bk_app.py

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

# Initialize Flask app with a name
app_run = Flask("Superkart Store Sale Predictor")

# Load the trained churn prediction model
model = joblib.load("D:\\AI_ML\\Model_Deployment\\Model_deployment_project\\deployment_files\\superkart_model_deployement.joblib")

# Define a route for the home page
@app_run.get('/')
def home():
    return "Welcome to the Superkart Store Sale Predictor API"

# Define an endpoint to predict churn for a single customer
@app_run.post('/v1/store_sale')
def predict_sales():
    # Get JSON data from the request
    customer_data = request.get_json()

    # Extract relevant customer features from the input data
    sample = {
        'Product_Weight': customer_data['Product_Weight'],
        'Product_Sugar_Content': customer_data['Product_Sugar_Content'],
        'Product_Allocated_Area': customer_data['Product_Allocated_Area'],
        'Product_Type': customer_data['Product_Type'],
        'Product_MRP': customer_data['Product_MRP'],
        'Store_Id': customer_data['Store_Id'],
        'Store_Establishment_Year': customer_data['Store_Establishment_Year'],
        'Store_Size': customer_data['Store_Size'],
        'Store_Location_City_Type': customer_data['Store_Location_City_Type'],
        'Store_Type': customer_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_label = "churn" if prediction == 1 else "not churn"
    # Predict button

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

# Define an endpoint to predict churn for a batch of customers
@app_run.post('/v1/customerbatch')
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 = [
    #    'Churn' if x == 1
    #    else "Not Churn"
    #    for x in model.predict(input_data.drop("customerID",axis=1)).tolist()
    #]
    predictions = [
        for x in 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

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