app.py

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

# Initialize flask app
sales_predictor_api = Flask("Sales Predictor")

# Load the trained sales prediction model
model = joblib.load("sales_prediction_model_v1_0.joblib")

# Home route
@sales_predictor_api.get('/')
def home():
    return "Welcome to the Sales Prediction API"

# Predict for a single product
@sales_predictor_api.post('/v1/product')
def predict_sales():
    try:
        # Get JSON data from the request
        customer_data = request.get_json()

        # Extract relevant features
        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'],
            'Product_Id': customer_data['Product_Id']
        }

        # Convert to DataFrame
        input_data = pd.DataFrame([sample])

        # Extract ID prefix
        input_data["Id"] = input_data["Product_Id"].str[:2]
        input_data.drop("Product_Id", axis=1, inplace=True)

        # Predict
        prediction = model.predict(input_data).tolist()[0]

        return jsonify({"Prediction": prediction})

    # To catch errors
    except Exception as e:
        return jsonify({"error": str(e)}), 400

# Predict for a batch of products
@sales_predictor_api.post('/v1/productbatch')
def predict_sales_batch():
    try:
        # Get uploaded CSV
        file = request.files['file']
        input_data = pd.read_csv(file)

        # Extract ID prefix
        input_data["Id"] = input_data["Product_Id"].str[:2]

        # Making a save of the product ID in a list
        cust_id_list = input_data["Product_Id"].tolist()
        input_data.drop("Product_Id", axis=1, inplace=True)

        # Predict
        predictions = model.predict(input_data).tolist()

        # Stitch Product ID with the predictions
        output_dict = dict(zip(cust_id_list, predictions))

        return jsonify(output_dict)
    
    # To catch errors
    except Exception as e:
        return jsonify({"error": str(e)}), 400

# Run Flask app
if __name__ == "__main__":
    sales_predictor_api.run(debug=True)