Update main.py

main.py

@@ -1,9 +1,9 @@
 """
-main.py — Pricelyst Shopping Advisor (Jessica Edition 2026 - Upgrade v2.4)
+main.py — Pricelyst Shopping Advisor (Jessica Edition 2026 - Upgrade v2.5)
 
-✅ 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.
+✅ Fixed: "Basket Regression" - AI now returns prices IMMEDIATELY.
+✅ Fixed: "Bluffing" - AI explicitly states if item is found or missing.
+✅ Optimization: Removed "Add to list" chatter. Shortest path to value.
 ✅ "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).
@@ -303,6 +303,8 @@ def calculate_basket_optimization(item_names: List[str]) -> Dict[str, Any]:
             "product_id": int(best_prod['product_id']),
             "name": str(best_prod['product_name']),
             "category": str(best_prod['category']),
+            "retailer": str(best_prod['retailer']), # Added explicitly for prompt access
+            "price": float(best_prod['price']),       # Added explicitly for prompt access
             "category_stats": cat_stats
         })
 
@@ -422,23 +424,21 @@ def gemini_detect_intent(transcript: str) -> Dict[str, Any]:
     Analyze transcript. Return STRICT JSON.
     Classify intent:
     - CASUAL_CHAT: Greetings, small talk, "hi", "thanks".
-    - SHOPPING_BASKET: Looking for prices, creating a list.
+    - SHOPPING_BASKET: Looking for prices, products, lists, or "cheapest X".
     - UTILITY_CALC: Electricity/ZESA questions.
     - STORE_DECISION: "Where should I buy?", "Which store is cheapest?".
     - TRUST_CHECK: "Is this expensive?", "Is this a good deal?".
     
     Extract:
-    - items: list of products
+    - items: list of products found in the text.
     - utility_amount: number
-    - context_tag: "budget", "speed", "quality"
     
     JSON Schema:
     {
       "actionable": boolean,
       "intent": "string",
       "items": ["string"],
-      "utility_amount": number,
-      "context_tag": "string"
+      "utility_amount": number
     }
     """
     try:
@@ -495,28 +495,34 @@ def gemini_chat_response(transcript: str, intent: Dict, analyst_data: Dict, chat
     context_str += f"ZIMBABWE CONTEXT: Fuel={ZIM_CONTEXT['fuel_petrol']}, ZESA Rate={ZIM_CONTEXT['zesa_step_1']['rate']}\n"
     
     if analyst_data:
-        context_str += f"ANALYST DATA: {json.dumps(analyst_data, default=str)}\n"
+        context_str += f"ANALYST DATA (Prices/Availability): {json.dumps(analyst_data, default=str)}\n"
 
     PROMPT = f"""
     You are Jessica, Pricelyst's Shopping Advisor (Zimbabwe).
-    Role: Helpful, thrifty, intelligent companion. Not a robot.
+    Role: Intelligent Shopping Companion.
+    Goal: Shortest path to value. Give answers, not promises.
     
     INPUT: "{transcript}"
     INTENT: {intent.get('intent')}
     CONTEXT:
     {context_str}
     
-    CRITICAL RULES FOR HISTORY:
-    1. Use 'RECENT CHAT HISTORY' ONLY to resolve references (e.g. "how much is *it*?").
-    2. **TOPIC RESET**: If the user says "Hi", "Hello", or asks about a completely NEW topic, IGNORE the previous history items. Do not bring up old shopping lists unless asked.
+    CRITICAL INSTRUCTIONS (Shortest Path Rule):
+    1. **CHECK ANALYST DATA FIRST**: 
+       - If `ANALYST DATA` contains `found_items_details` or `split_strategy` with prices: **REPORT THEM IMMEDIATELY**.
+       - Say: "I found [Product] at [Retailer] for $[Price]."
+       - Do NOT say "I will add this to your list."
+       - Do NOT say "I will check for you." (You have already checked!)
     
-    INSTRUCTIONS:
-    - **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'.
-    - **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.
+    2. **MISSING ITEMS**:
+       - If `global_missing` has items: Say "I checked, but we don't have [Item] in our current catalogue."
+       - Don't fake it. Be honest about catalogue gaps.
+
+    3. **CASUAL CHAT**: 
+       - Only if no products are mentioned. "Makadii! How can I help?"
+       - Reset topic if user says "Hi" or changes subject.
+
+    TONE: Helpful, direct, Zimbabwean. Use Markdown for prices (e.g. **$3.50**).
     """
     
     try:
@@ -527,7 +533,7 @@ def gemini_chat_response(transcript: str, intent: Dict, analyst_data: Dict, chat
         return resp.text
     except Exception as e:
         logger.error(f"Chat Gen Error: {e}")
-        return "I found the data, but I'm struggling to summarize it. Please check the plan below."
+        return "I checked the prices, but I'm having trouble displaying them right now."
 
 def gemini_generate_4step_plan(transcript: str, analyst_result: Dict) -> str:
     if not _gemini_client: return "# Error\nAI Offline."
@@ -563,7 +569,7 @@ def health():
         "ok": True,
         "offers_indexed": len(df),
         "api_source": PRICE_API_BASE,
-        "persona": "Jessica v2.4"
+        "persona": "Jessica v2.5 (Immediate Price Check)"
     })
 
 @app.post("/chat")
@@ -604,7 +610,8 @@ def chat():
     analyst_data = {}
     
     # 3. Data Processing (The Analyst)
-    if intent_type in ["SHOPPING_BASKET", "STORE_DECISION", "TRUST_CHECK"] and items:
+    # Trigger Analyst if Items exist OR intent is specifically about shopping/decisions
+    if items or intent_type in ["SHOPPING_BASKET", "STORE_DECISION", "TRUST_CHECK"]:
         analyst_data = calculate_basket_optimization(items)
         
     elif intent_type == "UTILITY_CALC":
@@ -651,10 +658,7 @@ def analyze_image():
     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.")
 
@@ -663,7 +667,7 @@ def analyze_image():
     
     # 2. Logic Branching
     if img_type == "IRRELEVANT" and not items:
-        # Graceful Rejection / Compliment
+        # Graceful Rejection
         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"}, {}, "")
         
@@ -671,17 +675,17 @@ def analyze_image():
         # Run the Analyst Engine
         analyst_data = calculate_basket_optimization(items)
         
-        # 3. DYNAMIC SIMULATED INTENT
+        # 3. DYNAMIC SIMULATED INTENT (Force immediate answer)
         if img_type == "MEAL":
-            simulated_user_msg = f"I want to cook {description}. I need these ingredients: {', '.join(items)}. How much will it cost?"
+            simulated_user_msg = f"I want to cook {description}. I need {', '.join(items)}. How much does it cost?"
             intent_sim = {"intent": "SHOPPING_BASKET"}
             
         elif img_type == "LIST":
-            simulated_user_msg = f"Here is my shopping list: {', '.join(items)}. Which store is cheapest for the whole basket?"
+            simulated_user_msg = f"Here is my list: {', '.join(items)}. What are the prices?"
             intent_sim = {"intent": "STORE_DECISION"}
             
         else: # PRODUCT
-            simulated_user_msg = f"I see {description}. Which store has the cheapest price for it right now?"
+            simulated_user_msg = f"I see {description}. What is the price for {', '.join(items)}?"
             intent_sim = {"intent": "STORE_DECISION"}
         
         # Generate Response
@@ -689,11 +693,10 @@ def analyze_image():
             simulated_user_msg, 
             intent_sim, 
             analyst_data,
-            chat_history="" # Fresh context
+            chat_history="" 
         )
     
     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({