Update main.py

main.py

@@ -1,14 +1,11 @@
 """
-main.py — Pricelyst Shopping Advisor (Jessica Edition 2026 - Upgrade v2.1)
+main.py — Pricelyst Shopping Advisor (Jessica Edition 2026 - Upgrade v2.2)
 
-✅ Flask API
-✅ Firebase Admin Persistence
-✅ Gemini 2.5 Flash (Multimodal)
+✅ Fixed: Vision SDK Crash (Part.from_text removed)
+✅ Fixed: "Clingy" Memory (Topic Reset Rule added)
 ✅ "Analyst Engine": Enhanced Basket Math, Category Context, ZESA Logic
-✅ "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)
+✅ Memory Logic: Short-Term Sliding Window (Last 6 messages)
 
 ENV VARS:
 - GOOGLE_API_KEY=...
@@ -236,7 +233,7 @@ def get_market_index(force_refresh: bool = False) -> pd.DataFrame:
     return _data_cache["df"]
 
 # =========================
-# 2. Analyst Engine (Enhanced Math Logic)
+# 2. Analyst Engine (Math Logic)
 # =========================
 
 def search_products_fuzzy(df: pd.DataFrame, query: str, limit: int = 10) -> pd.DataFrame:
@@ -268,7 +265,6 @@ def search_products_fuzzy(df: pd.DataFrame, query: str, limit: int = 10) -> pd.D
     return matches.head(limit)
 
 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 {}
     cat_df = df[df['category'].str.lower().str.contains(category_name.lower()) & df['is_offer']]
     if cat_df.empty:
@@ -285,9 +281,6 @@ 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, 2. Cheapest Split Mix.
-    """
     df = get_market_index()
     if df.empty: 
         return {"actionable": False, "error": "No data"}
@@ -377,8 +370,7 @@ def calculate_basket_optimization(item_names: List[str]) -> Dict[str, Any]:
     }
 
 def calculate_zesa_units(amount_usd: float) -> Dict[str, Any]:
-    """Calculates ZESA units with tiered logic explanation."""
-    remaining = amount_usd / 1.06  # Remove 6% REA Levy approx
+    remaining = amount_usd / 1.06 
     units = 0.0
     breakdown = []
     
@@ -419,7 +411,7 @@ def calculate_zesa_units(amount_usd: float) -> Dict[str, Any]:
     }
 
 # =========================
-# 3. Gemini Helpers (Persona & Vision)
+# 3. Gemini Helpers
 # =========================
 
 def gemini_detect_intent(transcript: str) -> Dict[str, Any]:
@@ -428,7 +420,7 @@ def gemini_detect_intent(transcript: str) -> Dict[str, Any]:
     PROMPT = """
     Analyze transcript. Return STRICT JSON.
     Classify intent:
-    - CASUAL_CHAT: Greetings, small talk, no shopping data needed.
+    - CASUAL_CHAT: Greetings, small talk, "hi", "thanks".
     - SHOPPING_BASKET: Looking for prices, creating a list.
     - UTILITY_CALC: Electricity/ZESA questions.
     - STORE_DECISION: "Where should I buy?", "Which store is cheapest?".
@@ -437,7 +429,7 @@ def gemini_detect_intent(transcript: str) -> Dict[str, Any]:
     Extract:
     - items: list of products
     - utility_amount: number
-    - context: "budget", "speed", "quality"
+    - context_tag: "budget", "speed", "quality"
     
     JSON Schema:
     {
@@ -477,17 +469,18 @@ def gemini_analyze_image(image_b64: str, caption: str = "") -> Dict[str, Any]:
     }}
     """
     try:
+        # FIX: Remove types.Part.from_text wrapping. Pass string directly.
         image_bytes = base64.b64decode(image_b64)
         resp = _gemini_client.models.generate_content(
             model=GEMINI_MODEL,
             contents=[
-                types.Part.from_text(PROMPT),
+                PROMPT, # Pass text as string directly
                 types.Part.from_bytes(data=image_bytes, mime_type="image/jpeg")
             ],
             config=types.GenerateContentConfig(response_mime_type="application/json")
         )
         result = _safe_json_loads(resp.text, {"type": "IRRELEVANT", "items": []})
-        logger.info(f"🔮 VISION RAW: {json.dumps(result)}") # Debug Logging
+        logger.info(f"🔮 VISION RAW: {json.dumps(result)}") 
         return result
     except Exception as e:
         logger.error(f"Vision Error: {e}")
@@ -496,7 +489,7 @@ def gemini_analyze_image(image_b64: str, caption: str = "") -> Dict[str, Any]:
 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_str = f"RECENT CHAT HISTORY:\n{chat_history}\n" if chat_history else ""
+    context_str = f"RECENT CHAT HISTORY (Last 6 messages):\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:
@@ -511,19 +504,17 @@ def gemini_chat_response(transcript: str, intent: Dict, analyst_data: Dict, chat
     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.
+    
     INSTRUCTIONS:
-    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.
-       - If 'split_strategy' saves money (>10%), suggest splitting the shop.
-    3. **Trust & Budget**:
-       - If user asks "Is this expensive?", check 'category_stats' in data.
-       - If price is > avg, say "That's a bit pricey, average is $X."
-    4. **ZESA**: Explain the units naturally using the breakdown provided.
+    - **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.
     
     TONE: Conversational, Zimbabwean English.
-    Do NOT dump JSON. Write a natural message. Use Markdown for lists/prices.
     """
     
     try:
@@ -570,7 +561,7 @@ def health():
         "ok": True,
         "offers_indexed": len(df),
         "api_source": PRICE_API_BASE,
-        "persona": "Jessica v2.1 (Memory Firewall)"
+        "persona": "Jessica v2.2"
     })
 
 @app.post("/chat")
@@ -589,11 +580,10 @@ def chat():
     history_str = ""
     if db:
         try:
-            # Get last 6 messages, ordered by time descending (newest first)
+            # Get last 6 messages
             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()
@@ -721,7 +711,7 @@ def call_briefing():
     
     return jsonify({
         "ok": True,
-        "memory_summary": prof.get("memory_summary", ""), # Keep Long Term memory here
+        "memory_summary": prof.get("memory_summary", ""),
         "kpi_snapshot": json.dumps(kpi_snapshot)
     })