File size: 1,769 Bytes
5575a8a | 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 | from flask import Flask, request, jsonify, render_template
import tensorflow as tf
import numpy as np
import joblib
import os
app = Flask(__name__)
# Load model and scalers globally for efficiency
MODEL_PATH = 'student_marks_rnn_model.h5'
SCALER_X_PATH = 'scaler_X.pkl'
SCALER_Y_PATH = 'scaler_y.pkl'
model = None
scaler_X = None
scaler_y = None
def load_resources():
global model, scaler_X, scaler_y
if os.path.exists(MODEL_PATH) and os.path.exists(SCALER_X_PATH) and os.path.exists(SCALER_Y_PATH):
model = tf.keras.models.load_model(MODEL_PATH)
scaler_X = joblib.load(SCALER_X_PATH)
scaler_y = joblib.load(SCALER_Y_PATH)
return True
return False
@app.route('/')
def index():
return render_template('index.html')
@app.route('/predict', methods=['POST'])
def predict():
if model is None:
if not load_resources():
return jsonify({'error': 'Model or scalers not found. Run training first.'}), 500
try:
data = request.get_json()
num_courses = float(data['num_courses'])
time_study = float(data['time_study'])
# Preprocess
input_data = np.array([[num_courses, time_study]])
input_scaled = scaler_X.transform(input_data)
input_reshaped = input_scaled.reshape((1, 1, 2))
# Predict
prediction_scaled = model.predict(input_reshaped)
prediction = scaler_y.inverse_transform(prediction_scaled)
result = float(prediction[0][0])
return jsonify({'marks': round(result, 2)})
except Exception as e:
return jsonify({'error': str(e)}), 400
if __name__ == '__main__':
load_resources()
app.run(debug=True, port=5000)
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