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import joblib
import pandas as pd
from flask import Flask, request, jsonify
# Initialize Flask app
app = Flask(__name__)
# Load the trained sales forecasting model pipeline
model = joblib.load("backend_files/final_sales_forecasting_model.joblib") # Adjust path as needed
# Define a route for the home page
@app.route('/')
def home():
return "Welcome to the SuperKart Sales Forecasting API"
# Define an endpoint to predict sales for a single product-store combination
@app.route('/predict_single', methods=['POST'])
def predict_single():
# Get JSON data from the request
data = request.get_json()
# Extract relevant features from the input data, ensuring correct order and names
# The keys in the dictionary should match the column names in your original training data X
try:
sample = {
'Product_Id': data['Product_Id'],
'Product_Weight': data['Product_Weight'],
'Product_Sugar_Content': data['Product_Sugar_Content'],
'Product_Allocated_Area': data['Product_Allocated_Area'],
'Product_Type': data['Product_Type'],
'Product_MRP': data['Product_MRP'],
'Store_Id': data['Store_Id'],
'Store_Establishment_Year': data['Store_Establishment_Year'],
'Store_Size': data['Store_Size'],
'Store_Location_City_Type': data['Store_Location_City_Type'],
'Store_Type': data['Store_Type']
}
# Convert the extracted data into a DataFrame
input_data = pd.DataFrame([sample])
# Make a sales prediction using the trained model pipeline
prediction = model.predict(input_data).tolist()[0]
# Return the prediction as a JSON response
return jsonify({'predicted_sales': prediction})
except KeyError as e:
return jsonify({'error': f'Missing data for key: {e}'}), 400
except Exception as e:
return jsonify({'error': str(e)}), 500
# Define an endpoint to predict sales for a batch of product-store combinations from a CSV file
@app.route('/predict_batch', methods=['POST'])
def predict_batch():
# Get the uploaded file from the request
if 'file' not in request.files:
return jsonify({'error': 'No file part in the request'}), 400
file = request.files['file']
# If the user does not select a file, the browser submits an empty file without a filename.
if file.filename == '':
return jsonify({'error': 'No selected file'}), 400
if file:
try:
# Read the file into a DataFrame
input_data = pd.read_csv(file)
# Make sales predictions using the trained model pipeline
predictions = model.predict(input_data).tolist()
# Return the predictions as a JSON response
return jsonify({'predicted_sales': predictions})
except Exception as e:
return jsonify({'error': str(e)}), 500
else:
return jsonify({'error': 'Something went wrong with file upload'}), 500
if __name__ == '__main__':
# Run the Flask app
app.run(debug=True, host='0.0.0.0', port=5000) # Run on all available interfaces and port 5000
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