| from flask import Flask, request, jsonify, send_file |
| from PIL import Image, ImageOps |
| import numpy as np |
| import tensorflow as tf |
| import io |
|
|
| app = Flask(__name__) |
|
|
| |
| model = tf.keras.models.load_model('my_model2.h5') |
|
|
| def import_and_predict(image_data): |
| image = ImageOps.fit(image_data, (100, 100), Image.ANTIALIAS) |
| image = image.convert('RGB') |
| image = np.asarray(image) |
| image = (image.astype(np.float32) / 255.0) |
| img_reshape = image[np.newaxis, ...] |
| prediction = model.predict(img_reshape) |
| return prediction |
|
|
| @app.route('/') |
| def index(): |
| return "Welcome to the Glaucoma Detector API!" |
|
|
| @app.route('/predict', methods=['POST']) |
| def predict(): |
| if 'file' not in request.files: |
| return jsonify({'error': 'No file part'}), 400 |
| file = request.files['file'] |
| if file.filename == '': |
| return jsonify({'error': 'No selected file'}), 400 |
| if file and file.filename.lower().endswith('.jpg'): |
| image = Image.open(io.BytesIO(file.read())) |
| prediction = import_and_predict(image) |
| pred = prediction[0][0] |
| if pred > 0.5: |
| result = "Your eye is Healthy. Great!" |
| else: |
| result = "You are affected by Glaucoma. Please consult an ophthalmologist as soon as possible." |
| return jsonify({'prediction': result}) |
| else: |
| return jsonify({'error': 'Invalid file format'}), 400 |
|
|
| if __name__ == '__main__': |
| app.run(port=5000, debug=True) |
|
|
