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import joblib
import pandas as pd
from flask import Flask, request, jsonify
# Initialize Flask app with a name
app = Flask("Product Sales Predictor")
# Load the trained product sales prediction model
model = joblib.load("product_sales_predictor_v1_0.joblib")
# Define a route for the home page
@app.get('/')
def home():
return "Welcome to the Product Sales Prediction API"
# Define an endpoint to predict sales for a single product
@app.post('/v1/product')
def predict_sales():
# Get JSON data from the request
product_data = request.get_json()
# Extract relevant product features from the input data
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_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 sales prediction using the trained model
prediction = model.predict(input_data).tolist()[0]
# Return the prediction as a JSON response
return jsonify({'Predicted_Sales': prediction})
# Define an endpoint to predict sales for a batch of products
@app.post('/v1/productbatch')
def predict_sales_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
predictions = model.predict(input_data).tolist()
# Return the predictions as a JSON response
return jsonify({'Predicted_Sales': predictions})
# Run the Flask app in debug mode
if __name__ == '__main__':
app.run(debug=True)
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