Update main.py

main.py

@@ -1,12 +1,12 @@
 """
-main.py — Pricelyst Shopping Advisor (Jessica 2026 Edition)
+main.py — Pricelyst Shopping Advisor (Jessica Edition 2026)
 
 ✅ Flask API
 ✅ Firebase Admin Persistence
-✅ Gemini 2.5 Flash (Target Model)
+✅ Gemini 2.5 Flash
 ✅ "Analyst Engine": Python Math for Baskets & ZESA
-✅ "Creative Engine": 4-Step Hybrid Plans (Catalogue -> Estimates -> Totals -> Context Ideas)
-✅ Jessica Context: Live Mini-Catalogue Injection
+✅ "Creative Engine": 4-Step Hybrid Plans
+✅ Type Safety: Explicit Casting for JSON Serialization (Fixes int64 error)
 
 ENV VARS:
 - GOOGLE_API_KEY=...
@@ -48,7 +48,6 @@ except Exception as e:
     logger.error("google-genai not installed. pip install google-genai. Error=%s", e)
 
 GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY", "")
-# Updated to 2.5 Flash as requested for 2026 timeline
 GEMINI_MODEL = os.environ.get("GEMINI_MODEL", "gemini-2.5-flash")
 
 _gemini_client = None
@@ -168,18 +167,18 @@ def fetch_and_flatten_data() -> pd.DataFrame:
     rows = []
     for p in all_products:
         try:
-            p_id = p.get("id")
-            p_name = p.get("name") or "Unknown"
+            p_id = int(p.get("id") or 0)
+            p_name = str(p.get("name") or "Unknown")
             clean_name = _norm(p_name)
             
             cat_obj = p.get("category") or {}
-            cat_name = cat_obj.get("name") or "General"
+            cat_name = str(cat_obj.get("name") or "General")
             
             brand_obj = p.get("brand") or {}
-            brand_name = brand_obj.get("brand_name") or ""
+            brand_name = str(brand_obj.get("brand_name") or "")
             
             views = int(p.get("view_count") or 0)
-            image = p.get("thumbnail") or p.get("image")
+            image = str(p.get("thumbnail") or p.get("image") or "")
 
             prices = p.get("prices") or []
             
@@ -200,7 +199,7 @@ def fetch_and_flatten_data() -> pd.DataFrame:
 
             for offer in prices:
                 retailer = offer.get("retailer") or {}
-                r_name = retailer.get("name") or "Unknown Store"
+                r_name = str(retailer.get("name") or "Unknown Store")
                 price_val = _coerce_price(offer.get("price"))
                 
                 if price_val > 0:
@@ -268,6 +267,7 @@ def search_products_fuzzy(df: pd.DataFrame, query: str, limit: int = 10) -> pd.D
 def calculate_basket_optimization(item_names: List[str]) -> Dict[str, Any]:
     """
     Determines the best store for a list of items.
+    Explicitly casts int64/float64 to native Python types for JSON safety.
     """
     df = get_market_index()
     if df.empty: 
@@ -289,12 +289,11 @@ def calculate_basket_optimization(item_names: List[str]) -> Dict[str, Any]:
         # Best match based on popularity
         best_prod = hits.iloc[0]
         found_items.append({
-            "query": item,
-            "product_id": best_prod['product_id'],
-            "name": best_prod['product_name']
+            "query": str(item),
+            "product_id": int(best_prod['product_id']), # Explicit int cast
+            "name": str(best_prod['product_name'])      # Explicit str cast
         })
 
-    # Note: We return actionable=True even if products missing, so Gemini can estimate
     if not found_items:
         return {
             "actionable": True,
@@ -319,10 +318,10 @@ def calculate_basket_optimization(item_names: List[str]) -> Dict[str, Any]:
         found_names = [x['name'] for x in found_items if x['product_id'] in retailer_pids]
         
         retailer_stats.append({
-            "retailer": retailer,
-            "total_price": float(total_price),
-            "item_count": found_count,
-            "coverage_percent": (found_count / len(found_items)) * 100,
+            "retailer": str(retailer),
+            "total_price": float(total_price), # Explicit float cast
+            "item_count": int(found_count),    # Explicit int cast
+            "coverage_percent": float((found_count / len(found_items)) * 100),
             "found_items": found_names
         })
 
@@ -373,13 +372,13 @@ def calculate_zesa_units(amount_usd: float) -> Dict[str, Any]:
         breakdown.append(f"All {bought:.1f}u @ ${t1['rate']}")
 
     return {
-        "amount_usd": amount_usd,
-        "est_units_kwh": round(units, 1),
+        "amount_usd": float(amount_usd),
+        "est_units_kwh": float(round(units, 1)),
         "breakdown": breakdown
     }
 
