Update app.py

app.py

@@ -1,139 +1,148 @@
-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('/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)
+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)