Update main.py

main.py

@@ -1,12 +1,13 @@
 """
-main.py — Pricelyst Shopping Advisor (single-file server)
+main.py — Pricelyst Shopping Advisor (Jessica Edition)
 
 ✅ Flask API
 ✅ Firebase Admin persistence (service account JSON via env var)
-✅ Gemini via NEW google-genai SDK (text + multimodal)
-✅ Product intelligence from Pricelyst API (/api/v1/products is open)
-✅ Graceful conversational handling (don’t “force” shopping intent)
-✅ Call briefing + call logging + optional actionable post-call report
+✅ Gemini via NEW google-genai SDK (text + multimodal + JSON Mode)
+✅ Product intelligence from Pricelyst API
+✅ Graceful conversational handling (Backwards Compatible)
+✅ Call briefing (Zim Essentials Injection)
+✅ Post-call Shopping Plan Generation (PDF-ready)
 
 ENV VARS YOU NEED
 - GOOGLE_API_KEY=...
@@ -14,36 +15,6 @@ ENV VARS YOU NEED
 - PRICE_API_BASE=https://api.pricelyst.co.zw   # optional
 - GEMINI_MODEL=gemini-2.0-flash                # optional
 - PORT=5000                                    # optional
-
-REQUEST SHAPES
-1) POST /chat
-{
-  "profile_id": "demo123",
-  "username": "Tinashe",              # optional
-  "message": "Where is cooking oil cheapest?",
-  "images": ["data:image/png;base64,...", "https://..."],   # optional
-  "context": { "budget": 20, "location": "Harare" }         # optional
-}
-
-2) POST /api/call-briefing
-{
-  "profile_id": "demo123",
-  "username": "Tinashe"
-}
-
-3) POST /api/log-call-usage
-{
-  "profile_id": "demo123",
-  "transcript": ".... full transcript ...",
-  "call_id": "optional-client-id",
-  "started_at": "2026-01-23T12:00:00Z",
-  "ended_at": "2026-01-23T12:08:05Z",
-  "stats": { "duration_sec": 485, "agent": "elevenlabs" }
-}
-
-NOTES
-- We DO NOT depend on upstream auth (you said products are open).
-- We keep our own "profile_id" for personalization; when integrated, the host app supplies real profile_id.
 """
 
 import os
@@ -73,6 +44,7 @@ logger = logging.getLogger("pricelyst-advisor")
 # pip install google-genai
 try:
     from google import genai
+    from google.genai import types
 except Exception as e:
     genai = None
     logger.error("google-genai not installed. pip install google-genai. Error=%s", e)
@@ -125,6 +97,16 @@ _product_cache: Dict[str, Any] = {
     "raw_count": 0,
 }
 
+# ---------- Static Data (New Feature) ----------
+# Injected into Voice Context & used for Text Fallback
+ZIM_ESSENTIALS = {
+    "fuel_petrol": "$1.58/L (Blend)",
+    "fuel_diesel": "$1.65/L (Diesel 50)",
+    "zesa_electricity": "Tiered: First 50 units cheap, then ~14c/kWh",
+    "bread_standard": "$1.00/loaf (Fixed)",
+    "gas_lpg": "$1.90 - $2.10 per kg"
+}
+
 # =========================
 # Helpers: time / strings
 # =========================
@@ -420,6 +402,27 @@ def gemini_generate_text(system: str, user: str, temperature: float = 0.4) -> st
         logger.error("Gemini text error: %s", e)
         return ""
 
+def gemini_generate_json(system: str, user: str, images: List = None) -> Dict[str, Any]:
+    """NEW: Strict JSON generation for reliable Plan/Intent"""
+    if not _gemini_client: return {}
+    parts = [{"text": system + "\n\n" + user}]
+    for img in images or []:
+        if "bytes" in img:
+            b64 = base64.b64encode(img["bytes"]).decode("utf-8")
+            parts.append({"inline_data": {"mime_type": img["mime"], "data": b64}})
+        elif "url" in img:
+            parts.append({"text": f"Image URL: {img['url']}"})
+    try:
+        resp = _gemini_client.models.generate_content(
+            model=GEMINI_MODEL,
+            contents=[{"role": "user", "parts": parts}],
+            config={"temperature": 0.2, "response_mime_type": "application/json", "max_output_tokens": 2000}
+        )
+        return json.loads(resp.text)
+    except Exception as e:
+        logger.error("Gemini JSON error: %s", e)
+        return {}
+
 def gemini_generate_multimodal(system: str, user: str, images: List[Dict[str, Any]]) -> str:
     """
     Uses Gemini multimodal:
@@ -478,6 +481,7 @@ Output schema:
     "product_discovery",
     "trust_check",
     "chit_chat",
+    "lifestyle_lookup",
     "other"
   ],
   "items": [{"name": "...", "quantity": 1}],
@@ -489,14 +493,31 @@ Rules:
 - If user is chatting/social (hi, jokes, thanks, how are you, etc) => actionable=false, intent="chit_chat".
 - If user asks about prices/stores/basket/what to buy => actionable=true.
 - If user provided a list, extract items + quantities if obvious.
+- "How much is fuel/zesa/bread" -> lifestyle_lookup
 - Keep it conservative: if unclear, actionable=false.
 """
 
