more changes

app.py

@@ -1,16 +1,15 @@
 import streamlit as st
-import pandas as pd
 from db import insert_data_if_empty, get_mongo_client
 from chatbot import chatbot_response
 
-# Insert the dataset into MongoDB if not already present.
+# Ensure MongoDB is populated.
 insert_data_if_empty()
-
-# Connect to MongoDB (useful for potential visualizations)
 collection = get_mongo_client()
 
-st.subheader("💬 Chatbot: Analyze MongoDB Entries")
-user_prompt = st.text_area("Ask me something (e.g., 'Provide analysis for the data entry 1 in the dataset'):")
+st.subheader("💬 Chatbot for MongoDB Entry Analysis")
+st.write("Ask me something (e.g., 'Provide analysis for the data entry 1 in the dataset'):")
+
+user_prompt = st.text_area("Your query:")
 
 if st.button("Get AI Response"):
     ai_response, sentiment_label, sentiment_confidence, topic_label, topic_confidence = chatbot_response(user_prompt)

chatbot.py

@@ -38,8 +38,12 @@ def analyze_sentiment(text):
         result = sentiment_pipeline(text)[0]
         label = result['label']
         score = result['score']
-        mapping = {"LABEL_0": "Negative", "LABEL_1": "Neutral", "LABEL_2": "Positive"}
-        return mapping.get(label, "Unknown"), score
+        sentiment_mapping = {
+            "LABEL_0": "Negative",
+            "LABEL_1": "Neutral",
+            "LABEL_2": "Positive"
+        }
+        return sentiment_mapping.get(label, "Unknown"), score
     except Exception as e:
         return f"Error analyzing sentiment: {e}", None
 
@@ -52,38 +56,47 @@ def extract_topic(text):
     except Exception as e:
         return f"Error extracting topic: {e}", None
 
-# Detect queries like "data entry 1" or "entry 3" (case-insensitive)
-def is_entry_query(prompt):
-    pattern = r"(?:data entry|entry)\s*(\d+)"
-    match = re.search(pattern, prompt, re.IGNORECASE)
+# Helper function to parse a data entry index from the user's prompt.
+# It looks for a pattern like "data entry 1" or "entry 1" (case insensitive).
+def parse_entry_index(prompt):
+    match = re.search(r'(?:data\s+entry|entry)\s+(\d+)', prompt, re.IGNORECASE)
     if match:
-        # Convert to index (assuming user numbering starts at 1)
-        index = int(match.group(1)) - 1
-        return True, index
-    return False, None
+        try:
+            # Convert to zero-based index.
+            return int(match.group(1)) - 1
+        except ValueError:
+            return None
+    return None
 
 def chatbot_response(user_prompt):
     if not user_prompt:
         return None, None, None, None, None
+
     try:
-        entry_query, index = is_entry_query(user_prompt)
-        if entry_query:
+        # Check if the prompt contains a specific data entry request.
+        index = parse_entry_index(user_prompt)
+        if index is not None:
+            # Fetch the specified entry from MongoDB.
             entry = get_entry_by_index(index)
             if entry is None:
-                return "❌ No entry found for the requested index.", None, None, None, None
+                return f"❌ No entry found for data entry {index+1}.", None, None, None, None
             entry_text = entry.get("text", "No text available.")
-            # Fixed AI response for entry queries (as per instructions)
-            ai_response_text = "Let's break down this tweet-like MongoDB entry:"
-            # Analyze the entry's text
+            # Construct a simple generative prompt.
+            combined_prompt = f"Let's break down this tweet-like MongoDB entry:\n{entry_text}"
+            # Analyze sentiment and topic on the entry's text.
             sentiment_label, sentiment_confidence = analyze_sentiment(entry_text)
             topic_label, topic_confidence = extract_topic(entry_text)
-            return ai_response_text, sentiment_label, sentiment_confidence, topic_label, topic_confidence
         else:
-            # For non-entry queries, fallback to the generative model as usual.
-            model_gen = genai.GenerativeModel("gemini-1.5-pro")
-            ai_response = model_gen.generate_content(user_prompt)
+            # For any other prompt, use it as is.
+            combined_prompt = user_prompt
             sentiment_label, sentiment_confidence = analyze_sentiment(user_prompt)
             topic_label, topic_confidence = extract_topic(user_prompt)
-            return ai_response.text, sentiment_label, sentiment_confidence, topic_label, topic_confidence
+        
+        # Generate AI response using Gemini with the constructed prompt.
+        model_gen = genai.GenerativeModel("gemini-1.5-pro")
+        ai_response = model_gen.generate_content(combined_prompt)
+
+        # Return the generative response and the separately computed sentiment and category.
+        return ai_response.text, sentiment_label, sentiment_confidence, topic_label, topic_confidence
     except Exception as e:
         return f"❌ Error: {e}", None, None, None, None

db.py

@@ -24,8 +24,9 @@ def insert_data_if_empty():
 
 def get_entry_by_index(index=0):
     collection = get_mongo_client()
-    doc_cursor = collection.find({}, {"_id": 0}).skip(index).limit(1)
-    docs = list(doc_cursor)
+    # Skip the first "index" documents and return the next one.
+    cursor = collection.find({}, {"_id": 0}).skip(index).limit(1)
+    docs = list(cursor)
     if docs:
         return docs[0]
     return None