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import pandas as pd
import joblib
from flask import Flask, request, jsonify
import os # Import os for debugging
superkart_api = Flask(__name__)
# Define MODEL_FILENAME inside the app.py string
MODEL_FILENAME = "tuned_gradient_boosting_regressor_model.joblib" # <-- ADD THIS LINE
print("--- APP STARTUP: Flask app instance created ---")
# Load the trained model
try:
print(f"--- APP STARTUP: Attempting to load model from {os.path.join(os.getcwd(), MODEL_FILENAME)} ---")
model = joblib.load("tuned_gradient_boosting_regressor_model.joblib")
print("--- APP STARTUP: Model loaded successfully ---")
except Exception as e:
print(f"--- APP STARTUP: ERROR loading model: {e} ---")
raise # Re-raise to crash early if model loading fails
print("--- APP STARTUP: Model variable assigned ---")
@superkart_api.get('/')
def home():
print("--- REQUEST: Home endpoint accessed ---")
return "Welcome to the Superkart Sales Prediction API!"
@superkart_api.post('/v1/predict')
def predict_sales():
print("--- REQUEST: Predict endpoint accessed ---")
data = request.get_json()
# ... (rest of your predict_sales logic) ...
sample = {
'Product_Weight': data['Product_Weight'],
'Product_Sugar_Content': data['Product_Sugar_Content'],
'Product_Allocated_Area': data['Product_Allocated_Area'],
'Product_MRP': data['Product_MRP'],
'Store_Size': data['Store_Size'],
'Store_Location_City_Type': data['Store_Location_City_Type'],
'Store_Type': data['Store_Type'],
'Product_Id_char': data['Product_Id_char'],
'Store_Age_Years': data['Store_Age_Years'],
'Product_Type_Category': data['Product_Type_Category']
}
input_df = pd.DataFrame([sample])
prediction = model.predict(input_df).tolist()[0]
print(f"--- REQUEST: Prediction made: {prediction} ---")
return jsonify({'Sales': prediction})
if __name__ == '__main__':
print("--- APP STARTUP: Running in __main__ block ---")
# import os # Redundant import removed
port = int(os.environ.get("PORT", 7860))
superkart_api.run(host="0.0.0.0", port=port)
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