app.py

# # Import necessary libraries
# import numpy as np
# import joblib  # For loading the serialized model
# import pandas as pd  # For data manipulation
# from flask import Flask, request, jsonify  # For creating the Flask API

# # Initialize the Flask application
# superkart_sales_api = Flask("SuperKart Sales Predictor")

# # Load the trained machine learning model
# model = joblib.load("superkart_sales_prediction_model_v1_0.joblib")

# # Define a route for the home page (GET request)
# @superkart_sales_api.get('/')
# def home():
#     return "Welcome to the SuperKart Sales Prediction API!"

# # Define an endpoint for single product prediction (POST request)
# @superkart_sales_api.post('/v1/sales')
# def predict_sales():
#     product_data = request.get_json()

#     sample = {
#         '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_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])
#     predicted_sales = model.predict(input_data)[0]

#     return jsonify({'Predicted Sales (in INR)': round(float(predicted_sales), 2)})

# # Define an endpoint for batch prediction (POST request)
# @superkart_sales_api.post('/v1/salesbatch')
# def predict_sales_batch():
#     file = request.files['file']
#     input_data = pd.read_csv(file)

#     predicted_sales = model.predict(input_data).tolist()
#     product_ids = input_data['Product_Id'].tolist()  # Assuming 'Product_Id' column exists

#     output_dict = dict(zip(product_ids, [round(float(p), 2) for p in predicted_sales]))

#     return jsonify(output_dict)

# # Run the Flask application in debug mode
# if __name__ == '__main__':
#     superkart_sales_api.run(debug=True)

from flask import Flask, request, jsonify
from flask_cors import CORS
import pandas as pd

app = Flask(__name__)
CORS(app)

# Dummy model logic for demo purposes
def predict_sales(features):
    # Replace this logic with your trained model
    return round(
        features.get("Product_MRP", 100) * 1.5 +
        features.get("Product_Allocated_Area", 0.1) * 2000 -
        features.get("Product_Weight", 0.0) * 5,
        2
    )

@app.route("/")
def health():
    return "✅ SuperKart Sales Prediction API is running."

@app.route("/v1/sales", methods=["POST"])
def online_prediction():
    data = request.get_json()
    prediction = predict_sales(data)
    return jsonify({"predicted_sales": prediction})

@app.route("/v1/salesbatch", methods=["POST"])
def batch_prediction():
    if "file" not in request.files:
        return jsonify({"error": "CSV file missing"}), 400

    file = request.files["file"]
    try:
        df = pd.read_csv(file)
        results = {
            str(i): predict_sales(row)
            for i, row in df.iterrows()
        }
        return jsonify(results)
    except Exception as e:
        return jsonify({"error": str(e)}), 500