File size: 2,306 Bytes
5fc1237 a64d62c 5fc1237 a64d62c 5fc1237 a64d62c |
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 |
import joblib
import pandas as pd
from flask import Flask, request, jsonify
# Initialize Flask app with a name
sales_forecast_api = Flask("Sales Forecasting")
# Load the trained churn prediction model
model = joblib.load("sales_forecast_model.joblib")
# Define a route for the home page
@sales_forecast_api.get('/')
def home():
return "Welcome to the Sales Forecast API!"
# Define an endpoint to predict churn for a single customer
@sales_forecast_api.post('/v1/customer')
def predict_sales():
# Get JSON data from the request
product_data = request.get_json()
# Extract relevant customer 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_Id': product_data['Store_Id'],
'Store_Establishment_Year': product_data['Store_Establishment_Year'],
'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 churn prediction using the trained model
prediction = model.predict(input_data).tolist()[0]
# Return the prediction as a JSON response
return jsonify({'Prediction': prediction})
# Define an endpoint to predict churn for a batch of customers
@sales_forecast_api.post('/v1/customerbatch')
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 and convert raw predictions into a readable format
predictions = model.predict(input_data.drop("Product_Id",axis=1)).tolist()
prod_id_list = input_data.Product_Id.values.tolist()
output_dict = dict(zip(prod_id_list, predictions))
return jsonify(output_dict)
# Run the Flask app in debug mode
if __name__ == '__main__':
sales_forecast_api.run(debug=True, host="0.0.0.0", port=7860)
|