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| import streamlit as st | |
| import pandas as pd | |
| from db import insert_data_if_empty, get_mongo_client | |
| from chatbot import chatbot_response # Import chatbot functionality | |
| # 1. Ensure Data is Inserted Before Display | |
| insert_data_if_empty() | |
| # 2. MongoDB Connection | |
| collection = get_mongo_client() | |
| st.title("📊 MongoDB Data Viewer with AI Sentiment Chatbot") | |
| # --- Historical Data Analytics Section --- | |
| st.subheader("Historical Data Analytics") | |
| data = list(collection.find({}, {"_id": 0})) | |
| if data: | |
| df = pd.DataFrame(data) | |
| # The sentiment140.csv dataset typically contains a column named 'target' | |
| # which holds the sentiment labels (0=Negative, 2=Neutral, 4=Positive) | |
| if 'target' in df.columns: | |
| # Map numeric sentiment labels to readable text | |
| sentiment_map = {0: "Negative", 2: "Neutral", 4: "Positive"} | |
| df['sentiment_label'] = df['target'].map(sentiment_map) | |
| # Compute sentiment distribution | |
| sentiment_counts = df['sentiment_label'].value_counts() | |
| st.write("### Sentiment Distribution in Historical Data") | |
| st.bar_chart(sentiment_counts) | |
| else: | |
| st.warning("The dataset does not contain a 'target' column for sentiment.") | |
| else: | |
| st.warning("No historical data available.") | |
| # Optionally, a button to display the complete historical data | |
| if st.button("Show Complete Historical Data"): | |
| st.write(pd.DataFrame(data)) | |
| # --- Chatbot Section with Sentiment & Topic Analysis --- | |
| st.subheader("💬 Chatbot with Sentiment & Topic Analysis") | |
| user_prompt = st.text_area("Ask me something:") | |
| if st.button("Get AI Response"): | |
| ai_response, sentiment_label, sentiment_confidence, topic_label, topic_confidence = chatbot_response(user_prompt) | |
| if ai_response: | |
| st.write("### AI Response:") | |
| st.write(ai_response) | |
| st.write("### Sentiment Analysis:") | |
| st.write(f"**Sentiment:** {sentiment_label} ({sentiment_confidence:.2f} confidence)") | |
| st.write("### Category Extraction:") | |
| st.write(f"**Detected Category:** {topic_label} ({topic_confidence:.2f} confidence)") | |
| # Additional: Compare with historical data | |
| if data and 'target' in df.columns: | |
| historical_count = df[df['sentiment_label'] == sentiment_label].shape[0] | |
| st.info(f"In our historical dataset, there are {historical_count} tweets with a {sentiment_label} sentiment.") | |
| else: | |
| st.warning("⚠️ Please enter a question or text for analysis.") | |