Traceback

app.py

@@ -16,6 +16,7 @@ import requests
 import base64
 import io
 import tempfile
+import traceback
 MAX_SEED = np.iinfo(np.int32).max
 TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
 os.makedirs(TMP_DIR, exist_ok=True)
@@ -141,198 +142,167 @@ def generate_model_from_images_and_upload(
     model_description: str,
     req: gr.Request
 ) -> str:
-    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
-    os.makedirs(user_dir, exist_ok=True)
-
-    # --- DEBUG LOGS --- Keep these for now
-    print(f"Python DEBUG: Raw image_inputs (as received by function): {image_inputs}")
-    print(f"Python DEBUG: Type of image_inputs: {type(image_inputs)}")
-    if isinstance(image_inputs, list):
-        print(f"Python DEBUG: Length of image_inputs list: {len(image_inputs)}")
-        if len(image_inputs) > 0 and isinstance(image_inputs[0], dict):
-            print(f"Python DEBUG: First element of image_inputs (should be a dict): {image_inputs[0]}")
-            print(f"Python DEBUG: Type of first element: {type(image_inputs[0])}")
-    print(f"Python DEBUG: Received input_type from Node.js: '{input_type}'")
-    # --- END DEBUG LOGS ---
-    
-    pil_images = []
-    image_basenames_for_prompt = [] # For constructing a prompt/description if needed
-
-    for i, file_data_obj in enumerate(image_inputs): # file_data_obj is one dict from the list
-        img_to_open_path = None # This will be the path to the actual file on HF server
-        # Get name if provided by Node.js (e.g., original filename or generated like "uploaded_image_0.png")
-        current_image_name = file_data_obj.get('name', f"image_{i}.png") 
-
-        print(f"Python DEBUG: Processing item {i}: {file_data_obj}, current_image_name: {current_image_name}")
+    try:
+        user_dir = os.path.join(TMP_DIR, str(req.session_hash))
+        os.makedirs(user_dir, exist_ok=True)
+
+        print(f"Python DEBUG: Raw image_inputs (as received by function): {image_inputs}")
+        print(f"Python DEBUG: Type of image_inputs: {type(image_inputs)}")
+        if isinstance(image_inputs, list):
+            print(f"Python DEBUG: Length of image_inputs list: {len(image_inputs)}")
+            if len(image_inputs) > 0 and isinstance(image_inputs[0], dict):
+                print(f"Python DEBUG: First element of image_inputs (should be a dict): {image_inputs[0]}")
+                print(f"Python DEBUG: Type of first element: {type(image_inputs[0])}")
+        print(f"Python DEBUG: Received input_type from Node.js: '{input_type}'")
+        
+        pil_images = []
+        image_basenames_for_prompt = []
+
+        for i, file_data_obj in enumerate(image_inputs):
+            img_to_open_path = None
+            current_image_name = file_data_obj.get('name', f"image_{i}.png") 
+            print(f"Python DEBUG: Processing item {i}: {file_data_obj}, current_image_name: {current_image_name}")
+
+            if input_type == 'url':
+                img_to_open_path = file_data_obj.get('path')
+                if not img_to_open_path:
+                    print(f"Error: For 'url' input_type, 'path' was missing in item {i}: {file_data_obj}")
+                    continue
+                print(f"Python INFO: 'url' type. Using Gradio-provided path for '{current_image_name}': {img_to_open_path}")
+            elif input_type == 'base64':
+                base64_data_uri = file_data_obj.get('data_uri')
+                if not base64_data_uri or not isinstance(base64_data_uri, str) or not base64_data_uri.startswith('data:image'):
+                    print(f"Error: For 'base64' input_type, 'data_uri' was missing or invalid in item {i}: {file_data_obj}")
+                    continue
+                try:
+                    print(f"Python INFO: 'base64' type. Decoding data_uri for '{current_image_name}'...")
+                    header, encoded_data = base64_data_uri.split(',', 1)
+                    image_data_bytes = base64.b64decode(encoded_data)
+                    file_extension = ".png"
+                    try:
+                        parsed_extension = os.path.splitext(current_image_name)[1]
+                        if parsed_extension:
+                            file_extension = parsed_extension
+                        elif '/' in header and ';' in header:
+                            mime_subtype = header.split('/')[1].split(';')[0]
+                            if mime_subtype:
+                                file_extension = "." + mime_subtype
+                    except Exception as e_ext:
+                        print(f"Python WARNING: Could not parse precise extension for '{current_image_name}' from header/name: {e_ext}. Using {file_extension}.")
