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| import streamlit as st | |
| from collections import Counter | |
| import textrazor | |
| from dotenv import load_dotenv | |
| import os | |
| import pandas as pd | |
| import replicate | |
| import plotly.express as px | |
| load_dotenv() | |
| REPLICATE_API_TOKEN = os.getenv("REPLICATE_API_TOKEN") | |
| text_ = """Alice: Hi there, is this Bob? | |
| # Bob: Yes, speaking. Who's calling? | |
| # Alice: Hey Bob, it's Alice from Acme Inc. We met at the conference last month. | |
| # Bob: Oh, hey Alice! Good to hear from you. How can I help you today? | |
| # Alice: Well, I was just calling because I wanted to touch base with you about that project we discussed at the conference. I was hoping we could set up a meeting to talk about it in more detail. | |
| # Bob: Absolutely, I'd be happy to. When were you thinking? | |
| # Alice: How about next Tuesday at 10 am? | |
| # Bob: That works for me. Where should we meet? | |
| # Alice: We can meet at our office in downtown. Here's the address: 123 Main St. Suite 400. | |
| # Bob: Great. And just to confirm, your mobile number is still (555) 123-4567, right? | |
| # Alice: Yes, that's correct. | |
| # Bob: Perfect. I'll put the meeting in my calendar and send you a confirmation email with all the details. | |
| # Alice: Sounds good, thanks Bob. Looking forward to it!""" | |
| replicate.Client(api_token=REPLICATE_API_TOKEN) | |
| def make_summary(text_): | |
| output = replicate.run( | |
| "replicate/flan-t5-xl:7a216605843d87f5426a10d2cc6940485a232336ed04d655ef86b91e020e9210", | |
| input={"prompt": """Write the summary extract any useful information like name, number, and organization of the following conversation {text_to_summarize} """.format(text_to_summarize = text_)}, | |
| max_length = 500, | |
| temperature = 0.7, | |
| top_p = 0.95, | |
| repetition_penalty = 1, | |
| ) | |
| return " ".join(output) | |
| load_dotenv() | |
| textrazor.api_key = os.getenv("TEXT_RAZOR_API_KEY") | |
| client = textrazor.TextRazor(extractors=["entities", "topics"]) | |
| def make_output(text:str): | |
| response = client.analyze(text=text) | |
| df = pd.DataFrame() | |
| for entity in response.entities(): | |
| output_dict = {"id": "".join(list(entity.id or "None")), | |
| "type": ", ".join(list(entity.dbpedia_types or "None")), | |
| "wiki link": "".join(list(entity.wikipedia_link or "None"))} | |
| df2 = pd.DataFrame.from_records([output_dict]) | |
| # df = pd.concat([df, pd.DataFrame([new_row])], ignore_index=True) | |
| df = pd.concat([df, df2], ignore_index=True) | |
| # print(entity.id) | |
| df1 = df.drop_duplicates() | |
| return(df) | |
| st.title("CultureScout NLP Tool 🤖") | |
| # taking user inputs for context search | |
| st.write("***Enter Text You Need Help With:***") | |
| user_input = st.text_input("Text Here:", "") | |
| if st.button("🔎 Search It!"): | |
| def predict_sentiment(data:str): | |
| ans = make_output(user_input) | |
| return ans | |
| df = predict_sentiment(user_input) | |
| df = df.drop_duplicates() | |
| st.table(df) | |
| id_df = predict_sentiment(user_input)['id'] | |
| id_freq = id_df.value_counts() | |
| most_common_id = id_df.value_counts().index[0] | |
| type_df = predict_sentiment(user_input)['type'] | |
| type_freq = type_df.value_counts() | |
| most_common_type = type_df.value_counts().index[0] | |
| st.bar_chart(id_freq) | |
| st.bar_chart(type_freq) | |
| st.write(f"Most appeared id is {most_common_id}.") | |
| st.write(f"Most appeared type is {most_common_type}.") | |
| st.write(""" | |
| """) | |
| # taking user inputs for summarization | |
| st.write("***Enter Text You Need to Summarize:***") | |
| user_input1 = st.text_area("Text Here:", "") | |
| if st.button("🪄 Summarize"): | |
| def summarize(data:str): | |
| ans1 = make_summary(user_input1) | |
| return ans1 | |
| st.write(summarize(user_input1)) |