Update main.py

main.py

@@ -1,5 +1,5 @@
 """
-main.py — Pricelyst Shopping Advisor (Jessica Edition 2026 - Upgrade v2.0)
+main.py — Pricelyst Shopping Advisor (Jessica Edition 2026 - Upgrade v2.1)
 
 ✅ Flask API
 ✅ Firebase Admin Persistence
@@ -8,6 +8,7 @@ main.py — Pricelyst Shopping Advisor (Jessica Edition 2026 - Upgrade v2.0)
 ✅ "Jessica Engine": Natural Conversation, Chit-Chat, & Advisory
 ✅ "Visual Engine": Lists, Products, & Meal-to-Recipe recognition
 ✅ Type Safety: Explicit Casting for JSON Serialization
+✅ Memory Logic: Separated Deep Memory (Calls) from Short-Term Context (Chat)
 
 ENV VARS:
 - GOOGLE_API_KEY=...
@@ -184,7 +185,6 @@ def fetch_and_flatten_data() -> pd.DataFrame:
 
             prices = p.get("prices") or []
             
-            # If no prices, still index it for context but mark as no offer
             if not prices:
                 rows.append({
                     "product_id": p_id,
@@ -270,10 +270,8 @@ def search_products_fuzzy(df: pd.DataFrame, query: str, limit: int = 10) -> pd.D
 def get_category_stats(df: pd.DataFrame, category_name: str) -> Dict[str, Any]:
     """Returns min, max, avg price for a category to determine value."""
     if df.empty: return {}
-    # Loose matching on category or name
     cat_df = df[df['category'].str.lower().str.contains(category_name.lower()) & df['is_offer']]
     if cat_df.empty:
-        # Try name matching if category fails
         cat_df = df[df['clean_name'].str.contains(category_name.lower()) & df['is_offer']]
         
     if cat_df.empty: return {}
@@ -288,9 +286,7 @@ def get_category_stats(df: pd.DataFrame, category_name: str) -> Dict[str, Any]:
 
 def calculate_basket_optimization(item_names: List[str]) -> Dict[str, Any]:
     """
-    Optimizes for:
-    1. Best Single Store (Convenience)
-    2. Split Store (Cheapest possible mix)
+    Optimizes for: 1. Best Single Store, 2. Cheapest Split Mix.
     """
     df = get_market_index()
     if df.empty: 
@@ -299,7 +295,6 @@ def calculate_basket_optimization(item_names: List[str]) -> Dict[str, Any]:
     found_items = [] 
     missing_global = []
 
-    # 1. Resolve Items
     for item in item_names:
         hits = search_products_fuzzy(df[df['is_offer']==True], item, limit=5)
         if hits.empty:
@@ -307,8 +302,6 @@ def calculate_basket_optimization(item_names: List[str]) -> Dict[str, Any]:
             continue
         
         best_prod = hits.iloc[0]
-        
-        # Get category stats for the first item to help with "is this expensive" logic later
         cat_stats = get_category_stats(df, str(best_prod['category']))
         
         found_items.append({
@@ -328,7 +321,6 @@ def calculate_basket_optimization(item_names: List[str]) -> Dict[str, Any]:
             "split_strategy": None
         }
 
-    # 2. Calculate Retailer Totals (Single Store Strategy)
     target_pids = [x['product_id'] for x in found_items]
     relevant_offers = df[df['product_id'].isin(target_pids) & df['is_offer']]
     
@@ -354,13 +346,10 @@ def calculate_basket_optimization(item_names: List[str]) -> Dict[str, Any]:
     retailer_stats.sort(key=lambda x: (-x['coverage_percent'], x['total_price']))
     best_single_store = retailer_stats[0] if retailer_stats else None
 