+                    with tempfile.NamedTemporaryFile(delete=False, suffix=file_extension, dir=TMP_DIR) as tmp_file:
+                        tmp_file.write(image_data_bytes)
+                        img_to_open_path = tmp_file.name
+                    print(f"Python INFO: Decoded base64 for '{current_image_name}' and saved to temporary file: {img_to_open_path}")
+                except Exception as e_b64:
+                    print(f"Error processing base64 image data for item {i} ('{current_image_name}'): {e_b64}")
+                    traceback.print_exc()
+                    continue
+            else:
+                print(f"Error: Unrecognized input_type '{input_type}' for item {i}. Skipping.")
+                continue
 
-        if input_type == 'url':
-            # For URLs, Gradio should have downloaded the image and put its local path in file_data_obj.get('path')
-            img_to_open_path = file_data_obj.get('path')
             if not img_to_open_path:
-                print(f"Error: For 'url' input_type, 'path' was missing in item {i}: {file_data_obj}")
-                continue # Skip this image
-            print(f"Python INFO: 'url' type. Using Gradio-provided path for '{current_image_name}': {img_to_open_path}")
-
-        elif input_type == 'base64':
-            base64_data_uri = file_data_obj.get('data_uri') # Get from 'data_uri' key sent by Node.js
-            if not base64_data_uri or not isinstance(base64_data_uri, str) or not base64_data_uri.startswith('data:image'):
-                print(f"Error: For 'base64' input_type, 'data_uri' was missing or invalid in item {i}: {file_data_obj}")
-                continue # Skip this image
-            
+                print(f"Error: No valid image path could be derived for item {i} (name: '{current_image_name}', type: '{input_type}'). Skipping.")
+                continue
             try:
-                print(f"Python INFO: 'base64' type. Decoding data_uri for '{current_image_name}'...")
-                header, encoded_data = base64_data_uri.split(',', 1)
-                image_data_bytes = base64.b64decode(encoded_data)
-                
-                file_extension = ".png" # Default
-                try:
-                    # Try to get extension from 'name' field if Node.js provided it (e.g., "uploaded_image_0.png")
-                    parsed_extension = os.path.splitext(current_image_name)[1]
-                    if parsed_extension:
-                        file_extension = parsed_extension
-                    elif '/' in header and ';' in header: # Fallback to MIME type if name had no extension
-                        mime_subtype = header.split('/')[1].split(';')[0]
-                        if mime_subtype:
-                            file_extension = "." + mime_subtype
-                except Exception as e_ext:
-                    print(f"Python WARNING: Could not parse precise extension for '{current_image_name}' from header/name: {e_ext}. Using {file_extension}.")
-
-                # Create a temporary file to write the decoded data
-                with tempfile.NamedTemporaryFile(delete=False, suffix=file_extension, dir=TMP_DIR) as tmp_file:
-                    tmp_file.write(image_data_bytes)
-                    img_to_open_path = tmp_file.name # Path to the newly saved temp file
-                
-                print(f"Python INFO: Decoded base64 for '{current_image_name}' and saved to temporary file: {img_to_open_path}")
-
-            except Exception as e_b64:
-                print(f"Error processing base64 image data for item {i} ('{current_image_name}'): {e_b64}")
-                import traceback
+                print(f"Python INFO: Opening image from path: {img_to_open_path} (intended name for prompt: {current_image_name})")
+                img = Image.open(img_to_open_path)
+                image_basenames_for_prompt.append(os.path.splitext(current_image_name)[0] or f"image_{i}")
+                if img.mode == 'RGBA' or img.mode == 'P':
+                    print(f"Converting image '{current_image_name}' from {img.mode} to RGB")
+                    img = img.convert('RGB')
+                processed_img = pipeline.preprocess_image(img)
+                pil_images.append(processed_img)
+                print(f"Image '{current_image_name}' (item {i+1}) processed successfully and added to list.")
+            except Exception as e_img_proc:
+                print(f"Error opening or processing image at '{img_to_open_path}' (item {i}, name: '{current_image_name}'): {e_img_proc}")
                 traceback.print_exc()
-                continue # Skip this image on error
-                
-        else:
-            print(f"Error: Unrecognized input_type '{input_type}' for item {i}. Skipping.")
-            continue
-
-        if not img_to_open_path:
-            print(f"Error: No valid image path could be derived for item {i} (name: '{current_image_name}', type: '{input_type}'). Skipping.")
-            continue
-
-        # Now, process the image using img_to_open_path
+            finally:
+                if input_type == 'base64' and img_to_open_path and os.path.exists(img_to_open_path):
+                    if TMP_DIR in os.path.abspath(img_to_open_path):
+                        try:
+                            os.remove(img_to_open_path)
+                            print(f"Python INFO: Removed temporary base64 file: {img_to_open_path}")
+                        except Exception as e_remove:
+                            print(f"Python WARNING: Could not remove temp file {img_to_open_path}: {e_remove}")
+                    else:
+                        print(f"Python WARNING: Skipped deletion of temp file as it's not in TMP_DIR (or was a Gradio-managed URL path): {img_to_open_path}")
+
+        if not pil_images:
+            print("Python ERROR: No valid images could be processed from the input list.")
