change done in app.py

app.py

@@ -3,48 +3,21 @@ 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
+# 1. Ensure the sentiment140 dataset is inserted into MongoDB
 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 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)
@@ -55,9 +28,22 @@ if st.button("Get AI Response"):
         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.")
+        # 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.")