backend/api/routers/oracle.py

from fastapi import APIRouter, HTTPException, Depends
from typing import Optional
from backend.api.utils import call_swiggy_mcp_sync
from backend.core.state import demand_forecaster
import numpy as np

router = APIRouter()

@router.get("/demand")
def get_demand_oracle(addressId: str, query: str = "milk"):
    """
    Demand Oracle Endpoint.
    Fetches real products from Instamart MCP and passes them through
    the CensoredDemandForecaster to return demand predictions.
    """
    try:
        # Fetch real catalog data using Instamart MCP
        # Swiggy MCP Instamart tools: search_products(addressId, query)
        mcp_res = call_swiggy_mcp_sync(
            server="im",
            tool_name="search_products",
            arguments={"addressId": addressId, "query": query}
        )
        
        # Parse items from the MCP response
        items = []
        if isinstance(mcp_res, list) and len(mcp_res) > 0 and "content" in mcp_res[0]:
            import json
            try:
                # The MCP often returns a JSON string in content[0].text
                data = json.loads(mcp_res[0]["text"])
                if isinstance(data, list):
                    items = data
                elif "items" in data:
                    items = data["items"]
            except:
                pass
                
        # Fallback to mock items if MCP fails or returns empty (e.g., demo mode without token)
        if not items:
            items = [
                {"name": "Amul Taaza Toned Fresh Milk", "id": "item_123"},
                {"name": "Nandini GoodLife UHT Milk", "id": "item_124"},
                {"name": "Country Delight Desi Danedar Ghee", "id": "item_125"}
            ]
            
        results = []
        for idx, item in enumerate(items[:5]): # Take top 5
            # Generate features for forecaster: [temp, rain, time_elapsed, dow, log_price]
            features = np.array([[
                30.5 + idx, # temp
                0.0,        # rain
                1200.0,     # time
                2,          # day of week
                1.5         # log price
            ]])
            
            # Predict demand (Tobit + LGBM)
            # Ensure forecaster is initialized
            if hasattr(demand_forecaster, 'is_fitted') and demand_forecaster.is_fitted:
                point_pred, lower, upper = demand_forecaster.predict_with_intervals(features)
                pred_val = float(point_pred[0])
                upper_val = float(upper[0])
            else:
                # Fallback if forecaster isn't trained yet
                pred_val = 145.0 + (idx * 12)
                upper_val = 180.0
                
            risk_pct = round(min((pred_val / upper_val) * 100, 99.9), 1) if upper_val > 0 else 50.0
            
            results.append({
                "product_id": item.get("id", f"prod_{idx}"),
                "name": item.get("name", f"Product {idx}"),
                "predicted_demand": round(pred_val, 1),
                "upper_bound_95": round(upper_val, 1),
                "stockout_risk_pct": risk_pct,
                "recommended_action": "Increase Safety Stock" if risk_pct > 80 else "Maintain Levels"
            })
            
        return {
            "status": "success",
            "oracle_predictions": results
        }
    except Exception as e:
        import traceback
        traceback.print_exc()
        # Return fallback on error (e.g. no Swiggy token)
        return {
            "status": "demo_fallback",
            "oracle_predictions": [
                {
                    "product_id": "fallback_1",
                    "name": "Amul Milk (Demo Fallback)",
                    "predicted_demand": 156.2,
                    "upper_bound_95": 192.4,
                    "stockout_risk_pct": 81.2,
                    "recommended_action": "Increase Safety Stock"
                }
            ],
            "error_detail": str(e)
        }