 def detect_intent(message: str, images_present: bool, context: Dict[str, Any]) -> Dict[str, Any]:
+    # 1. Fast path for ZIM_ESSENTIALS (Optimization)
+    msg_lower = message.lower()
+    for k in ZIM_ESSENTIALS:
+        clean_k = k.split('_')[-1] # fuel_petrol -> petrol
+        if clean_k in msg_lower and "price" in msg_lower:
+            return {"actionable": True, "intent": "lifestyle_lookup", "items": [{"name": k}]}
+            
+    # 2. Gemini Detection
     ctx_str = json.dumps(context or {}, ensure_ascii=False)
     user = f"Message: {message}\nImagesPresent: {images_present}\nContext: {ctx_str}"
-    out = gemini_generate_text(INTENT_SYSTEM, user, temperature=0.1)
-    data = _safe_json_loads(out, fallback={})
+    
+    # Try using the strict JSON helper first for better reliability
+    try:
+        data = gemini_generate_json(INTENT_SYSTEM, user)
+        if not isinstance(data, dict): raise ValueError("Invalid JSON")
+    except:
+        # Fallback to text parsing if JSON mode fails (Backward Compat)
+        out = gemini_generate_text(INTENT_SYSTEM, user, temperature=0.1)
+        data = _safe_json_loads(out, fallback={})
+
     if not isinstance(data, dict):
         return {"actionable": False, "intent": "other", "items": [], "constraints": {}, "notes": "bad_json"}
     # normalize
@@ -506,6 +527,30 @@ def detect_intent(message: str, images_present: bool, context: Dict[str, Any]) -
     data.setdefault("constraints", {})
     return data
 
+# =========================
+# Shopping Plan Generator (NEW)
+# =========================
+PLAN_SYSTEM_PROMPT = """
+You are Jessica, the Pricelyst Shopping Advisor. Analyze the conversation transcript.
+If the user discussed a shopping list, budget plan, or event needs, create a structured plan.
+
+OUTPUT JSON SCHEMA:
+{
+  "is_actionable": boolean,
+  "title": "Short title (e.g. 'Weekend Braai List')",
+  "summary": "1 sentence summary",
+  "items": [{"name": "string", "qty": "string", "est_price": number|null}],
+  "markdown_content": "A clean Markdown report for a PDF. Include headers (#), bullet points, and a budget summary table if applicable. Keep it professional."
+}
+
+If no shopping/planning occurred, set is_actionable=false.
+"""
+
+def generate_shopping_plan(transcript: str) -> Dict[str, Any]:
+    if not transcript or len(transcript) < 30:
+        return {"is_actionable": False}
+    return gemini_generate_json(PLAN_SYSTEM_PROMPT, f"TRANSCRIPT:\n{transcript}")
+
 # =========================
 # Matching + analytics
 # =========================
@@ -759,34 +804,6 @@ def extract_items_from_images(images: List[Dict[str, Any]]) -> Dict[str, Any]:
     data.setdefault("items", [])
     return data
 
-# =========================
-# Post-call report synthesis (only if actionable)
-# =========================
-CALL_REPORT_SYSTEM = """
-You are Pricelyst AI. You will receive a full call transcript.
-Decide whether there is an actionable request (party planning, shopping needs, budgeting, groceries, etc).
-If actionable, produce a concise MARKDOWN report that the client can turn into a PDF.
-If NOT actionable (just chatting), return an empty string.
-
-Rules:
-- Be practical and Zimbabwe-oriented.
-- If planning an event: include (1) Assumptions, (2) Shopping list with quantities, (3) Budget ranges, (4) Simple menu/recipe ideas, (5) Optional restaurant/catering suggestions (generic; do NOT invent addresses).
-- Only output Markdown or empty string. No code blocks.
-"""
-
-def build_call_report_markdown(transcript: str) -> str:
-    if not transcript or len(transcript.strip()) < 40:
-        return ""
-    md = gemini_generate_text(CALL_REPORT_SYSTEM, transcript, temperature=0.3)
-    md = (md or "").strip()
-    # Guardrail: if model returns JSON or obvious nonsense, drop it.
-    if md.startswith("{") or md.startswith("["):
-        return ""
-    # Conservative: must contain at least one list bullet or heading to be “report-like”
-    if ("#" not in md) and ("- " not in md) and ("* " not in md):
-        return ""
-    return md
-
 # =========================
 # Routes
 # =========================
@@ -854,7 +871,21 @@ def chat():
 
     response_payload: Dict[str, Any] = {"type": "unknown", "message": "No result."}
 
-    if intent["intent"] in ("price_lookup", "trust_check", "product_discovery"):
+    # --- NEW: Check for Lifestyle/Essentials (Fuel/ZESA) ---
+    if intent["intent"] == "lifestyle_lookup":
+        # Items are auto-detected in detect_intent
+        key = intent["items"][0]["name"]
+        # Fuzzy match dict key
+        val = ZIM_ESSENTIALS.get(key) or ZIM_ESSENTIALS.get("fuel_petrol") # fallback
+        response_payload = {
+            "type": "info_card",
+            "title": f"Market Rate: {key.replace('_', ' ').title()}",
+            "message": str(val),
+            "highlights": [f"Current: {val}"]
+        }
+
+    # --- Original Logic ---
+    elif intent["intent"] in ("price_lookup", "trust_check", "product_discovery"):
         # pick first item or treat message as query
         query = ""
         if intent.get("items"):
@@ -961,18 +992,23 @@ def call_briefing():
     for ii in intents:
         intent_counts[ii] = intent_counts.get(ii, 0) + 1
 
