| from flask import Flask, request, jsonify, render_template
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| import tensorflow as tf
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| import numpy as np
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| import joblib
|
| import os
|
|
|
| app = Flask(__name__)
|
|
|
|
|
| MODEL_PATH = 'student_marks_rnn_model.h5'
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| SCALER_X_PATH = 'scaler_X.pkl'
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| SCALER_Y_PATH = 'scaler_y.pkl'
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|
|
| model = None
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| scaler_X = None
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| scaler_y = None
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|
|
| def load_resources():
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| global model, scaler_X, scaler_y
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| if os.path.exists(MODEL_PATH) and os.path.exists(SCALER_X_PATH) and os.path.exists(SCALER_Y_PATH):
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| model = tf.keras.models.load_model(MODEL_PATH)
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| scaler_X = joblib.load(SCALER_X_PATH)
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| scaler_y = joblib.load(SCALER_Y_PATH)
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| return True
|
| return False
|
|
|
| @app.route('/')
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| def index():
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| return render_template('index.html')
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|
|
| @app.route('/predict', methods=['POST'])
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| def predict():
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| if model is None:
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| if not load_resources():
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| return jsonify({'error': 'Model or scalers not found. Run training first.'}), 500
|
|
|
| try:
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| data = request.get_json()
|
| num_courses = float(data['num_courses'])
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| time_study = float(data['time_study'])
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|
|
|
|
| input_data = np.array([[num_courses, time_study]])
|
| input_scaled = scaler_X.transform(input_data)
|
| input_reshaped = input_scaled.reshape((1, 1, 2))
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|
|
|
|
| prediction_scaled = model.predict(input_reshaped)
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| prediction = scaler_y.inverse_transform(prediction_scaled)
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|
|
| result = float(prediction[0][0])
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| return jsonify({'marks': round(result, 2)})
|
|
|
| except Exception as e:
|
| return jsonify({'error': str(e)}), 400
|
|
|
| if __name__ == '__main__':
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| load_resources()
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| app.run(debug=True, port=5000)
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|
|