-    # 3. Calculate Split Strategy (Cheapest Global)
-    # For each found item, find the absolute minimum price across all stores
     split_basket = []
     split_total = 0.0
     
     for item in found_items:
-        # Find offers for this specific product ID
         p_offers = relevant_offers[relevant_offers['product_id'] == item['product_id']]
         if not p_offers.empty:
             best_offer = p_offers.sort_values('price').iloc[0]
@@ -399,7 +388,7 @@ def calculate_zesa_units(amount_usd: float) -> Dict[str, Any]:
     if remaining > cost_t1:
         units += t1["limit"]
         remaining -= cost_t1
-        breakdown.append(f"First {t1['limit']} units at cheap rate (${t1['rate']}) ✅")
+        breakdown.append(f"First {t1['limit']}u @ ${t1['rate']}")
         
         t2 = ZIM_CONTEXT["zesa_step_2"]
         cost_t2 = t2["limit"] * t2["rate"]
@@ -407,20 +396,20 @@ def calculate_zesa_units(amount_usd: float) -> Dict[str, Any]:
         if remaining > cost_t2:
             units += t2["limit"]
             remaining -= cost_t2
-            breakdown.append(f"Next {t2['limit']} units at standard rate (${t2['rate']}) ⚡")
+            breakdown.append(f"Next {t2['limit']}u @ ${t2['rate']}")
             
             t3 = ZIM_CONTEXT["zesa_step_3"]
             bought = remaining / t3["rate"]
             units += bought
-            breakdown.append(f"Remainder bought at high tariff (${t3['rate']}) 💸")
+            breakdown.append(f"Balance -> {bought:.1f}u @ ${t3['rate']}")
         else:
             bought = remaining / t2["rate"]
             units += bought
-            breakdown.append(f"Remainder bought at standard rate (${t2['rate']}) ⚡")
+            breakdown.append(f"Balance -> {bought:.1f}u @ ${t2['rate']}")
     else:
         bought = remaining / t1["rate"]
         units += bought
-        breakdown.append(f"All units at cheapest 'Lifeline' rate (${t1['rate']}) 💚")
+        breakdown.append(f"All {bought:.1f}u @ ${t1['rate']}")
 
     return {
         "amount_usd": float(amount_usd),
@@ -434,10 +423,6 @@ def calculate_zesa_units(amount_usd: float) -> Dict[str, Any]:
 # =========================
 
 def gemini_detect_intent(transcript: str) -> Dict[str, Any]:
-    """
-    Expanded Intent Classification.
-    Detects: CASUAL, SHOPPING_BASKET, UTILITY, STORE_DECISION, TRUST_CHECK
-    """
     if not _gemini_client: return {"actionable": False}
     
     PROMPT = """
@@ -452,7 +437,7 @@ def gemini_detect_intent(transcript: str) -> Dict[str, Any]:
     Extract:
     - items: list of products
     - utility_amount: number
-    - context: "budget", "speed", "quality" (if mentioned)
+    - context: "budget", "speed", "quality"
     
     JSON Schema:
     {
@@ -475,10 +460,6 @@ def gemini_detect_intent(transcript: str) -> Dict[str, Any]:
         return {"actionable": False, "intent": "CASUAL_CHAT"}
 
 def gemini_analyze_image(image_b64: str, caption: str = "") -> Dict[str, Any]:
-    """
-    Multimodal Image Analysis.
-    Determines if image is a Shopping List, Product, or Meal.
-    """
     if not _gemini_client: return {"error": "AI Offline"}
     
     PROMPT = f"""
@@ -505,20 +486,17 @@ def gemini_analyze_image(image_b64: str, caption: str = "") -> Dict[str, Any]:
             ],
             config=types.GenerateContentConfig(response_mime_type="application/json")
         )
-        return _safe_json_loads(resp.text, {"type": "IRRELEVANT", "items": []})
+        result = _safe_json_loads(resp.text, {"type": "IRRELEVANT", "items": []})
+        logger.info(f"🔮 VISION RAW: {json.dumps(result)}") # Debug Logging
+        return result
     except Exception as e:
         logger.error(f"Vision Error: {e}")
         return {"type": "IRRELEVANT", "items": []}
 
-def gemini_chat_response(transcript: str, intent: Dict, analyst_data: Dict, memory_summary: str = "") -> str:
-    """
-    The Persona Engine (Jessica).
-    Synthesizes Analyst Data + ZESA Context + Memory into a natural response.
-    """
+def gemini_chat_response(transcript: str, intent: Dict, analyst_data: Dict, chat_history: str = "") -> str:
     if not _gemini_client: return "I'm having trouble connecting to my brain right now."
 
