File size: 2,880 Bytes
9b8378b 06803ba 9b8378b 10be9f9 9b8378b e9d0c4f 9b8378b 10be9f9 9b8378b e9d0c4f 6ed0497 9b8378b | 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 | # Import necessary libraries
import numpy as np
import joblib # For loading the serialized model
import pandas as pd # For data manipulation
from flask import Flask, request, jsonify # For creating the Flask API
# Initialize the Flask application
sales_forecast_api = Flask("Sales Forecast Prediction API ")
# Load the trained churn prediction model
model = joblib.load("sales_forecast_model_v1_0.joblib")
# Define a route for the home page
@sales_forecast_api.get('/')
def home():
return "Welcome to the Sales Forecast Prediction API!"
# Define an endpoint to predict churn for a single customer
@sales_forecast_api.post('/v1/product')
def predict_sales_forecast():
# 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 prediction (get sales_forecast)
predicted_price = model.predict(input_data)[0]
# Map prediction result to a human-readable label
predicted_price = round(float(predicted_price), 2)
# Return the prediction as a JSON response
return jsonify({'Sales Forecast (in dollars)': predicted_price})
# Define an endpoint to predict forecast for a batch of products
@sales_forecast_api.post('/v1/productbatch')
def predict_sales_forecast_batch():
# 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)
# Make predictions for all products in the DataFrame (get log_prices)
predicted_sales = model.predict(input_data).tolist()
# Calculate actual prices
predicted_sales = [round(float(Product_Store_Sales_Total), 2) for Product_Store_Sales_Total in predicted_sales]
# Create a dictionary of predictions with Product IDs as keys
product_ids = input_data['Product_Id'].tolist()
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 application in debug mode if this script is executed directly
if __name__ == '__main__':
sales_forecast_api.run(debug=True)
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