Update main.py

main.py

@@ -1,11 +1,11 @@
 """
-main.py — Pricelyst Shopping Advisor (Jessica Edition 2026 - Upgrade v3.1)
+main.py — Pricelyst Shopping Advisor (April Edition 2026 - Upgrade v3.1)
 
 ✅ Feature: "Vernacular Engine" (Shona/Ndebele/English Input -> Native Response).
 ✅ Feature: "Precision Search" (Prioritizes exact phrase matches over popularity).
 ✅ Feature: "Concept Exploder" (Event Planning -> Shopping List).
 ✅ UI/UX: "Nearest Match" phrasing for substitutions.
-✅ Core: Deep Vector Search + Market Matrix + Store Preferences.
+✅ Core: Deep Vector Search + Market Matrix + Store Preferences + Savings Calculator.
 
 ENV VARS:
 - GOOGLE_API_KEY=...
@@ -289,13 +289,13 @@ def search_products_deep(df: pd.DataFrame, query: str, limit: int = 15) -> pd.Da
 
 def calculate_basket_optimization(item_names: List[str], preferred_retailer: str = None) -> Dict[str, Any]:
     """
-    Generates a FULL MARKET MATRIX with Precision Search.
+    Generates a FULL MARKET MATRIX with Precision Search and Savings Calculation.
     """
     df = get_market_index()
     if df.empty: 
         return {"actionable": False, "error": "No data"}
 
-    found_items = [] 
+    found_items = []
     missing_global = []
 
     # 1. Resolve Items & Check Brand Fidelity
@@ -305,7 +305,7 @@ def calculate_basket_optimization(item_names: List[str], preferred_retailer: str
         if hits.empty:
             missing_global.append(item)
             continue
-        
+            
         best_match = hits.iloc[0]
         
         # --- Brand Fidelity Check ---
@@ -314,12 +314,10 @@ def calculate_basket_optimization(item_names: List[str], preferred_retailer: str
         q_tokens = q_norm.split()
         
         is_substitute = False
-        # If query has brand/spec but result score is low-ish (not exact name match), flag it.
-        # Using a simple heuristic for now based on token overlap vs query length
         found_tokens = sum(1 for t in q_tokens if t in res_norm)
         if len(q_tokens) > 1 and found_tokens < len(q_tokens):
             is_substitute = True
-                
+            
         # Aggregate all offers
         product_offers = hits[hits['product_name'] == best_match['product_name']].sort_values('price')
         
@@ -327,12 +325,17 @@ def calculate_basket_optimization(item_names: List[str], preferred_retailer: str
         for _, r in product_offers.iterrows():
             offers_list.append({"retailer": r['retailer'], "price": float(r['price'])})
 
+        best_price = offers_list[0]['price']
+        max_price = offers_list[-1]['price']
+        potential_savings = max_price - best_price
+
         found_items.append({
             "query": item,
             "product_name": str(best_match['product_name']),
             "is_substitute": is_substitute,
             "offers": offers_list,
-            "best_price": offers_list[0]['price']
+            "best_price": best_price,
+            "potential_savings": potential_savings
         })
 
     if not found_items:
@@ -345,7 +348,7 @@ def calculate_basket_optimization(item_names: List[str], preferred_retailer: str
             all_involved_retailers.add(o['retailer'])
             
     store_comparison = []
-    
+
     for retailer in all_involved_retailers:
         total_price = 0.0
         found_count = 0
@@ -368,7 +371,17 @@ def calculate_basket_optimization(item_names: List[str], preferred_retailer: str
         })
 
     store_comparison.sort(key=lambda x: (-x['found_count'], x['total_price']))
-    
+
+    # 3. Calculate Basket-Level Savings
+    if len(store_comparison) > 1:
+        most_expensive_total = max(s['total_price'] for s in store_comparison if s['found_count'] == store_comparison[0]['found_count'])
+        for store in store_comparison:
+            # Savings calculated against the highest total for an equivalent sized basket
+            store['basket_savings'] = most_expensive_total - store['total_price'] if store['found_count'] == store_comparison[0]['found_count'] else 0.0
+    else:
+        for store in store_comparison:
+            store['basket_savings'] = 0.0
+
     return {
         "actionable": True,
         "is_basket": len(found_items) > 1,
@@ -380,7 +393,7 @@ def calculate_basket_optimization(item_names: List[str], preferred_retailer: str
     }
 
 def calculate_zesa_units(amount_usd: float) -> Dict[str, Any]:
-    remaining = amount_usd / 1.06 
+    remaining = amount_usd / 1.06
     units = 0.0
     
     t1 = ZIM_CONTEXT["zesa_step_1"]
@@ -520,7 +533,7 @@ def gemini_chat_response(transcript: str, intent: Dict, analyst_data: Dict, chat
     language = intent.get("language", "English")
 
     PROMPT = f"""
-    You are Jessica, Pricelyst's Shopping Advisor (Zimbabwe).
+    You are April, Pricelyst's Shopping Advisor (Zimbabwe).
     Role: Intelligent Shopping Companion.
     Goal: Shortest path to value. Complete Transparency.
     
@@ -535,15 +548,16 @@ def gemini_chat_response(transcript: str, intent: Dict, analyst_data: Dict, chat
     1. **LANGUAGE**: Reply in **{language}**. If Shona, use Shona. If English, use English.
     
     2. **BASKET COMPARISON**:
-       - If `market_matrix` has multiple stores, compare totals.
-       - "Spar is **$6.95**, OK Mart is **$4.00** (but missing Oil)."
+       - If `market_matrix` has multiple stores, compare totals and explicitly state the savings using the pre-calculated `basket_savings`.
+       - Example: "Spar is **$6.95**, OK Mart is **$4.00** (but missing Oil). You save **$2.95** by getting the basket at OK Mart!"
 