-    # Context Construction
-    context_str = f"USER MEMORY: {memory_summary}\n" if memory_summary else ""
+    context_str = f"RECENT CHAT HISTORY:\n{chat_history}\n" if chat_history else ""
     context_str += f"ZIMBABWE CONTEXT: Fuel={ZIM_CONTEXT['fuel_petrol']}, ZESA Rate={ZIM_CONTEXT['zesa_step_1']['rate']}\n"
     
     if analyst_data:
@@ -534,7 +512,7 @@ def gemini_chat_response(transcript: str, intent: Dict, analyst_data: Dict, memo
     {context_str}
     
     INSTRUCTIONS:
-    1. **Casual Chat**: If intent is CASUAL, be warm. "Makadii! How can I help you save today?"
+    1. **Casual Chat**: Use the chat history to reply naturally. If history is empty, be warm. "Makadii! How can I help?"
     2. **Shopping Advice**: 
        - If data exists, guide them. "I found XYZ at Store A for $5."
        - If 'best_store' exists, recommend it explicitly based on coverage.
@@ -544,7 +522,7 @@ def gemini_chat_response(transcript: str, intent: Dict, analyst_data: Dict, memo
        - If price is > avg, say "That's a bit pricey, average is $X."
     4. **ZESA**: Explain the units naturally using the breakdown provided.
     
-    TONE: Conversational, Zimbabwean English (use 'USD', maybe 'shame' or 'eish' rarely if bad news, but professional).
+    TONE: Conversational, Zimbabwean English.
     Do NOT dump JSON. Write a natural message. Use Markdown for lists/prices.
     """
     
@@ -559,9 +537,6 @@ def gemini_chat_response(transcript: str, intent: Dict, analyst_data: Dict, memo
         return "I found the data, but I'm struggling to summarize it. Please check the plan below."
 
 def gemini_generate_4step_plan(transcript: str, analyst_result: Dict) -> str:
-    """
-    Generates the Formal Shopping Plan (Markdown).
-    """
     if not _gemini_client: return "# Error\nAI Offline."
 
     PROMPT = f"""
@@ -573,8 +548,8 @@ def gemini_generate_4step_plan(transcript: str, analyst_result: Dict) -> str:
     1. **In Our Catalogue ✅** (Table: Item | Store | Price)
     2. **Not in Catalogue 😔** (Estimates)
     3. **Recommendation 💡**
-       - "Best Single Store" vs "Split & Save" (if split saves money).
-    4. **Budget Tips** (Based on items, e.g., generic brands).
+       - "Best Single Store" vs "Split & Save".
+    4. **Budget Tips**
     
     Make it look professional yet friendly.
     """
@@ -595,14 +570,14 @@ def health():
         "ok": True,
         "offers_indexed": len(df),
         "api_source": PRICE_API_BASE,
-        "persona": "Jessica v2.0"
+        "persona": "Jessica v2.1 (Memory Firewall)"
     })
 
