app.py

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

app = Flask("Superkart Sales Predictor")

# Load the trained churn prediction model
model = joblib.load("superkart_sales_prediction_model_v1_0.joblib")

@app.get('/')
#def hello_world():
#    return "Hello, World from Hugging Face Space!"
def home():
    return "Welcome to the Superkart Sales Prediction API !"

#@app.post('/v1/Product')

@app.route('/v1/Product', methods=['POST'])
def predict_churn():
    # Get JSON data from the request
    product_data = request.get_json()

    # Extract relevant customer features from the input data
    
    sample = {
        'Product_Weight':product_data['Product_Weight'],
        'Product_Allocated_Area':product_data['Product_Allocated_Area'],
        'Product_MRP':product_data['Product_MRP'],
        'Store_Establishment_Year':product_data['Store_Establishment_Year'],
        'Product_Sugar_Content':product_data['Product_Sugar_Content'],
        'Product_Type':product_data['Product_Type'],
        'Store_Id':product_data['Store_Id'],
        'Store_Size':product_data['Store_Size'],
        'Store_Location_City_Type':product_data['Store_Location_City_Type'],
        'Store_Type':product_data['Store_Type']
    }

    # Convert the extracted data into a DataFrame
    input_data = pd.DataFrame([sample])

    # Make a churn prediction using the trained model
    prediction = model.predict(input_data).tolist()[0]

    # Map prediction result to a human-readable label
    prediction_value = prediction

    # Return the prediction as a JSON response
    return jsonify({'Prediction': prediction_value})

# Define an endpoint to predict churn for a batch of customers
@app.route('/v1/Productbatch', methods=['POST'])
def predict_churn_batch():
    # Get the uploaded CSV file from the request
    file = request.files['file']

    # Read the file into a DataFrame
    input_data = pd.read_csv(file)

    # Make predictions for the batch data and convert raw predictions into a readable format
    predictions = model.predict(input_data.drop("Product_Id",axis=1)).tolist()

    product_id_list = input_data.Product_Id.values.tolist()
    output_dict = dict(zip(product_id_list, predictions))

    return output_dict
    
if __name__ == '__main__':
    app.run(debug=True)