Update app.py

app.py

@@ -10,8 +10,13 @@ import sqlite3
 
 app = Flask(__name__)
 
-# ✅ 1. SQLite database setup
-DB_PATH = 'results.db'
+# ✅ Ensure results directory exists
+if not os.path.exists('results'):
+    os.makedirs('results')
+
+# ✅ Database path inside results directory
+DB_PATH = 'results/results.db'
+
 def init_db():
     conn = sqlite3.connect(DB_PATH)
     cursor = conn.cursor()
@@ -30,19 +35,18 @@ def init_db():
 
 init_db()
 
-# ✅ 2. Load Model
+# ✅ Load Model
 model = load_model('mobilenet_glaucoma_model.h5', compile=False)
 
-# ✅ 3. Preprocess Image
+# ✅ 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
+# ✅ 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])
 
@@ -61,12 +65,10 @@ 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
 
-# ✅ 5. Save Grad-CAM Overlay
+# ✅ 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)
 
@@ -88,89 +90,8 @@ 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 "❌ 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('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)
+        return