 # =========================
-# 3. Gemini Helpers (Detect + Creative)
+# 3. Gemini Helpers
 # =========================
 
 def gemini_detect_intent(transcript: str) -> Dict[str, Any]:
@@ -423,7 +422,7 @@ def gemini_generate_4step_plan(transcript: str, analyst_result: Dict) -> str:
     USER CONTEXT: "{transcript}"
     
     ANALYST DATA (REAL PRICES):
-    {json.dumps(analyst_result, indent=2)}
+    {json.dumps(analyst_result, indent=2, default=str)}
 
     REQUIRED SECTIONS (Do not deviate):
     
@@ -432,7 +431,7 @@ def gemini_generate_4step_plan(transcript: str, analyst_result: Dict) -> str:
        - Show product name, retailer, and exact price from data.
        
     2. **Not in Catalogue (Estimates) 😔**
-       - List items from 'global_missing' or items in the user list but not in 'found_items'.
+       - List items from 'global_missing'.
        - Provide a realistic USD estimate for Zimbabwe.
        
     3. **Totals 💰**
@@ -471,7 +470,7 @@ def health():
 
 @app.post("/chat")
 def chat():
-    """Text Chat - Uses simple summary."""
+    """Text Chat."""
     body = request.get_json(silent=True) or {}
     msg = body.get("message", "")
     pid = body.get("profile_id")
@@ -489,13 +488,12 @@ def chat():
         elif intent_data["intent"] == "PRODUCT_SEARCH" and intent_data.get("items"):
             analyst_data = calculate_basket_optimization(intent_data["items"])
 
-    # Quick chat summary
     reply = "I'm processing that for you."
     if analyst_data:
         try:
             resp = _gemini_client.models.generate_content(
                 model=GEMINI_MODEL,
-                contents=f"Summarize this shopping data for chat:\n{json.dumps(analyst_data)}"
+                contents=f"Summarize this shopping data for chat:\n{json.dumps(analyst_data, default=str)}"
             )
             reply = resp.text
         except: pass
@@ -541,7 +539,7 @@ def call_briefing():
         top = df[df['is_offer']].sort_values('views', ascending=False).drop_duplicates('product_name').head(60)
         lines = []
         for _, r in top.iterrows():
-            lines.append(f"{r['product_name']} (${r['price']:.2f})")
+            lines.append(f"{r['product_name']} (~${r['price']:.2f})")
         catalogue_str = ", ".join(lines)
 
     kpi_snapshot = {
@@ -558,12 +556,7 @@ def call_briefing():
 @app.post("/api/log-call-usage")
 def log_call_usage():
     """
-    The Post-Call Orchestrator.
-    1. Update Memory.
-    2. Detect Intent.
-    3. Run Analyst.
-    4. Generate 4-Step Creative Plan.
-    5. Save.
+    Post-Call Orchestrator.
     """
     body = request.get_json(silent=True) or {}
     pid = body.get("profile_id")
@@ -573,7 +566,6 @@ def log_call_usage():
     
     logger.info(f"Log Call: Processing {pid}. Transcript Len: {len(transcript)}")
 
-    # 1. Update Memory
     if len(transcript) > 20 and db:
         try:
             curr_mem = db.collection("pricelyst_profiles").document(pid).get().to_dict().get("memory_summary", "")
@@ -583,21 +575,16 @@ def log_call_usage():
         except Exception as e:
             logger.error(f"Memory Update Error: {e}")
 
-    # 2. Intent Detection
     intent_data = gemini_detect_intent(transcript)
     logger.info(f"Intent: {intent_data.get('intent')}")
     
     plan_data = {}
     
-    # 3. Actionable Logic
     if intent_data.get("actionable"):
         
-        # Run Analyst
         if intent_data.get("items"):
             analyst_result = calculate_basket_optimization(intent_data["items"])
             
-            # 4. Generate Creative Plan (4-Step)
-            # We generate if we have actionable basket results OR missing items
             if analyst_result.get("actionable"):
                 md_content = gemini_generate_4step_plan(transcript, analyst_result)
                 
@@ -615,7 +602,6 @@ def log_call_usage():
                     doc_ref.set(plan_data)
                     logger.info(f"Plan Saved: {doc_ref.id}")
 
-    # 5. Log Call
     if db:
         db.collection("pricelyst_profiles").document(pid).collection("call_logs").add({
             "transcript": transcript,