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from flask import Flask,request,jsonify |
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from flask_cors import CORS |
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import joblib |
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import numpy as np |
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import pandas as pd |
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import logging |
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logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s') |
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app = Flask(__name__) |
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CORS(app, resources={r"/predict":{"origins":"*"}}) |
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try: |
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model_gradbosot = joblib.load('gradboost_RSCV.joblib') |
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model_rndmfrst = joblib.load('RndmFrstReg_RSCV.joblib') |
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pipeline = joblib.load('pipeline.joblib') |
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feature_names = joblib.load('feature_names.joblib') |
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except Exception as Ex: |
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logging.error(f'Exception in loading joblib file: {Ex}') |
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required_features =['Product_Weight','Product_Sugar_Content','Product_Allocated_Area', |
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'Product_Type','Product_MRP', |
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'Store_Size','Store_Location_City_Type','Store_Type','Age_Of_Store' |
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] |
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@app.get('/') |
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def home(): |
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logging.debug("Accessed endpoint of Home page") |
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return "Welcome to Superkart Prediction system" |
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@app.route('/predict', methods=['POST']) |
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def predict(): |
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try: |
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data = request.get_json() |
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logging.debug(f"Input received:{data}") |
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if not data: |
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return jsonify({'Error':'No data provided for prediction'},400) |
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if not all(feature in data for feature in required_features): |
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feature_missing = [feature for feature in required_features if feature not in data] |
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logging.error(f"Exception feature missing:{feature_missing}") |
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return jsonify({'Exception':f'Feature missing {feature_missing}'},400) |
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feature_for_prediction =pd.DataFrame([{ |
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'Product_Weight':float(data['Product_Weight']), |
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'Product_Sugar_Content':data['Product_Sugar_Content'], |
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'Product_Allocated_Area':float(data['Product_Allocated_Area']), |
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'Product_Type': data['Product_Type'], |
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'Product_MRP':float(data['Product_MRP']), |
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'Store_Size':data['Store_Size'], |
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'Store_Location_City_Type':data['Store_Location_City_Type'], |
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'Store_Type':data['Store_Type'], |
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'Age_Of_Store':float(data['Age_Of_Store']) |
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}],columns=required_features) |
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features_scaled = pipeline.transform(feature_for_prediction) |
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logging.debug(f"Features scaled: {features_scaled}") |
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prediction_gradboost = model_gradbosot.predict(features_scaled)[0] |
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prediction_randFrst = model_rndmfrst.predict(features_scaled)[0] |
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logging.debug(f"Prediction gradmodel: {prediction_gradboost}") |
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logging.debug(f"Prediction RandmFrst: {prediction_randFrst}") |
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return jsonify ({'gradientBoosting':float(prediction_gradboost), |
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'randomForest':float(prediction_randFrst)}) |
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except Exception as ex: |
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logging.error(f'Exception: {ex}') |
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return jsonify({'Exception': str(ex) }) |
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if __name__ == '__main__': |
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app.run(host='0.0.0.0', port=7860, debug=False) |
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