))
-            plt.xticks(rotation='vertical')
-            st.pyplot(fig)
-
-        st.title("Weekly Activity HeatMap")
-        Activity_heatmap=helper.activity_heatmap(selected_user,df_new)
-        fig,ax=plt.subplots()
-        ax=sns.heatmap(Activity_heatmap,cmap='RdBu',linewidths=1,linecolor='black')
-        st.pyplot(fig)
-
-        if selected_user == "Group analysis":
-            st.title("Most busy user")
-            x,new_df=helper.most_busy_users(df_new)
-            fig,ax=plt.subplots()
-            col1,col2=st.columns(2)
-
-            with col1:
-                ax.bar(x.index, x.values,color=('blue','red','pink','orange','green'))
-                plt.xticks(rotation='vertical')
-                st.pyplot(fig)
-            with col2:
-                st.dataframe(new_df)
-
-        st.title("Chat Sentiment Analysis")
-        col1, col2, col3 = st.columns(3)
-
-        with col1:
-            st.header("Positive")
-            pos_words = helper.pos_words(selected_user, df_new)
-            st.dataframe(pos_words)
-        with col2:
-            st.header("Negative")
-            neg_words = helper.neg_words(selected_user, df_new)
-            st.dataframe(neg_words)
-        with col3:
-            st.header("Neutral")
-            neu_words = helper.neu_words(selected_user, df_new)
-            st.dataframe(neu_words)
-
-
-        st.title("Word cloud")
-        df_wc = helper.word_cloud(selected_user, df_new)
-        fig, ax = plt.subplots()
-        ax.imshow(df_wc)
-        plt.axis('off')
-        st.pyplot(fig)
-
-        st.title("Most Common Words")
-        most_common_df=helper.most_common_words(selected_user,df_new)
-        fig,ax=plt.subplots()
-        ax.barh(most_common_df[0],most_common_df[1])
-        st.pyplot(fig)
-        st.dataframe(most_common_df.style.set_properties(**{"background-color": "black", "color": "lawngreen"}))
-
-        emoji_df=helper.emoji_helper(selected_user,df_new)
-        st.title("Emoji Analysis")
-        st.dataframe(emoji_df.style.set_properties(**{"background-color": "black", "color": "lawngreen"}))
-
-
-st.title("Sentiment Analysis")
-@st.cache(allow_output_mutation=True)
-def get_model():
-    MODEL = f"cardiffnlp/twitter-roberta-base-sentiment"
-    tokenizer = AutoTokenizer.from_pretrained(MODEL)
-    model = AutoModelForSequenceClassification.from_pretrained(MODEL)
-    return tokenizer,model
-
-
-tokenizer, model = get_model()
-
-user_input = st.text_area('Enter Text to Analyze')
-button = st.button("Analyze")
-
-sent_pipeline = pipeline("sentiment-analysis")
-if user_input and button:
-    test_sample = tokenizer([user_input], padding=True, truncation=True, max_length=512, return_tensors='pt')
-    # test_sample
-    output = model(**test_sample)
-    st.write("Prediction: ", sent_pipeline(user_input))
-    showWarningOnDirectExecution = False