local file llok

app.py

@@ -162,43 +162,34 @@ def generate_model_from_images_and_upload(
     # print(f"Received image_inputs: {image_inputs}, input_type: {input_type}") # Original log, can be removed if too verbose
 
     for i, file_data_obj in enumerate(image_inputs):
-        img_data_url = None
-        original_filename = f"image_{i}" # Default filename
+        img_local_path = None
+        original_filename = f"image_{i}"
+
         if isinstance(file_data_obj, dict):
-            img_data_url = file_data_obj.get('path') or file_data_obj.get('url') # Prefer path, fallback to url
+            img_local_path = file_data_obj.get('path')
             original_filename = file_data_obj.get('name', original_filename)
-        elif isinstance(file_data_obj, str): # Fallback if it somehow still comes as a plain string
-            img_data_url = file_data_obj
-            original_filename = os.path.basename(img_data_url).split('.')[0] or original_filename
+            print(f"DEBUG: Extracted local path: {img_local_path}, original_filename: {original_filename} from dict: {file_data_obj}")
+        # elif isinstance(file_data_obj, str): # This fallback may not be needed if Gradio always gives a dict with gr.JSON
+            # img_local_path = file_data_obj 
+            # original_filename = os.path.basename(img_local_path).split('.')[0] or original_filename
         
-        if not img_data_url:
-            print(f"Error: Could not extract URL from file_data_obj {i}: {file_data_obj}")
-            # Skip this item or raise an error, for now, skip
+        if not img_local_path:
+            print(f"Error: Could not extract local path from file_data_obj {i}: {file_data_obj}")
             continue 
 
         try:
-            print(f"Processing image {i+1}/{len(image_inputs)} from URL: {img_data_url} (original name: {original_filename}) with type '{input_type}'")
-            if input_type == "url": # This check is still relevant based on what Node.js sends as input_type
-                # print(f"Fetching image from URL: {img_data_url}") # Already printed
-                response_img = requests.get(img_data_url, stream=True, timeout=30)
-                response_img.raise_for_status()
-                img = Image.open(response_img.raw)
-                image_basenames.append(os.path.basename(img_data_url).split('.')[0] or f"image_{i}")
-            # Note: The base64 and filepath cases from original code might be less relevant now
-            # if the server always sends FileData-like objects with type "url".
-            # Consider if you still need to support direct base64/filepath strings in image_inputs list here.
-            elif input_type == "base64":
-                print(f"Decoding base64 image data (first 30 chars): {img_data_url[:30]}...")
-                missing_padding = len(img_data_url) % 4
-                if missing_padding:
-                    img_data_url += '=' * (4 - missing_padding)
-                img_bytes = base64.b64decode(img_data_url)
-                img = Image.open(io.BytesIO(img_bytes))
-                image_basenames.append(f"base64_image_{i}")
-            else:
-                print(f"Unsupported input_type: {input_type} for FileData object flow")
-                raise ValueError(f"Unsupported input_type: {input_type} for FileData object flow")
+            # The input_type check is less about HOW to load (it's always local now), but can be kept for logging/context
+            print(f"Processing image {i+1}/{len(image_inputs)} from LOCAL PATH: {img_local_path} (original name: {original_filename}), received input_type: '{input_type}'")
+            
+            # DIRECTLY OPEN THE LOCAL FILE provided by Gradio
+            img = Image.open(img_local_path) 
             
+            # Use original_filename (which might have been extracted from the FileData object)
+            image_basenames.append(original_filename.split('.')[0] or f"image_{i}")
+
+            # The old requests.get() and base64 decoding sections are no longer needed here
+            # as Gradio handles the initial download/provisioning of the file to a local path.
+
             print(f"Image {i+1} loaded, mode: {img.mode}, size: {img.size}. Preprocessing...")
             # Ensure image is in RGB format if it's not, e.g. RGBA or P
             if img.mode == 'RGBA' or img.mode == 'P':
@@ -210,7 +201,7 @@ def generate_model_from_images_and_upload(
             print(f"Image {i+1} processed and added.")
 
         except Exception as e:
-            print(f"Error processing image {i} ('{str(img_data_url)[:50]}...'): {e}")
+            print(f"Error processing image {i} ('{str(img_local_path)[:50]}...'): {e}")
             import traceback
             traceback.print_exc()
             raise gr.Error(f"Failed to load or process input image {i} ({input_type}): {e}")