Update app.py

app.py

@@ -99,16 +99,23 @@ def test_file():
 
 @app.route('/predict', methods=['POST'])
 def predict():
-    """Perform prediction and save results to SQLite database."""
+    """Perform prediction, save results (including uploaded image), and save to SQLite database."""
     if 'file' not in request.files:
         return jsonify({'error': 'No file uploaded'}), 400
 
-    file = request.files['file']
-    if file.filename == '':
+    uploaded_file = request.files['file']
+    if uploaded_file.filename == '':
         return jsonify({'error': 'No file selected'}), 400
 
     try:
-        img = Image.open(file.stream).convert('RGB')
+        # ✅ Save the uploaded image
+        timestamp = int(datetime.now().timestamp())
+        uploaded_filename = f"uploaded_{timestamp}.png"
+        uploaded_file_path = os.path.join(OUTPUT_DIR, uploaded_filename)
+        uploaded_file.save(uploaded_file_path)
+
+        # ✅ Perform prediction
+        img = Image.open(uploaded_file_path).convert('RGB')
         img_array = preprocess_image(img)
 
         prediction = model.predict(img_array)[0]
@@ -117,18 +124,18 @@ def predict():
         result = 'Glaucoma' if glaucoma_prob > normal_prob else 'Normal'
         confidence = float(glaucoma_prob) if result == 'Glaucoma' else float(normal_prob)
 
-        # Grad-CAM
+        # ✅ Grad-CAM
         heatmap = make_gradcam(img_array, model, last_conv_layer_name='Conv_1_bn')
-        gradcam_filename = f"gradcam_{int(datetime.now().timestamp())}.png"
+        gradcam_filename = f"gradcam_{timestamp}.png"
         save_gradcam_image(img, heatmap, filename=gradcam_filename)
 
-        # Save results to SQLite
+        # ✅ 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()))
+        """, (uploaded_filename, result, confidence, gradcam_filename, datetime.now().isoformat()))
         conn.commit()
         conn.close()
 
@@ -137,7 +144,8 @@ def predict():
             'confidence': confidence,
             'normal_probability': float(normal_prob),
             'glaucoma_probability': float(glaucoma_prob),
-            'gradcam_image': gradcam_filename
+            'gradcam_image': gradcam_filename,
+            'image_filename': uploaded_filename
         })
 
     except Exception as e:
@@ -174,5 +182,14 @@ def get_gradcam(filename):
     else:
         return jsonify({'error': 'File not found'}), 404
 
+@app.route('/image/<filename>')
+def get_image(filename):
+    """Serve the original uploaded 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)