Update app.py

app.py

@@ -1041,40 +1041,9 @@ def server_status():
 
 @app.route('/upload', methods=['POST'])
 def upload_file():
-    """Handle file upload and prediction (legacy endpoint)"""
-    if 'file' not in request.files:
-        return jsonify({'error': 'No file part'}), 400
-    
-    file = request.files['file']
-    direction = request.form.get('direction', 'north')
-    task_type = request.form.get('task_type', 'TASK_1')
-    num_obstacles = request.form.get('NUM_OBSTACLES', '0')
-    
-    if file.filename == '':
-        return jsonify({'error': 'No selected file'}), 400
-    
-    if file:
-        filename = os.path.basename(file.filename)
-        file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
-        file.save(file_path)
-        
-        # Process the file and predict
-        class_id, detection_result = process_file(file_path, direction, task_type, filename)
-        
-        # Handle NUM_OBSTACLES parameter for obstacle display
-        if num_obstacles and num_obstacles.isdigit() and 1 <= int(num_obstacles) <= 8:
-            save_obstacle_image(detection_result, int(num_obstacles))
-        
-        if class_id is not None:
-            return jsonify({
-                'message': 'File successfully uploaded and processed',
-                'predicted_id': class_id,
-                'direction': direction,
-                'task_type': task_type,
-                'num_obstacles': num_obstacles
-            }), 200
-        else:
-            return jsonify({'error': 'Failed to process image'}), 500
+    """Handle file upload and prediction (legacy endpoint - redirects to /image)"""
+    # Redirect to /image endpoint to avoid code duplication
+    return image_predict()
 
 @app.route('/image', methods=['POST'])
 def image_predict():
@@ -1095,13 +1064,18 @@ def image_predict():
     file_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
     file.save(file_path)
     
+    # Get parameters from both old and new format
+    direction = request.form.get('direction', 'north')
+    task_type = request.form.get('task_type', 'TASK_1')
+    num_obstacles = request.form.get('NUM_OBSTACLES', '0')  # Support NUM_OBSTACLES parameter
+    
     # Try to parse filename format: "<timestamp>_<obstacle_id>_<signal>.jpeg"
     # But be flexible with different formats
     constituents = file.filename.split("_")
     
     # Default values
     obstacle_id = "unknown"
-    signal = "C"  # Default to center
+    signal = direction  # Use direction parameter as signal
     
     # Try to extract obstacle_id and signal if available
     try:
@@ -1123,11 +1097,14 @@ def image_predict():
     
     # Check for optional preference parameter
     prefer_close = request.form.get('prefer_close_objects', 'true').lower() == 'true'
-    task_type = request.form.get('task_type', 'TASK_1')
     
     # Process the file and predict
     class_id, detection_result = process_file(file_path, signal, task_type, filename)
     
+    # Handle NUM_OBSTACLES parameter for obstacle display
+    if num_obstacles and num_obstacles.isdigit() and 1 <= int(num_obstacles) <= 8:
+        save_obstacle_image(detection_result, int(num_obstacles))
+    
     if detection_result is None:
         return jsonify({'error': 'Failed to process image'}), 500
     
@@ -1138,20 +1115,32 @@ def image_predict():
     print(f"Annotated image saved to: {detection_result['marked_image_path']}")
     print(f"YOLO txt file saved to: {detection_result.get('txt_file_path', 'N/A')}")
     
-    # Return detailed detection information in compatible format
-    result = {
-        "obstacle_id": obstacle_id,
-        "image_id": image_id,
-        "detection": {
-            "label": detection_result["label"],
-            "confidence": detection_result["confidence"],
-            "bbox_coordinates": detection_result["bbox"],
-            "original_image_path": detection_result.get("original_image_path"),
-            "annotated_image_path": detection_result["marked_image_path"],
-            "txt_file_path": detection_result.get("txt_file_path")
+    # Determine response format based on request context
+    # If it's from the web interface (has direction/task_type), return simple format
+    if direction != 'north' or task_type != 'TASK_1' or num_obstacles != '0':
+        # Web interface format
+        return jsonify({
+            'message': 'File successfully uploaded and processed',
+            'predicted_id': class_id,
+            'direction': direction,
+            'task_type': task_type,
+            'num_obstacles': num_obstacles
+        }), 200
+    else:
+        # Original API format
+        result = {
+            "obstacle_id": obstacle_id,
+            "image_id": image_id,
+            "detection": {
+                "label": detection_result["label"],
+                "confidence": detection_result["confidence"],
+                "bbox_coordinates": detection_result["bbox"],
+                "original_image_path": detection_result.get("original_image_path"),
+                "annotated_image_path": detection_result["marked_image_path"],
+                "txt_file_path": detection_result.get("txt_file_path")
+            }
         }
-    }
-    return jsonify(result)
+        return jsonify(result)
 
 @app.route('/latest-result')
 def get_latest_result():
@@ -1315,7 +1304,7 @@ if __name__ == '__main__':
     print(f"Starting Enhanced Image Recognition Server...")
     print(f"Web interface available at: http://{HOST}:{PORT}")
     print(f"Obstacles display available at: http://{HOST}:{PORT}/obstacles")
-    print(f"API endpoints: /image (main), /upload (legacy)")
+    print(f"API endpoints: /image (unified endpoint), /upload (legacy redirect)")
     
     try:
         app.run(host=HOST, port=PORT, debug=False)