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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 the sentiment140 dataset is inserted into MongoDB
insert_data_if_empty()
# 2. MongoDB Connection
collection = get_mongo_client()
st.subheader("💬 Chatbot with Sentiment & Topic Analysis")
# User input for chatbot
user_prompt = st.text_area("Ask me something:")
if st.button("Get AI Response"):
# Generate real-time AI response, sentiment, and topic extraction
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)")
# 3. Historical Insight: Compare with historical data from sentiment140.csv
historical_data = list(collection.find({}, {"_id": 0}))
if historical_data:
df = pd.DataFrame(historical_data)
# Assume the CSV has a 'sentiment' column with numeric labels:
# 0: Negative, 2: Neutral, 4: Positive.
if 'sentiment' in df.columns:
sentiment_map = {0: "Negative", 2: "Neutral", 4: "Positive"}
# Ensure the sentiment column is numeric and map it to readable labels
df['sentiment_label'] = df['sentiment'].astype(int).map(sentiment_map)
matching_count = df[df['sentiment_label'] == sentiment_label].shape[0]
st.write("### Historical Insights:")
st.info(f"There are {matching_count} tweets in our dataset with a {sentiment_label} sentiment.")
else:
st.warning("Historical data does not contain a sentiment field.")
else:
st.warning("No historical data available.")
else:
st.warning("⚠️ Please enter a question or text for analysis.")
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