File size: 3,921 Bytes
cf7a16c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 | import joblib
import pandas as pd
from flask import Flask, request, jsonify
import numpy as np
# Initialize Flask app
sales_forecast_api = Flask("SuperKart Sales Forecast API")
# Load the trained sales forecasting model
model = joblib.load("superkart_sales_forecast_model.pkl")
# Define a route for the home page
@sales_forecast_api.get('/')
def home():
return "Welcome to the SuperKart Sales Forecasting API! 🛒📊"
# Define an endpoint to predict sales for a single product-store combination
@sales_forecast_api.post('/v1/predict_sales')
def predict_sales():
try:
# Get JSON data from the request
input_data = request.get_json()
# Extract relevant features from the input data
sample = {
'Product_Weight': input_data['Product_Weight'],
'Product_Sugar_Content': input_data['Product_Sugar_Content'],
'Product_Allocated_Area': input_data['Product_Allocated_Area'],
'Product_Type': input_data['Product_Type'],
'Product_MRP': input_data['Product_MRP'],
'Store_Establishment_Year': input_data['Store_Establishment_Year'],
'Store_Size': input_data['Store_Size'],
'Store_Location_City_Type': input_data['Store_Location_City_Type'],
'Store_Type': input_data['Store_Type']
}
# Convert the extracted data into a DataFrame
input_df = pd.DataFrame([sample])
# Make sales prediction using the trained model
prediction = model.predict(input_df)[0]
# Convert NumPy float32 to Python float
prediction = float(prediction)
# Return the prediction as a JSON response
return jsonify({
'Product_Id': input_data.get('Product_Id', 'N/A'),
'Store_Id': input_data.get('Store_Id', 'N/A'),
'Predicted_Sales': round(prediction, 2),
'Currency': 'INR',
'Status': 'Success'
})
except Exception as e:
return jsonify({
'Error': str(e),
'Status': 'Failed'
}), 400
# Define an endpoint to predict sales for a batch of product-store combinations
@sales_forecast_api.post('/v1/predict_sales_batch')
def predict_sales_batch():
try:
# Get the uploaded CSV file from the request
file = request.files['file']
# Read the file into a DataFrame
input_data = pd.read_csv(file)
# Select only the required features for prediction
feature_columns = [
'Product_Weight', 'Product_Sugar_Content', 'Product_Allocated_Area',
'Product_Type', 'Product_MRP', 'Store_Establishment_Year',
'Store_Size', 'Store_Location_City_Type', 'Store_Type'
]
# Prepare data for prediction
prediction_data = input_data[feature_columns]
# Make predictions for the batch data
predictions = model.predict(prediction_data)
# Create output with Product_Id and Store_Id mapping
output_list = []
for i, (_, row) in enumerate(input_data.iterrows()):
prediction_entry = {
'Product_Id': row.get('Product_Id', f'Product_{i}'),
'Store_Id': row.get('Store_Id', f'Store_{i}'),
'Predicted_Sales': float(predictions[i])
}
output_list.append(prediction_entry)
return jsonify({
'Predictions': output_list,
'Total_Records': len(predictions),
'Currency': 'INR',
'Status': 'Success'
})
except Exception as e:
return jsonify({
'Error': str(e),
'Status': 'Failed'
}), 400
# Run the Flask app
if __name__ == '__main__':
sales_forecast_api.run(debug=True, host='0.0.0.0', port=7860) # Port 7860 for Hugging Face |