+            raise gr.Error("No valid images could be processed.")
+        print(f"Python INFO: Total PIL images ready for pipeline: {len(pil_images)}")
+        print("Running multi-image pipeline...")
+        outputs = pipeline.run_multi_image(
+            pil_images,
+            seed=seed_val,
+            formats=["gaussian", "mesh"],
+            preprocess_image=False, 
+            sparse_structure_sampler_params={
+                "steps": ss_sampling_steps_val,
+                "cfg_strength": ss_guidance_strength_val,
+            },
+            slat_sampler_params={
+                "steps": slat_sampling_steps_val,
+                "cfg_strength": slat_guidance_strength_val,
+            },
+            mode=multiimage_algo_val,
+        )
+        print("Multi-image pipeline completed.")
+        gs_result = outputs['gaussian'][0]
+        mesh_result = outputs['mesh'][0]
+        print(f"Extracting GLB with simplify: {mesh_simplify_val}, texture_size: {texture_size_val}")
+        glb_data = postprocessing_utils.to_glb(gs_result, mesh_result, simplify=mesh_simplify_val, texture_size=texture_size_val, verbose=False)
+        temp_glb_filename = 'temp_output_image_model.glb'
+        temp_glb_path = os.path.join(user_dir, temp_glb_filename)
+        print(f"Exporting GLB to temporary path: {temp_glb_path}")
+        glb_data.export(temp_glb_path)
+        torch.cuda.empty_cache()
+        print("CUDA cache cleared.")
+        print(f"Uploading GLB from {temp_glb_path} to {NODE_SERVER_UPLOAD_URL}")
+        persistent_url = None
+        upload_prompt_name = model_description or "_".join(filter(None, image_basenames_for_prompt)) or "imagen_generated_model"
+        upload_prompt_name = "".join(c if c.isalnum() or c in ['_', '-'] else '_' for c in upload_prompt_name)[:50]
         try:
-            print(f"Python INFO: Opening image from path: {img_to_open_path} (intended name for prompt: {current_image_name})")
-            img = Image.open(img_to_open_path)
-            
-            image_basenames_for_prompt.append(os.path.splitext(current_image_name)[0] or f"image_{i}")
-
-            if img.mode == 'RGBA' or img.mode == 'P':
-                print(f"Converting image '{current_image_name}' from {img.mode} to RGB")
-                img = img.convert('RGB')
-
-            processed_img = pipeline.preprocess_image(img)
-            pil_images.append(processed_img)
-            print(f"Image '{current_image_name}' (item {i+1}) processed successfully and added to list.")
-
-        except Exception as e_img_proc:
-            print(f"Error opening or processing image at '{img_to_open_path}' (item {i}, name: '{current_image_name}'): {e_img_proc}")
-            import traceback
-            traceback.print_exc()
-            # Continue to next image if one fails, so a batch isn't entirely lost if one image is bad
-        
+            with open(temp_glb_path, "rb") as f:
+                files = {"modelFile": (temp_glb_filename, f, "model/gltf-binary")}
+                payload = {
+                    "clientType": "imagen",
+                    "prompt": upload_prompt_name,
+                    "modelStage": "imagen_mesh"
+                }
+                print(f"Upload payload to Node.js: {payload}")
+                response = requests.post(NODE_SERVER_UPLOAD_URL, files=files, data=payload, timeout=120)
+                response.raise_for_status()
+                result = response.json()
+                persistent_url = result.get("persistentUrl")
+                if not persistent_url:
+                    print(f"No persistent URL in Node.js server response: {result}")
+                    raise ValueError("Upload successful, but no persistent URL returned from Node.js server")
+                print(f"Successfully uploaded to Node server. Persistent URL: {persistent_url}")
+        except requests.exceptions.RequestException as upload_err:
+            print(f"FAILED to upload GLB to Node server: {upload_err}")
+            if hasattr(upload_err, 'response') and upload_err.response is not None:
+                print(f"Node server response status: {upload_err.response.status_code}")
+                print(f"Node server response text: {upload_err.response.text}")
+            raise gr.Error(f"Failed to upload result to backend server: {upload_err}")
+        except Exception as e:
+            print(f"UNEXPECTED error during upload: {e}", exc_info=True)
+            raise gr.Error(f"Unexpected error during upload: {e}")
         finally:
-            # Clean up the temporary file if it was created by base64 decoding and path exists
-            if input_type == 'base64' and img_to_open_path and os.path.exists(img_to_open_path):
-                # Safety check: only delete if it seems to be one we created in TMP_DIR
-                if TMP_DIR in os.path.abspath(img_to_open_path):
-                    try:
-                        os.remove(img_to_open_path)
-                        print(f"Python INFO: Removed temporary base64 file: {img_to_open_path}")
-                    except Exception as e_remove:
-                        print(f"Python WARNING: Could not remove temp file {img_to_open_path}: {e_remove}")
-                else:
-                    print(f"Python WARNING: Skipped deletion of temp file as it's not in TMP_DIR (or was a Gradio-managed URL path): {img_to_open_path}")
-
-    if not pil_images:
-        print("Python ERROR: No valid images could be processed from the input list.")