     3. **BRAND SUBSTITUTES (Phrasing)**:
-       - If `is_substitute` is TRUE for an item, say: 
+       - If `is_substitute` is TRUE for an item, say:
          "I couldn't find **[Query]**, but the **nearest match is** **[Found]** ($Price)."
 
     4. **SINGLE ITEMS**:
-       - Best price first, then others.
+       - State the best price first, then others. Explicitly state how much is saved by choosing the cheapest option over the most expensive one based on `potential_savings`.
+       - Example: "The cheapest is **$2.00** at OK. You save **$0.50** compared to the most expensive store!"
 
     5. **CASUAL**:
        - Reset if user says "Hi".
@@ -576,21 +590,22 @@ def gemini_generate_4step_plan(transcript: str, analyst_result: Dict) -> str:
     
     SECTIONS:
     
-    1. **In Our Catalogue ✅** 
-       (Markdown Table: | Item | Retailer | Price (USD) |)
-    
+    1. **In Our Catalogue ✅**
+       (Markdown Table: | Item | Retailer | Price (USD) | Potential Savings |)
+       
     2. **Not in Catalogue (Estimates) 😔**
        (Markdown Table: | Item | Estimated Price (USD) |)
        *Fill in estimated prices for missing items based on Zimbabwe market knowledge.*
        
-    3. **Totals 💰**
+    3. **Totals & Savings 💰**
        - Confirmed Total (Catalogue)
+       - Total Basket Savings (From cheapest vs most expensive store)
        - Estimated Total (Missing Items)
        - **Grand Total Estimate**
        
     4. **Ideas & Tips 💡**
        - 3 Creative ideas based on the specific event/meal (e.g. Braai tips, Cooking hacks).
-    
+
     Tone: Warm, Professional, Zimbabwean.
     """
     try:
@@ -610,7 +625,7 @@ def health():
         "ok": True,
         "offers_indexed": len(df),
         "api_source": PRICE_API_BASE,
-        "persona": "Jessica v3.1 (Babel Fish)"
+        "persona": "April v3.1 (Babel Fish)"
     })
 
 @app.post("/chat")
@@ -627,7 +642,8 @@ def chat():
         try:
             docs = db.collection("pricelyst_profiles").document(pid).collection("chat_logs") \
                      .order_by("ts", direction=firestore.Query.DESCENDING).limit(6).stream()
-            msgs = [f"User: {d.to_dict().get('message')}\nJessica: {d.to_dict().get('response')}" for d in docs]
+            # Persona updated to April here for context memory
+            msgs = [f"User: {d.to_dict().get('message')}\nApril: {d.to_dict().get('response')}" for d in docs]
             if msgs: history_str = "\n".join(reversed(msgs))
         except: pass
 
@@ -635,7 +651,7 @@ def chat():
     intent_data = gemini_detect_intent(msg)
     intent_type = intent_data.get("intent", "CASUAL_CHAT")
     items = intent_data.get("items", [])
-    store_pref = intent_data.get("store_preference") 
+    store_pref = intent_data.get("store_preference")
     
     analyst_data = {}
     
@@ -647,7 +663,7 @@ def chat():
         analyst_data = calculate_zesa_units(amount)
         
     reply = gemini_chat_response(msg, intent_data, analyst_data, history_str)
-    
+
     if db:
         db.collection("pricelyst_profiles").document(pid).collection("chat_logs").add({
             "message": msg,
@@ -661,7 +677,8 @@ def chat():
 @app.post("/api/analyze-image")
 def analyze_image():
     body = request.get_json(silent=True) or {}
-    image_b64 = body.get("image_data") 
+    image_b64 = body.get("image_data")
+    
     caption = body.get("caption", "")
     pid = body.get("profile_id")
     
@@ -692,7 +709,7 @@ def analyze_image():
         else: sim_msg = f"Cheapest price for {', '.join(items)}?"
         
         response_text = gemini_chat_response(sim_msg, {"intent": "STORE_DECISION"}, analyst_data, "")
-    
+        
     else:
         response_text = "I couldn't identify the product. Could you type the name?"
 
@@ -723,9 +740,9 @@ def call_briefing():
         doc = ref.get()
         if doc.exists: prof = doc.to_dict()
         else: ref.set({"created_at": datetime.now(timezone.utc).isoformat()})
-    
-    if username != "Friend" and username != prof.get("username"):
-        if db: db.collection("pricelyst_profiles").document(pid).set({"username": username}, merge=True)
+
+        if username != "Friend" and username != prof.get("username"):
+            if db: db.collection("pricelyst_profiles").document(pid).set({"username": username}, merge=True)
 
     # 2. Market Intelligence Generation
     df = get_market_index()
@@ -752,7 +769,7 @@ def call_briefing():
             if not hits.empty:
                 cheapest = hits.sort_values('price').iloc[0]
                 staple_summary.append(f"- {s}: ${cheapest['price']} @ {cheapest['retailer']}")
-    
+
     staples_section = "\n[STAPLES - LOWEST]\n" + "\n".join(staple_summary)
     
     # C. Top 60 Catalogue
@@ -859,6 +876,7 @@ def delete_plan(plan_id):
         return jsonify({"ok": True})
     except: return jsonify({"ok": False}), 500
 
+
 if __name__ == "__main__":
     port = int(os.environ.get("PORT", 7860))
     try: get_market_index(force_refresh=True)