main.py

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import joblib
import pandas as pd
import numpy as np
import os

# Initialize FastAPI app
app = FastAPI(
    title="Outlet Performance Predictor API",
    description="API for predicting outlet performance based on item details.",
    version="1.0.0",
)

# --- 1. Load the Trained Model ---
MODEL_PATH = 'AAPOutletFinalfinal.joblib'
model_pipeline = None # Initialize to None

try:
    if os.path.exists(MODEL_PATH):
        model_pipeline = joblib.load(MODEL_PATH)
        print("Model pipeline loaded successfully!")
    else:
        print(f"Error: Model file not found at {MODEL_PATH}. Prediction service will be unavailable.")
except Exception as e:
    print(f"Error loading model pipeline: {e}. Prediction service will be unavailable.")


# --- 2. Define Input Data Schema ---
class ItemFeatures(BaseModel):
    Item_Identifier: str # This field will be removed before passing to the model
    Item_Weight: float
    Item_Fat_Content: str
    Item_Visibility: float
    Item_Type: str
    Item_MRP: float
    Outlet_Identifier: str
    Outlet_Establishment_Year: int
    Outlet_Size: str = None
    Outlet_Location_Type: str
    Outlet_Type: str

# --- 3. Define the Prediction Endpoint ---
@app.post("/predict/outlet_performance")
async def predict_outlet_performance(item: ItemFeatures):
    if model_pipeline is None:
        raise HTTPException(status_code=500, detail="Model pipeline could not be loaded on server startup. Service unavailable.")

    try:
        input_data_dict = item.model_dump()

        # --- IMPORTANT: Remove Item_Identifier before creating DataFrame for the model ---
        item_identifier_value = input_data_dict.pop('Item_Identifier')

        input_df = pd.DataFrame([input_data_dict])

        # --- Feature Engineering for Inference (as in your notebook) ---
        input_df['Outlet_Age'] = 2010 - input_df['Outlet_Establishment_Year']
        input_df['Item_Sales_Deviation'] = 0.0 # Placeholder for new, single items

        # --- DIAGNOSTIC PRINTS (FOR DEBUGGING) ---
        print("\n--- DataFrame sent to pipeline (input_df) ---")
        print(input_df.head())
        print(input_df.dtypes)
        print("-------------------------------------------\n")
        # --- END DIAGNOSTIC PRINTS ---

        prediction_numeric = model_pipeline.predict(input_df)

        label_map = {0: 'Poor', 1: 'Medium', 2: 'Good'}
        predicted_label = label_map.get(prediction_numeric[0], "Unknown")

        return {"predicted_outlet_performance": predicted_label}

    except KeyError as e:
        print(f"Prediction input data error: Missing expected feature - {e}")
        raise HTTPException(status_code=400, detail=f"Invalid input data: A required feature is missing or misnamed - {e}")
    except ValueError as e:
        print(f"Prediction pipeline error: {e}")
        raise HTTPException(status_code=422, detail=f"Data validation or transformation error during prediction: {e}. Check input values and categories.")
    except Exception as e:
        print(f"An unexpected error occurred during prediction: {e}")
        raise HTTPException(status_code=500, detail=f"Internal Server Error during prediction: {e}")

# --- Optional: Root endpoint for API health check ---
@app.get("/")
async def root():
    return {"message": "Outlet Performance Predictor API is running! Access docs at /docs"}

# Commands to run
# 1. pip install "fastapi[standard]" or conda install "fastapi[standard]"
# 2. fastapi dev main.py
# You will be able to find your docs at https://127.0.0.1/docs/

@app.get("/api/get_result")
def predict(request):
    data = request.json()
    postal_code = data["postal_code"]
    return {"response": "Hello!"}