Update app.py

app.py

@@ -370,6 +370,9 @@ def handle_small_talk(query):
     else:
         return "I'm here to help with HDB price predictions and information. How can I assist you today?"
 
+
+
+"""
 def chat_with_llm(query, chat_history):
     """Handle chat queries about HDB pricing and small talk"""
     if not groq_api_key or client is None:
@@ -421,7 +424,90 @@ def chat_with_llm(query, chat_history):
         response = f"I need more information to make a prediction. Please provide: {missing_list}."
         chat_history.append((query, response))
         return response, chat_history
+"""
+#start here
+
+def chat_with_llm(query, chat_history):
+    """Handle chat queries about HDB pricing and small talk"""
+    if not groq_api_key or client is None:
+        return "Please set GROQ_API_KEY...", chat_history
+
+    # 1. First, check for small talk
+    if is_small_talk(query):
+        response = handle_small_talk(query)
+        chat_history.append((query, response))
+        return response, chat_history
+
+    # 2. Check if the query is a clear request for a general explanation/trend (not a specific price)
+    is_general_query = any(keyword in query.lower() for keyword in [
+        'trend', 'overview', 'how are', 'what are', 'like in', 'average', 
+        'over the years', 'market', 'compare'
+    ])
+    
+    # 3. If it's a general query, use the LLM to answer it directly
+    if is_general_query:
+        try:
+            completion = client.chat.completions.create(
+                model="llama-3.3-70b-versatile",
+                messages=[
+                    {
+                        "role": "system", 
+                        "content": "You are a helpful assistant specialized in HDB (Housing & Development Board) properties in Singapore. Provide accurate, helpful information about HDB prices, policies, and market trends. Use the provided context if available."
+                    },
+                    {
+                        "role": "user", 
+                        "content": f"Based on general HDB market knowledge, answer this question: {query}"
+                    }
+                ],
+                temperature=0.3,
+                max_tokens=500
+            )
+            response = completion.choices[0].message.content
+            chat_history.append((query, response))
+            return response, chat_history
+        except Exception as e:
+            error_msg = f"I encountered an error. Please try again later."
+            chat_history.append((query, error_msg))
+            return error_msg, chat_history
+
+    # 4. If it's not clearly general, try to extract parameters for a specific prediction
+    params = extract_parameters_from_query(query)
+
+    if "error" in params:
+        # If extraction failed, fall back to general Q&A
+        return answer_general_hdb_question(query, chat_history)
+
+    # 5. Check what we got back from parameter extraction
+    extracted_params = {k: v for k, v in params.items() if v is not None}
+    required_for_prediction = ['town', 'flat_type', 'floor_area_sqm', 'storey_level', 'flat_age']
+
+    # 6. If the user only provided a town or one other parameter, it's likely a general question.
+    if len(extracted_params) < 3: # e.g., if only 'town' and 'flat_type' are provided
+        # Ask a clarifying question or provide a general overview
+        if 'town' in extracted_params:
+            town = extracted_params['town']
+            # You could add a pre-generated fact here, e.g., average price for that town from the dataset
+            response = f"You asked about {town}. HDB prices can vary widely based on flat type, size, age, and specific location within the town. "
+            response += f"For example, are you interested in 4-Room or 5-Room flats? What's your budget or preferred size? "
+            response += "Alternatively, I can give you a prediction if you provide more details like flat type, size, and age."
+        else:
+            response = "I specialize in HDB price predictions and information. Could you provide more details about the property you're interested in (e.g., town, flat type, size) so I can give you a accurate estimate or information?"
+        chat_history.append((query, response))
+        return response, chat_history
 
+    # 7. If we have most parameters, ask for the missing ones specifically
+    missing_params = [param for param in required_for_prediction if params.get(param) is None]
+    if missing_params:
+        missing_list = ", ".join(missing_params)
+        response = f"I'd be happy to predict a price for you. I just need a few more details: {missing_list}."
+        chat_history.append((query, response))
+        return response, chat_history
+
+    # 8. If we have all parameters, make the prediction!
+    # ... (rest of the prediction code remains the same)
+
+
+    
     # If we have all parameters, make a prediction
     try:
         # Convert string numbers to appropriate types