gowdhamankarthikeyan/GL_Backend

1import joblib2import pandas as pd3from flask import Flask, request, jsonify4 5# Initialize Flask app6SuperKartSales_ForecastModel = Flask("SuperKartSales_ForecastModel")7 8# Load the trained Boston housing model9model = joblib.load("SuperKartSales_ForecastModel_v1_0.joblib")10 11# Define a route for the home page12@SuperKartSales_ForecastModel.get('/')13def home():14    return "Welcome to the SuperKart sales forecast!"15 16# Define an endpoint to predict price for a Single Store Sales17@SuperKartSales_ForecastModel.post('/v1/sales')18def predict_sales():19    # Get JSON data from the request20    sales_data = request.get_json()21 22    # Extract relevant house features from the input data23    sample = {24        'Product_Id': sales_data['Product_Id'],25        'Product_Weight': sales_data['Product_Weight'],26        'Product_Sugar_Content': sales_data['Product_Sugar_Content'],27        'Product_Allocated_Area': sales_data['Product_Allocated_Area'],28        'Product_Type': sales_data['Product_Type'],29        'Product_MRP': sales_data['Product_MRP'],30        'Store_Id': sales_data['Store_Id'],31        'Store_Establishment_Year': sales_data['Store_Establishment_Year'],32        'Store_Size': sales_data['Store_Size'],33        'Store_Location_City_Type': sales_data['Store_Location_City_Type'],34        'Store_Type': sales_data['Store_Type']35    }36 37    # Convert the extracted data into a DataFrame38    input_data = pd.DataFrame([sample])39 40    # Make a prediction using the trained model41    prediction = model.predict(input_data).tolist()[0]42 43    # Return the prediction as a JSON response44    return jsonify({'Predicted Store Sales': prediction})45 46# Define an endpoint to predict sales for batch of input47@SuperKartSales_ForecastModel.post('/v1/salesbatch')48def predict_sales_forecast_batch():49    # Get the uploaded CSV file from the request50    file = request.files['file']51 52    # Read the file into a DataFrame53    input_data = pd.read_csv(file)54 55    # Make predictions for the batch data56    predictions = model.predict(input_data).tolist()57 58    # Add predictions to the DataFrame59    input_data['Predicted_Store_Sales'] = predictions60 61    # Convert results to dictionary62    result = input_data.to_dict(orient="records")63 64    return jsonify(result)65 66# Run the Flask app in debug mode67if __name__ == '__main__':68    SuperKartSales_ForecastModel.run(debug=True)69