changed in UI

chatbot.py

@@ -72,7 +72,6 @@ def is_entry_query(prompt):
     return False, None
 
 # Helper: Detect if the query is a basic dataset question.
-# Examples: "What is the dataset summary?", "Show me the sentiment distribution", etc.
 def is_basic_dataset_question(prompt):
     lower = prompt.lower()
     keywords = ["dataset summary", "total tweets", "sentiment distribution", "overall dataset", "data overview", "data summary"]
@@ -87,7 +86,6 @@ def chatbot_response(user_prompt):
         if is_basic_dataset_question(user_prompt):
             summary = get_dataset_summary()
             ai_response = "Dataset Summary:\n" + summary
-            # Run analysis on the summary text
             sentiment_label, sentiment_confidence = analyze_sentiment(summary)
             topic_label, topic_confidence = extract_topic(summary)
             return ai_response, sentiment_label, sentiment_confidence, topic_label, topic_confidence
@@ -102,18 +100,18 @@ def chatbot_response(user_prompt):
             entry_text = entry.get("text", "No text available.")
             entry_user = entry.get("user", "Unknown")
             entry_date = entry.get("date", "Unknown")
-            # Build a static response message with the required format
+            # Build a static response message with new lines for each field.
             ai_response = (
                 "Let's break down this tweet-like MongoDB entry:\n\n"
-                f"Tweet: {entry_text}\n"
-                f"User: {entry_user}\n"
-                f"Date: {entry_date}"
+                f"**Tweet:** {entry_text}\n\n"
+                f"**User:** {entry_user}\n\n"
+                f"**Date:** {entry_date}"
             )
             sentiment_label, sentiment_confidence = analyze_sentiment(entry_text)
             topic_label, topic_confidence = extract_topic(entry_text)
             return ai_response, sentiment_label, sentiment_confidence, topic_label, topic_confidence
 
-        # For other queries, use the generative model (this branch may be slower).
+        # For other queries, use the generative model.
         model_gen = genai.GenerativeModel("gemini-1.5-pro")
         ai_response_obj = model_gen.generate_content(user_prompt)
         ai_response = ai_response_obj.text