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from flask import Flask, request, jsonify, send_file
from tensorflow.keras.models import load_model, Model
from PIL import Image
import numpy as np
import os
import cv2
import tensorflow as tf
import firebase_admin
from firebase_admin import credentials, db
from datetime import datetime
app = Flask(__name__)
# ✅ 1. Initialize Firebase
cred = credentials.Certificate("glaucoma-4b682-firebase-adminsdk-fbsvc-cd31fbe99d.json") # Path to your service account JSON
firebase_admin.initialize_app(cred, {
'databaseURL': 'https://glaucoma-4b682-default-rtdb.firebaseio.com/'
})
results_ref = db.reference('results') # Will save results here
# ✅ 2. Load the Model
model = load_model('mobilenet_glaucoma_model.h5', compile=False)
# ✅ 3. Preprocess Image
def preprocess_image(img):
img = img.resize((224, 224))
img = np.array(img) / 255.0
img = np.expand_dims(img, axis=0)
return img
# ✅ 4. Grad-CAM Generation
def make_gradcam(img_array, model, last_conv_layer_name='Conv2D_1'):
"""Generate Grad-CAM for the given image and model."""
last_conv_layer = model.get_layer(last_conv_layer_name)
grad_model = Model(inputs=model.inputs, outputs=[last_conv_layer.output, model.output])
with tf.GradientTape() as tape:
conv_outputs, predictions = grad_model(img_array)
loss = predictions[:, 0]
grads = tape.gradient(loss, conv_outputs)
pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))
conv_outputs = conv_outputs[0]
for i in range(conv_outputs.shape[-1]):
conv_outputs[..., i] *= pooled_grads[i]
heatmap = tf.reduce_mean(conv_outputs, axis=-1).numpy()
heatmap = np.maximum(heatmap, 0)
heatmap /= np.max(heatmap)
return heatmap
# ✅ 5. Save Grad-CAM Overlay
def save_gradcam_image(original_img, heatmap, filename='gradcam.png', output_dir='results'):
"""Save the Grad-CAM overlay image and return its path."""
if not os.path.exists(output_dir):
os.makedirs(output_dir)
img = np.array(original_img.resize((224, 224)))
heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))
heatmap = np.uint8(255 * heatmap)
heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET)
overlay = cv2.addWeighted(img, 0.6, heatmap, 0.4, 0)
filepath = os.path.join(output_dir, filename)
cv2.imwrite(filepath, overlay)
return filepath
@app.route('/')
def home():
return "Glaucoma Detection Flask API is running!"
@app.route("/test_file")
def test_file():
"""Check if the Firebase service account JSON is present and readable."""
filepath = "glaucoma-4b682-firebase-adminsdk-fbsvc-cd31fbe99d.json"
if os.path.exists(filepath):
return f"✅ Service account file found at: {filepath}"
else:
return "❌ Service account JSON NOT found."
@app.route('/predict', methods=['POST'])
def predict():
"""Perform prediction and save results to Firebase."""
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_array = preprocess_image(img)
prediction = model.predict(img_array)[0]
glaucoma_prob = 1 - prediction[0]
normal_prob = prediction[0]
result = 'Glaucoma' if glaucoma_prob > normal_prob else 'Normal'
confidence = float(glaucoma_prob) if result == 'Glaucoma' else float(normal_prob)
# Grad-CAM
heatmap = make_gradcam(img_array, model, last_conv_layer_name='Conv2D_1')
gradcam_filename = f"gradcam_{int(datetime.now().timestamp())}.png"
save_gradcam_image(img, heatmap, filename=gradcam_filename)
# Save to Firebase
results_ref.push({
'image_filename': file.filename,
'prediction': result,
'confidence': confidence,
'gradcam_filename': gradcam_filename,
'timestamp': datetime.now().isoformat()
})
return jsonify({
'prediction': result,
'confidence': confidence,
'normal_probability': float(normal_prob),
'glaucoma_probability': float(glaucoma_prob),
'gradcam_image': gradcam_filename
})
except Exception as e:
return jsonify({'error': str(e)}), 500
@app.route('/results', methods=['GET'])
def results():
"""List all results from the Firebase database."""
results_data = results_ref.get()
if not results_data:
results_data = []
return jsonify(results_data)
@app.route('/gradcam/<filename>')
def get_gradcam(filename):
"""Serve the Grad-CAM overlay image."""
filepath = os.path.join('results', filename)
if os.path.exists(filepath):
return send_file(filepath, mimetype='image/png')
else:
return jsonify({'error': 'File not found'}), 404
if __name__ == '__main__':
app.run(host='0.0.0.0', port=7860)
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