Update app.py

app.py

@@ -10,12 +10,12 @@ import sqlite3
 
 app = Flask(__name__)
 
-# ✅ Ensure results directory exists
-if not os.path.exists('results'):
-    os.makedirs('results')
+# ✅ Directory and database path
+OUTPUT_DIR = '/tmp/results'
+if not os.path.exists(OUTPUT_DIR):
+    os.makedirs(OUTPUT_DIR)
 
-# ✅ Database path inside results directory
-DB_PATH = 'results/results.db'
+DB_PATH = os.path.join(OUTPUT_DIR, 'results.db')
 
 def init_db():
     conn = sqlite3.connect(DB_PATH)
@@ -47,6 +47,7 @@ def preprocess_image(img):
 
 # ✅ Grad-CAM Generation
 def make_gradcam(img_array, model, last_conv_layer_name='Conv_1_bn'):
+    """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])
 
@@ -65,13 +66,12 @@ def make_gradcam(img_array, model, last_conv_layer_name='Conv_1_bn'):
     heatmap = tf.reduce_mean(conv_outputs, axis=-1).numpy()
     heatmap = np.maximum(heatmap, 0)
     heatmap /= np.max(heatmap)
+
     return heatmap
 
 # ✅ Save Grad-CAM Overlay
-def save_gradcam_image(original_img, heatmap, filename='gradcam.png', output_dir='results'):
-    if not os.path.exists(output_dir):
-        os.makedirs(output_dir)
-
+def save_gradcam_image(original_img, heatmap, filename='gradcam.png', output_dir=OUTPUT_DIR):
+    """Save the Grad-CAM overlay image and return its path."""
     img = np.array(original_img.resize((224, 224)))
     heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))
     heatmap = np.uint8(255 * heatmap)
@@ -90,8 +90,89 @@ def home():
 
 @app.route("/test_file")
 def test_file():
+    """Check if the model file is present and readable."""
     filepath = "mobilenet_glaucoma_model.h5"
     if os.path.exists(filepath):
         return f"✅ Model file found at: {filepath}"
     else:
-        return
+        return "❌ Model file NOT found."
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    """Perform prediction and save results to SQLite database."""
+    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='Conv_1_bn')
+        gradcam_filename = f"gradcam_{int(datetime.now().timestamp())}.png"
+        save_gradcam_image(img, heatmap, filename=gradcam_filename)
+
+        # Save results to SQLite
+        conn = sqlite3.connect(DB_PATH)
+        cursor = conn.cursor()
+        cursor.execute("""
+            INSERT INTO results (image_filename, prediction, confidence, gradcam_filename, timestamp) 
+            VALUES (?, ?, ?, ?, ?)
+        """, (file.filename, result, confidence, gradcam_filename, datetime.now().isoformat()))
+        conn.commit()
+        conn.close()
+
+        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 SQLite database."""
+    conn = sqlite3.connect(DB_PATH)
+    cursor = conn.cursor()
+    cursor.execute("SELECT * FROM results ORDER BY timestamp DESC")
+    results_data = cursor.fetchall()
+    conn.close()
+
+    results_list = []
+    for record in results_data:
+        results_list.append({
+            'id': record[0],
+            'image_filename': record[1],
+            'prediction': record[2],
+            'confidence': record[3],
+            'gradcam_filename': record[4],
+            'timestamp': record[5]
+        })
+
+    return jsonify(results_list)
+
+@app.route('/gradcam/<filename>')
+def get_gradcam(filename):
+    """Serve the Grad-CAM overlay image."""
+    filepath = os.path.join(OUTPUT_DIR, 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)