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| from model_functions import * | |
| from preprocessor import * | |
| import streamlit as st | |
| import pandas as pd | |
| def load_example_file(file): | |
| with open(file, "rb") as f: | |
| return f.read() | |
| def main(): | |
| # Load models | |
| tokenizer_sentiment, model_sentiment = load_sentiment_analyzer() | |
| tokenizer_summary, model_summary = load_summarizer() | |
| pipe_ner = load_NER() | |
| st.markdown(""" | |
| <style> | |
| body {{ | |
| background-color: #e6fedb; | |
| }} | |
| </style> | |
| <h1><img src="logo.png" width="60" height="60"> WhatsApp Analysis Tool</h1> | |
| """, unsafe_allow_html=True) | |
| st.markdown("This app summarizes Whatsapp chats and provides named entity recognition as well as sentiment analysis for the conversation") | |
| st.markdown("**NOTE**: *This app can only receive chats downloaded from IOS as the downloaded chat format is different than from Android.*") | |
| st.markdown("Download your whatsapp chat by going to Settings > Chats > Export Chat and there select the chat you want to summarize (download 'Without Media').") | |
| st.markdown("**Example Files**: Download example zip files to test the app:") | |
| example_files = { | |
| "Example 1": "example1.zip", | |
| "Example 2": "example2.zip", | |
| "Example 3": "example3.zip" | |
| } | |
| for name, file in example_files.items(): | |
| data = load_example_file(file) | |
| st.download_button(label=name, data=data, file_name=file, mime="application/zip") | |
| # File uploader | |
| uploaded_file = st.file_uploader("Choose a file (.zip)", type=['zip']) | |
| if uploaded_file is not None: | |
| file_type = detect_file_type(uploaded_file.name) | |
| if file_type == "zip": | |
| # Process the file | |
| data = preprocess_whatsapp_messages(uploaded_file, file_type) | |
| if data.empty: | |
| st.write("No messages found or the file could not be processed.") | |
| else: | |
| # Date selector | |
| date_options = data['date'].dt.strftime('%Y-%m-%d').unique() | |
| selected_date = st.selectbox("Select a date for analysis:", date_options) | |
| if selected_date: | |
| text_for_analysis = get_dated_input(data, selected_date) | |
| with st.expander("Show/Hide Original Conversation"): | |
| st.markdown(f"```\n{text_for_analysis}\n```", unsafe_allow_html=True) | |
| process = st.button('Process') | |
| if process: | |
| # Perform analysis | |
| sentiment = get_sentiment_analysis(text_for_analysis, tokenizer_sentiment, model_sentiment) | |
| summary = generate_summary(text_for_analysis, tokenizer_summary, model_summary) | |
| ner_results = get_NER(summary, pipe_ner) | |
| # Display results | |
| st.subheader("Sentiment Analysis") | |
| st.write("Sentiment:", sentiment) | |
| st.subheader("Summary") | |
| st.write("Summary:", summary) | |
| st.subheader("Named Entity Recognition") | |
| ner_df = pd.DataFrame(ner_results, columns=["Word", "Entity Group"]) | |
| st.write(ner_df) | |
| else: | |
| st.error("Unsupported file type. Please upload a .txt or .zip file.") | |
| else: | |
| st.info("Please upload a file to proceed.") | |
| if __name__ == "__main__": | |
| main() |