Update main.py

main.py

@@ -1,9 +1,9 @@
 """
-main.py — Pricelyst Shopping Advisor (Jessica Edition 2026 - Upgrade v2.3)
+main.py — Pricelyst Shopping Advisor (Jessica Edition 2026 - Upgrade v2.4)
 
-✅ Fixed: Image Endpoint now auto-resolves prices (Simulated Intent).
-✅ Fixed: Vision SDK Crash (Part.from_text removed).
-✅ Fixed: "Clingy" Memory (Topic Reset Rule added).
+✅ Fixed: Vision Prompt now forces item extraction for Products/Meals.
+✅ Fixed: "Nice Dog" Logic. Irrelevant images get a Persona response, not an error.
+✅ Fixed: "Limbo State". Fallback logic ensures 'Pepsi' is searched even if items=[] initially.
 ✅ "Analyst Engine": Enhanced Basket Math, Category Context, ZESA Logic.
 ✅ "Visual Engine": Lists, Products, & Meal-to-Recipe recognition.
 ✅ Memory Logic: Short-Term Sliding Window (Last 6 messages).
@@ -457,15 +457,17 @@ def gemini_analyze_image(image_b64: str, caption: str = "") -> Dict[str, Any]:
     
     PROMPT = f"""
     Analyze this image. Context: {caption}
-    1. Is it a handwritten/printed SHOPPING LIST? -> Extract items.
-    2. Is it a SINGLE PRODUCT? -> Identify the product name.
-    3. Is it a MEAL/DISH? -> Identify the dish and list key ingredients to buy.
-    4. IRRELEVANT? -> Return type "IRRELEVANT".
+    1. SHOPPING LIST? -> Extract items.
+    2. SINGLE PRODUCT? -> Extract the BRAND and PRODUCT NAME into 'items'. (e.g. "Pepsi 500ml")
+    3. MEAL/DISH? -> Identify the dish and ingredients.
+    4. IRRELEVANT (Pet, Person, Nature)? -> Return type "IRRELEVANT".
+
+    IMPORTANT: If type is 'PRODUCT', the 'items' list MUST contain the product name. Do not leave it empty.
 
     Return STRICT JSON:
     {{
       "type": "LIST" | "PRODUCT" | "MEAL" | "IRRELEVANT",
-      "items": ["item1", "item2"],
+      "items": ["item1"],
       "description": "Short description of what is seen"
     }}
     """
@@ -512,7 +514,7 @@ def gemini_chat_response(transcript: str, intent: Dict, analyst_data: Dict, chat
     - **Casual**: Warm greeting. If they just said "Hi", reply "Makadii! How can I help you save today?". Don't summarize past chats.
     - **Advice**: If data exists, guide them. "I found XYZ at Store A."
     - **Trust**: If user asks "Is this expensive?", check 'category_stats'.
-    - **ZESA**: Explain units clearly.
+    - **Irrelevant Images**: If user sent a photo of a dog/nature, compliment it warmly, then gently mention you specialize in shopping.
     
     TONE: Conversational, Zimbabwean English.
     """
@@ -561,7 +563,7 @@ def health():
         "ok": True,
         "offers_indexed": len(df),
         "api_source": PRICE_API_BASE,
-        "persona": "Jessica v2.3"
+        "persona": "Jessica v2.4"
     })
 
 @app.post("/chat")
@@ -646,34 +648,39 @@ def analyze_image():
     vision_result = gemini_analyze_image(image_b64, caption)
     img_type = vision_result.get("type", "IRRELEVANT")
     items = vision_result.get("items", [])
-    description = vision_result.get("description", "item")
+    description = vision_result.get("description", "an image")
     
+    # Fallback: If type is PRODUCT/MEAL but items is empty, try to use description as search item
+    # This catches the "Pepsi bottle" case where items was []
+    if (img_type in ["PRODUCT", "MEAL"]) and not items and description:
+        # Simple heuristic: treat the description as the item for fuzzy search
+        # It won't be perfect, but it prevents the "silent failure"
+        items = [description]
+        logger.info(f"🔮 Fallback: Used description '{description}' as item.")
+
     response_text = ""
     analyst_data = {}
     
     # 2. Logic Branching
-    if img_type == "IRRELEVANT":
-        response_text = "I see the image, but I can't find any shopping items or meals in it. Try a receipt, a product, or a plate of food!"
+    if img_type == "IRRELEVANT" and not items:
+        # Graceful Rejection / Compliment
+        prompt = f"User uploaded a photo of: {description}. If it is a pet/flower/view, compliment it warmly! Then effectively explain you are a shopping bot and can't price check that."
+        response_text = gemini_chat_response(prompt, {"intent": "CASUAL_CHAT"}, {}, "")
         
     elif items:
         # Run the Analyst Engine
         analyst_data = calculate_basket_optimization(items)
         
         # 3. DYNAMIC SIMULATED INTENT
-        # We vary the "user voice" so Jessica's reply matches the context.
-        
         if img_type == "MEAL":
-            # Context: "I want to cook this"
-            simulated_user_msg = f"I want to cook {description}. I need these ingredients: {', '.join(items)}. How much will it cost to make this?"
-            intent_sim = {"intent": "SHOPPING_BASKET"} # Triggers advice mode
+            simulated_user_msg = f"I want to cook {description}. I need these ingredients: {', '.join(items)}. How much will it cost?"
+            intent_sim = {"intent": "SHOPPING_BASKET"}
             
         elif img_type == "LIST":
-            # Context: "Quote this list"
             simulated_user_msg = f"Here is my shopping list: {', '.join(items)}. Which store is cheapest for the whole basket?"
             intent_sim = {"intent": "STORE_DECISION"}
             
         else: # PRODUCT
-            # Context: "Price check"
             simulated_user_msg = f"I see {description}. Which store has the cheapest price for it right now?"
             intent_sim = {"intent": "STORE_DECISION"}
         
@@ -685,6 +692,10 @@ def analyze_image():
             chat_history="" # Fresh context
         )
     
+    else:
+        # Catch-all if something really weird happens (Product type but no description/items)
+        response_text = "I couldn't quite identify the product in that image. Could you type the name for me?"
+
     return jsonify({
         "ok": True,
         "image_type": img_type,