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import joblib
import pandas as pd
from flask import Flask, request, jsonify, request
# Initialize Flask app with a name
product_sales_predictor_api = Flask("Product Sales Predictor")
# Load the trained churn prediction model
model = joblib.load("product_sales_prediction_model_v1_0.joblib")
# Define a route for the home page
@product_sales_predictor_api.get('/')
def home():
return "Welcome to the Product Sales Prediction API!"
# Define an endpoint to predict sales for a single product
@product_sales_predictor_api.post('/v1/product')
def predict_sales():
# Get JSON data from the request
product_sales = request.get_json()
# Extract relevant product features from the input data
sample = {
'Product_Weight': product_sales['Product_Weight'],
'Product_Sugar_Content': product_sales['Product_Sugar_Content'],
'Product_Allocated_Area': product_sales['Product_Allocated_Area'],
'Product_Type': product_sales['Product_Type'],
'Product_MRP': product_sales['Product_MRP'],
'Store_Id': product_sales['Store_Id'],
'Store_Establishment_Year': product_sales['Store_Establishment_Year'],
'Store_Size': product_sales['Store_Size'],
'Store_Location_City_Type': product_sales['Store_Location_City_Type'],
'Store_Type': product_sales['Store_Type']
}
# Convert the extracted data into a DataFrame
input_data = pd.DataFrame([sample])
print('inside post')
print(input_data)
print(model.predict(input_data).tolist()[0])
# Make a sales prediction using the trained model and convert to float
predicted_sales = model.predict(input_data).tolist()[0]
predicted_sales = round(float(predicted_sales),2)
# Return the prediction as a JSON response
return jsonify({'Predicted Sales': predicted_sales})
# Define an endpoint to predict sales for a batch of products
@product_sales_predictor_api.post('/v1/productbatch')
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)
input_data.head()
# Make predictions for the batch data and convert raw predictions into a readable format
predicted_sales = [round(float(sales),2) for sales in model.predict(input_data).tolist()]
# Create a dictionary of predictions with Product ID and Predicted sales
product_id = input_data['Product_ID'].tolist() # Assuming id as the key or product id
output_dict = dict(zip(product_id, predicted_sales)) # Sales value
# Return the predictions dictionary as a JSON response
return output_dict
# Run the Flask app in debug mode
if __name__ == '__main__':
product_sales_predictor_api.run(host='0.0.0.0', port=8501, debug=True)
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