Refine intent classification and image request handling in AI chat

- Enhanced the `extract_and_update_intent` function to streamline the classification of user requests, allowing for more efficient handling of inquiries related to property listings, images, and addresses.
- Updated the `handle_image_request` function to improve property selection based on user identifiers, ensuring that the AI can accurately respond to image requests with relevant property images.
- Adjusted the `receive_message` function to correctly identify image requests, enhancing the system's responsiveness to user inquiries about images and photos.
- These changes aim to improve user interaction by providing clearer and more accurate responses to property and image requests, ultimately enhancing the overall user experience.

ai_chat.py

@@ -284,7 +284,15 @@ async def extract_and_update_intent(state):
         # Check if user is asking for properties, images, or address (using AI classification)
         classification = state.get("classification")
         print(f"DEBUG - Intent update classification check: '{classification}'")
-        if classification in ["search_listings", "request_images", "request_address"]:
+        
+        # Check if this is a request that should skip preference questions
+        skip_preferences = (
+            classification == "search_listings" or
+            classification.startswith("request_images") or
+            classification == "request_address"
+        )
+        
+        if skip_preferences:
             # User is asking for properties, images, or address - don't interrupt with preference questions
             print(f"DEBUG - User asking for {classification}, skipping preference questions")
             return {"response": None}
@@ -316,7 +324,11 @@ Classify the user's message into exactly one of:
 - request_address  (user wants the address/location of a listing)
 - request_details  (user wants other info like features, floorplan, video)
 - other             (anything else)
-Return only the tag.
+
+If the user is asking for images and mentions a specific property (like "option 1", "the office", "warehouse", etc.), 
+extract the property identifier and return: request_images:IDENTIFIER
+
+Return only the tag (and identifier if applicable).
 Message: {user_message}
 """
     resp = await llm.ainvoke([{"role":"user","content":prompt}])
@@ -333,7 +345,15 @@ async def extract_and_search_properties(state):
     # Only search when the LLM tagged this as a listings request
     classification = state.get("classification")
     print(f"DEBUG - Property search classification check: '{classification}'")
-    if classification not in ["search_listings", "request_images", "request_address"]:
+    
+    # Check if classification matches our search categories
+    is_search_request = (
+        classification == "search_listings" or
+        classification.startswith("request_images") or
+        classification == "request_address"
+    )
+    
+    if not is_search_request:
         print(f"DEBUG - Skipping property search, classification is '{classification}'")
         return {"response": None}
 
@@ -450,36 +470,72 @@ async def handle_image_request(state):
     """
     user_message = state["user_message"].lower()
     props = state.get("properties", [])
+    classification = state.get("classification", "")
     
-    # Check if user is asking for images (more specific triggers)
-    image_triggers = [
-        "show me the images", "send me the images", "show me photos", "send me photos",
-        "show me pictures", "send me pictures", "i want to see", "can i see",
-        "image", "images", "photo", "photos", "picture", "pictures"
-    ]
-    
-    # Check if any image trigger is in the message
-    is_image_request = any(trigger in user_message for trigger in image_triggers)
+    # Check if this is an image request
+    if not classification.startswith("request_images") or not props:
+        return None
     
-    # Also check if user is asking about a specific property's images
-    if not is_image_request and props:
-        property_title = props[0].get("title", "").lower()
-        if property_title and any(word in user_message for word in property_title.split()):
-            is_image_request = any(trigger in user_message for trigger in ["image", "photo", "picture"])
+    # Extract property identifier from classification if present
+    property_identifier = None
+    if ":" in classification:
+        property_identifier = classification.split(":", 1)[1].lower()
+    
+    # Smart property selection based on AI classification
+    selected_property = None
+    
+    if property_identifier:
+        # Method 1: Handle option numbers
+        if "option" in property_identifier:
+            # Extract numeric option
+            import re
+            numbers = re.findall(r'\d+', property_identifier)
+            if numbers:
+                option_num = int(numbers[0])
+                if 1 <= option_num <= len(props):
+                    selected_property = props[option_num - 1]
+        
+        # Method 2: Handle text-based identifiers
+        if not selected_property:
+            best_match_score = 0
+            for prop in props:
+                title = prop.get("title", "").lower()
+                location = prop.get("location", "").lower()
+                city = prop.get("city", "").lower()
+                
+                # Check if identifier matches property keywords
+                score = 0
+                identifier_words = property_identifier.split()
+                
+                for word in identifier_words:
+                    if word in title:
+                        score += 3  # Title matches are most important
+                    if word in location:
+                        score += 2
+                    if word in city:
+                        score += 1
+                    # Check for property type keywords
+                    if word in ["office", "warehouse", "space"] and word in title:
+                        score += 2
+                
+                if score > best_match_score:
+                    best_match_score = score
+                    selected_property = prop
     
-    if not is_image_request or not props:
-        return None
+    # Fallback: use first property if no specific identifier
+    if not selected_property:
+        selected_property = props[0]
     
-    # Get the first property (assuming user is asking about the most recent listing)
-    property_data = props[0]
-    images = property_data.get("images", [])
+    # Get images from selected property
+    images = selected_property.get("images", [])
     
     if not images:
-        return ["Sorry, I don't have any images available for this listing."]
+        property_title = selected_property.get("title", "this listing")
+        return [f"Sorry, I don't have any images available for {property_title}."]
     
     # Prepare image messages
     image_messages = []
-    property_title = property_data.get("title", "This property")
+    property_title = selected_property.get("title", "This property")
     
     # Add a text message first to introduce the images
     image_messages.append(f"Here are the images for {property_title}:")

main.py

@@ -76,7 +76,7 @@ async def receive_message(req: Request):
             classification = ai_result.get("classification", "")
             image_messages = None
             
-            if classification == "request_images" or "image" in user_message.lower() or "photo" in user_message.lower():
+            if classification.startswith("request_images") or "image" in user_message.lower() or "photo" in user_message.lower():
                 image_messages = await handle_image_request(state)
             
             if image_messages: