import joblib import numpy as np import pandas as pd from flask import Flask, request, jsonify super_kart_api=Flask("Superkart_price_prediction") model=joblib.load('model.joblib') @super_kart_api.get('/') def home(): return "Welcome to SuperKart sales Prediction" @super_kart_api.post('/v1/spkart_single') def sale_pred_single(): sale_data=request.get_json() sample={ 'Product_Weight':sale_data['Product_Weight'], 'Product_Sugar_Content':sale_data['Product_Sugar_Content'], 'Product_Allocated_Area':sale_data['Product_Allocated_Area'], 'Product_Type':sale_data['Product_Type'], 'Product_MRP':sale_data['Product_MRP'], 'Store_Id':sale_data['Store_Id'], 'Store_Size':sale_data['Store_Size'], 'Store_Location_City_Type':sale_data['Store_Location_City_Type'], 'Store_Type':sale_data['Store_Type'], 'Store_age':sale_data['Store_age'] } input_data=pd.DataFrame([sample]) predicted_sale=model.predict(input_data)[0] response={'Store_Outlet':sample['Store_Id'],"Sale":round(float(predicted_sale), 2)} return jsonify(response) @super_kart_api.post('/v1/spkart_batch') def sale_pred_batch(): file = request.files['file'] print("File Received:", file.filename) # Read input data input_data = pd.read_csv(file) print("Input Data Shape:", input_data.shape) print(input_data.head()) # Make predictions predicted_sale = model.predict(input_data).tolist() print("Predicted Sales Length:", len(predicted_sale)) # Add predictions to input data input_data['Predicted_Sale'] = predicted_sale print("Input Data with Predictions:", input_data.head()) # Group by Store_Id and sum the predicted sales grouped_sales = input_data.groupby('Store_Id')['Predicted_Sale'].sum().to_dict() print("Grouped Sales:", grouped_sales) # Create response response = { 'store_sales': {store_id: round(float(sale), 2) for store_id, sale in grouped_sales.items()} } print("Final Response:", response) return jsonify(response) if __name__=='__main__': super_kart_api.run()