File size: 3,327 Bytes
5c8db15 2fa985b 5c8db15 2fa985b 5c8db15 2fa985b 5c8db15 2fa985b 5c8db15 2fa985b 5c8db15 2fa985b 5c8db15 2fa985b 5c8db15 2fa985b 5c8db15 4767f39 5c8db15 4767f39 5192ef7 5c8db15 4767f39 5c8db15 4767f39 5c8db15 5192ef7 |
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 |
import joblib
import pandas as pd
from flask import Flask, request, jsonify
# Initialize Flask app with a name
superkart_product_sales_prediction_api = Flask("SuperKart Sales Forecast API")
# Load the trained churn prediction model
model = joblib.load("superkart_product_sales_prediction_model_v1_0.joblib")
# Define a route for the home page
@superkart_product_sales_prediction_api.get('/')
def home():
return "Welcome to the SuperKart Sales Forecast API"
# Define an endpoint to predict churn for a single customer
@superkart_product_sales_prediction_api.post('/v1/forecast')
def predict_sales_forecast():
"""
This function handles POST requests to the '/v1/forecast' endpoint.
It expects a JSON payload containing property details and returns
the predicted rental price as a JSON response.
"""
# Get JSON data from the request
product_data = request.get_json()
# Extract relevant customer features from the input data
sample = {
'Product_Weight': product_data.get('Product_Weight'),
'Product_Sugar_Content': product_data.get('Product_Sugar_Content'),
'Product_Allocated_Area': product_data.get('Product_Allocated_Area'),
'Product_Type': product_data.get('Product_Type'),
'Product_MRP': product_data.get('Product_MRP'),
'Store_Id': product_data.get('Store_Id'),
'Store_Establishment_Year': product_data.get('Store_Establishment_Year'),
'Store_Size': product_data.get('Store_Size'),
'Store_Location_City_Type': product_data.get('Store_Location_City_Type'),
'Store_Type': product_data.get('Store_Type')
}
# Convert the extracted data into a DataFrame
input_data = pd.DataFrame([sample])
# Make a churn prediction using the trained model
predicted_sales = model.predict(input_data).tolist()[0]
# Convert predicted_price to Python float
predicted_sales = round(float(predicted_sales), 2)
# Return the prediction as a JSON response
return jsonify({'Prediction': predicted_sales})
@superkart_product_sales_prediction_api.post('/v1/forecastbatch')
def predict_sales_forecast_batch():
"""
This function handles POST requests to the '/v1/forecastbatch' endpoint.
It expects a CSV file containing product and store details for multiple products
and returns the predicted sales forecast prices as a dictionary in the JSON response.
"""
# Get the uploaded CSV file from the request
file = request.files['file']
# Read the CSV file into a Pandas DataFrame
input_data = pd.read_csv(file)
#print(input_data.loc[0])
# Make predictions for all products in the DataFrame (get sales prices)
predicted_sales = model.predict(input_data).tolist()
# Convert predicted_price to Python float
predicted_sales = [round(float(price), 2) for price in predicted_sales]
#print(predicted_sales)
# Create a dictionary of predictions with property IDs as keys
product_ids = input_data['Product_Id'].tolist() # Assuming 'Product_Id' is the product_id ID column
output_dict = dict(zip(product_ids, predicted_sales)) # Use actual prices
# Return the predictions dictionary as a JSON response
return output_dict
# Run the Flask app in debug mode
if __name__ == '__main__':
superkart_product_sales_prediction_api.run(debug=True)
|