app.py

import json
import joblib
import pandas as pd
from flask import Flask, request, jsonify

app = Flask("SuperKart Predictor")
model = joblib.load("model.joblib")
with open("model.json", "r") as f:
    model_info = json.load(f)


@app.get("/")
def home():
    return app.response_class(response=json.dumps(model_info, indent=4), status=200, mimetype="application/json")


@app.post("/v1/product")
def predict():
    product_data = request.get_json()
    sample = {
        "Product_Id": product_data["Product_Id"],
        "Product_Weight": product_data["Product_Weight"],
        "Product_Sugar_Content": product_data["Product_Sugar_Content"],
        "Product_Allocated_Area": product_data["Product_Allocated_Area"],
        "Product_Type": product_data["Product_Type"],
        "Product_MRP": product_data["Product_MRP"],
        "Store_Id": product_data["Store_Id"],
        "Store_Establishment_Year": product_data["Store_Establishment_Year"],
        "Store_Size": product_data["Store_Size"],
        "Store_Location_City_Type": product_data["Store_Location_City_Type"],
        "Store_Type": product_data["Store_Type"],
    }
    input_data = pd.DataFrame([sample])
    prediction = model.predict(input_data).tolist()[0]
    return jsonify({"Prediction": prediction})


@app.post("/v1/productbatch")
def predict_batch():
    file = request.files["file"]
    input_data = pd.read_csv(file)
    predictions = model.predict(input_data).tolist()
    input_data["Prediction"] = predictions
    result = input_data.to_dict(orient="records")
    return jsonify(result)


if __name__ == "__main__":
    app.run(debug=True)