 @app.post("/chat")
 def chat():
     """
     Unified Text Chat Endpoint.
-    Handles Casual, Search, and Advisory intents.
+    Uses SHORT-TERM SLIDING WINDOW memory only.
     """
     body = request.get_json(silent=True) or {}
     msg = body.get("message", "")
@@ -610,11 +585,24 @@ def chat():
     
     if not pid: return jsonify({"ok": False, "error": "Missing profile_id"}), 400
     
-    # 1. Memory Lookup
-    memory_summary = ""
+    # 1. Fetch Short-Term History (Sliding Window)
+    history_str = ""
     if db:
-        prof = db.collection("pricelyst_profiles").document(pid).get()
-        if prof.exists: memory_summary = prof.to_dict().get("memory_summary", "")
+        try:
+            # Get last 6 messages, ordered by time descending (newest first)
+            docs = db.collection("pricelyst_profiles").document(pid).collection("chat_logs") \
+                     .order_by("ts", direction=firestore.Query.DESCENDING).limit(6).stream()
+            
+            # Reverse to Chronological order for the LLM
+            msgs = []
+            for d in docs:
+                data = d.to_dict()
+                msgs.append(f"User: {data.get('message')}\nJessica: {data.get('response')}")
+            
+            if msgs:
+                history_str = "\n".join(reversed(msgs))
+        except Exception as e:
+            logger.error(f"History Fetch Error: {e}")
 
     # 2. Intent Detection
     intent_data = gemini_detect_intent(msg)
@@ -632,7 +620,7 @@ def chat():
         analyst_data = calculate_zesa_units(amount)
         
     # 4. Response Generation (The Persona)
-    reply = gemini_chat_response(msg, intent_data, analyst_data, memory_summary)
+    reply = gemini_chat_response(msg, intent_data, analyst_data, history_str)
     
     # 5. Async Logging
     if db:
@@ -654,7 +642,7 @@ def chat():
 @app.post("/api/analyze-image")
 def analyze_image():
     """
-    New Endpoint: Handles Image -> List/Product/Meal -> Shopping Data
+    Handles Image -> List/Product/Meal -> Shopping Data
     """
     body = request.get_json(silent=True) or {}
     image_b64 = body.get("image_data") # Base64 string
@@ -676,10 +664,10 @@ def analyze_image():
         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!"
         
     elif items:
-        # Run the Analyst Engine on the extracted items
+        # Run the Analyst Engine
         analyst_data = calculate_basket_optimization(items)
         
-        # Craft a specific prompt for image results
+        # Craft a specific prompt for image results (No history needed here usually)
         intent_sim = {"intent": "SHOPPING_BASKET"}
         response_text = gemini_chat_response(
             f"User uploaded image of {img_type}: {vision_result.get('description')}. Items found: {items}", 
@@ -698,7 +686,7 @@ def analyze_image():
 @app.post("/api/call-briefing")
 def call_briefing():
     """
-    Injects Memory + Context for Voice Bot.
+    Injects LONG-TERM Memory + Context for Voice Bot.
     """
     body = request.get_json(silent=True) or {}
     pid = body.get("profile_id")
@@ -718,7 +706,7 @@ def call_briefing():
     if username and username != prof.get("username"):
         if db: db.collection("pricelyst_profiles").document(pid).set({"username": username}, merge=True)
 
-    # Mini-Catalogue (Top 60 items for context)
+    # Mini-Catalogue
     df = get_market_index()
     catalogue_str = ""
     if not df.empty:
@@ -733,7 +721,7 @@ def call_briefing():
     
     return jsonify({
         "ok": True,
-        "memory_summary": prof.get("memory_summary", ""),
+        "memory_summary": prof.get("memory_summary", ""), # Keep Long Term memory here
         "kpi_snapshot": json.dumps(kpi_snapshot)
     })
 
@@ -741,7 +729,7 @@ def call_briefing():
 def log_call_usage():
     """
     Post-Call Orchestrator.
-    Generates Plans & Updates Memory.
+    Generates Plans & Updates Long-Term Memory.
     """
     body = request.get_json(silent=True) or {}
     pid = body.get("profile_id")
@@ -749,7 +737,7 @@ def log_call_usage():
     
     if not pid: return jsonify({"ok": False}), 400
     
-    # 1. Update Memory
+    # 1. Update Long-Term Memory
     if len(transcript) > 20 and db:
         try:
             curr_mem = db.collection("pricelyst_profiles").document(pid).get().to_dict().get("memory_summary", "")