-    shopping_intelligence = {
+    # --- KPI Snapshot Logic ---
+    # We construct a dictionary that the React client will pass as a JSON string
+    # We inject ZIM_ESSENTIALS here so the Agent has knowledge of fuel/zesa prices
+    kpi_data = {
         "username": prof.get("username") or "there",
         "last_best_store": last_store,
         "top_intents_last_25": sorted(intent_counts.items(), key=lambda x: x[1], reverse=True)[:5],
         "tone": "practical_zimbabwe",
+        "market_rates_essentials": ZIM_ESSENTIALS  # <--- INJECTED KNOWLEDGE
     }
 
     return jsonify({
         "ok": True,
         "profile_id": profile_id,
         "memory_summary": prof.get("memory_summary", ""),
-        "shopping_intelligence": shopping_intelligence
+        # This string is passed to ElevenLabs by the React Client
+        "kpi_snapshot": json.dumps(kpi_data) 
     })
 
 @app.post("/api/log-call-usage")
@@ -990,23 +1026,30 @@ def log_call_usage():
 
     prof = get_profile(profile_id)
 
-    # Conservative “actionable report” generation:
-    # - only generate if transcript has planning keywords
-    # - and Gemini returns report-ish markdown
-    planning_keywords = ["party", "birthday", "wedding", "braai", "grocer", "basket", "shopping", "budget", "ingredients", "recipe", "cook", "drinks", "snacks"]
-    looks_planning = any(k in transcript.lower() for k in planning_keywords)
-
+    # --- UPGRADE: Use Shopping Plan Generator (JSON + Markdown) ---
+    plan_data = generate_shopping_plan(transcript)
+    plan_id = None
     report_md = ""
-    if looks_planning and _gemini_client:
-        report_md = build_call_report_markdown(transcript)
 
+    if plan_data.get("is_actionable"):
+        # Save structured plan
+        plan_ref = db.collection("pricelyst_profiles").document(profile_id).collection("shopping_plans").document()
+        plan_data["id"] = plan_ref.id
+        plan_data["call_id"] = call_id
+        plan_data["created_at"] = now_utc_iso()
+        plan_ref.set(plan_data)
+        plan_id = plan_ref.id
+        report_md = plan_data.get("markdown_content", "")
+
+    # Log the call (link the plan_id)
     doc_id = log_call(profile_id, {
         "call_id": call_id,
         "started_at": started_at,
         "ended_at": ended_at,
         "stats": stats,
         "transcript": transcript,
-        "report_markdown": report_md,
+        "generated_plan_id": plan_id,
+        "report_markdown": report_md, 
     })
 
     # update counters
@@ -1016,9 +1059,37 @@ def log_call_usage():
     return jsonify({
         "ok": True,
         "logged_call_doc_id": doc_id,
-        "report_markdown": report_md  # client turns this into PDF; empty if non-actionable
+        "shopping_plan": plan_data if plan_id else None # Frontend uses this for PDF
     })
 
+# --- NEW: Shopping Plans CRUD ---
+@app.get("/api/shopping-plans")
+def list_plans():
+    pid = request.args.get("profile_id")
+    if not pid: return jsonify({"ok": False}), 400
+    try:
+        docs = db.collection("pricelyst_profiles").document(pid).collection("shopping_plans")\
+                 .order_by("created_at", direction=firestore.Query.DESCENDING).limit(20).stream()
+        plans = [{"id": d.id, **d.to_dict()} for d in docs]
+        return jsonify({"ok": True, "plans": plans})
+    except Exception as e:
+        return jsonify({"ok": False, "error": str(e)}), 500
+
+@app.get("/api/shopping-plans/<plan_id>")
+def get_plan(plan_id):
+    pid = request.args.get("profile_id")
+    if not pid: return jsonify({"ok": False}), 400
+    doc = db.collection("pricelyst_profiles").document(pid).collection("shopping_plans").document(plan_id).get()
+    if not doc.exists: return jsonify({"ok": False, "error": "Not found"}), 404
+    return jsonify({"ok": True, "plan": doc.to_dict()})
+
+@app.delete("/api/shopping-plans/<plan_id>")
+def delete_plan(plan_id):
+    pid = request.args.get("profile_id")
+    if not pid: return jsonify({"ok": False}), 400
+    db.collection("pricelyst_profiles").document(pid).collection("shopping_plans").document(plan_id).delete()
+    return jsonify({"ok": True})
+
 # =========================
 # Run
 # =========================