emojis added

app.py

@@ -9,20 +9,20 @@ insert_data_if_empty()
 # Connect to MongoDB (optional: for additional visualizations)
 collection = get_mongo_client()
 
-st.subheader("💬 Chatbot with Dataset Analysis")
+st.subheader("💬 Chatbot with Sentiment Analysis & Category Extraction")
 # Updated hint to include examples for basic questions and entry queries.
 user_prompt = st.text_area(
     "Ask me something (e.g., 'Provide analysis for data entry 1 in the dataset' or 'What is the dataset summary?'):"
 )
 
-if st.button("Get AI Response"):
+if st.button("Get Response"):
     ai_response, sentiment_label, sentiment_confidence, topic_label, topic_confidence = chatbot_response(user_prompt)
     if ai_response:
         st.write("### Response:")
         st.write(ai_response)
         st.write("### Sentiment Analysis:")
-        st.write(f"**Sentiment:** {sentiment_label} ({sentiment_confidence:.2f} confidence)")
+        st.write(f"**Sentiment Detected:** {sentiment_label} ({sentiment_confidence:.2f} confidence)")
         st.write("### Category Extraction:")
-        st.write(f"**Detected Category:** {topic_label} ({topic_confidence:.2f} confidence)")
+        st.write(f"**Category Detected:** {topic_label} ({topic_confidence:.2f} confidence)")
     else:
         st.warning("⚠️ Please enter a question or text for analysis.")

chatbot.py

@@ -29,8 +29,9 @@ except Exception as e:
 
 # Predefined topic labels for classification
 TOPIC_LABELS = [
-    "Technology", "Politics", "Business", "Sports", "Entertainment",
-    "Health", "Science", "Education", "Finance", "Travel", "Food"
+    "Technology", "Politics", "Business", "Sports", "Entertainment", "Health", "Science",
+    "Education", "Finance", "Travel", "Food", "Environment", "Culture", "History", "Art",
+    "Litreature", "Automotive", "Law", "Music", "Movies"
 ]
 
 def analyze_sentiment(text):
@@ -39,9 +40,9 @@ def analyze_sentiment(text):
         label = result['label']
         score = result['score']
         sentiment_mapping = {
-            "LABEL_0": "Negative",
-            "LABEL_1": "Neutral",
-            "LABEL_2": "Positive"
+            "LABEL_0": "😞 Negative",
+            "LABEL_1": "😐 Neutral",
+            "LABEL_2": "😄 Positive"
         }
         return sentiment_mapping.get(label, "Unknown"), score
     except Exception as e:
@@ -102,7 +103,7 @@ def chatbot_response(user_prompt):
             entry_date = entry.get("date", "Unknown")
             # Build a static response message with new lines for each field.
             ai_response = (
-                "Let's break down this tweet-like MongoDB entry:\n\n"
+                "Let's break down this MongoDB entry:\n\n"
                 f"**Tweet:** {entry_text}\n\n"
                 f"**User:** {entry_user}\n\n"
                 f"**Date:** {entry_date}"