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File size: 1,216 Bytes
b76ebbf 70d455e b76ebbf 70d455e b76ebbf 70d455e b76ebbf 70d455e b76ebbf 70d455e b76ebbf 70d455e b76ebbf | 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 | from flask import Flask, request, jsonify
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
import os
app = Flask(__name__)
# Load the model
model = load_model('mobilenet_glaucoma_model.h5', compile=False)
def preprocess_image(img):
img = img.resize((224, 224))
img = np.array(img) / 255.0
img = np.expand_dims(img, axis=0)
return img
@app.route('/')
def home():
return "Glaucoma Detection Flask API is running!"
@app.route('/predict', methods=['POST'])
def predict():
if 'file' not in request.files:
return jsonify({'error': 'No file uploaded'}), 400
file = request.files['file']
if file.filename == '':
return jsonify({'error': 'No file selected'}), 400
try:
img = Image.open(file.stream).convert('RGB')
img = preprocess_image(img)
prediction = model.predict(img)[0]
result = 'Glaucoma' if prediction[0] < 0.5 else 'Normal'
return jsonify({
'prediction': result,
'confidence': float(prediction[0])
})
except Exception as e:
return jsonify({'error': str(e)}), 500
if __name__ == '__main__':
app.run(host='0.0.0.0', port=7860)
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