-        raise gr.Error("No valid images could be processed.")
-    
-    print(f"Python INFO: Total PIL images ready for pipeline: {len(pil_images)}")
-
-    print("Running multi-image pipeline...")
-    outputs = pipeline.run_multi_image(
-        pil_images,
-        seed=seed_val,
-        formats=["gaussian", "mesh"],
-        preprocess_image=False, 
-        sparse_structure_sampler_params={
-            "steps": ss_sampling_steps_val,
-            "cfg_strength": ss_guidance_strength_val,
-        },
-        slat_sampler_params={
-            "steps": slat_sampling_steps_val,
-            "cfg_strength": slat_guidance_strength_val,
-        },
-        mode=multiimage_algo_val,
-    )
-    print("Multi-image pipeline completed.")
-    
-    gs_result = outputs['gaussian'][0]
-    mesh_result = outputs['mesh'][0]
-
-    print(f"Extracting GLB with simplify: {mesh_simplify_val}, texture_size: {texture_size_val}")
-    glb_data = postprocessing_utils.to_glb(gs_result, mesh_result, simplify=mesh_simplify_val, texture_size=texture_size_val, verbose=False)
-    
-    temp_glb_filename = 'temp_output_image_model.glb'
-    temp_glb_path = os.path.join(user_dir, temp_glb_filename)
-    print(f"Exporting GLB to temporary path: {temp_glb_path}")
-    glb_data.export(temp_glb_path)
-    
-    torch.cuda.empty_cache()
-    print("CUDA cache cleared.")
-
-    print(f"Uploading GLB from {temp_glb_path} to {NODE_SERVER_UPLOAD_URL}")
-    persistent_url = None
-    upload_prompt_name = model_description or "_".join(filter(None, image_basenames_for_prompt)) or "imagen_generated_model"
-    upload_prompt_name = "".join(c if c.isalnum() or c in ['_', '-'] else '_' for c in upload_prompt_name)[:50]
-
-
-    try:
-        with open(temp_glb_path, "rb") as f:
-            files = {"modelFile": (temp_glb_filename, f, "model/gltf-binary")}
-            payload = {
-                "clientType": "imagen",
-                "prompt": upload_prompt_name,
-                "modelStage": "imagen_mesh"
-            }
-            print(f"Upload payload to Node.js: {payload}")
-            response = requests.post(NODE_SERVER_UPLOAD_URL, files=files, data=payload, timeout=120)
-            response.raise_for_status()
-            result = response.json()
-            persistent_url = result.get("persistentUrl")
-            if not persistent_url:
-                print(f"No persistent URL in Node.js server response: {result}")
-                raise ValueError("Upload successful, but no persistent URL returned from Node.js server")
-            print(f"Successfully uploaded to Node server. Persistent URL: {persistent_url}")
-    except requests.exceptions.RequestException as upload_err:
-        print(f"FAILED to upload GLB to Node server: {upload_err}")
-        if hasattr(upload_err, 'response') and upload_err.response is not None:
-            print(f"Node server response status: {upload_err.response.status_code}")
-            print(f"Node server response text: {upload_err.response.text}")
-        raise gr.Error(f"Failed to upload result to backend server: {upload_err}")
-    except Exception as e:
-        print(f"UNEXPECTED error during upload: {e}", exc_info=True)
-        raise gr.Error(f"Unexpected error during upload: {e}")
-    finally:
-        if os.path.exists(temp_glb_path):
-            print(f"Cleaning up temporary GLB: {temp_glb_path}")
-            os.remove(temp_glb_path)
-
-    if not persistent_url:
-        print("Failed to obtain a persistent URL for the generated model.")
-        raise gr.Error("Failed to obtain a persistent URL for the generated model.")
-
-    print(f"Returning persistent URL: {persistent_url}")
-    return persistent_url
+            if os.path.exists(temp_glb_path):
+                print(f"Cleaning up temporary GLB: {temp_glb_path}")
+                os.remove(temp_glb_path)
+        if not persistent_url:
+            print("Failed to obtain a persistent URL for the generated model.")
+            raise gr.Error("Failed to obtain a persistent URL for the generated model.")
+        print(f"Returning persistent URL: {persistent_url}")
+        return persistent_url
+
+    except Exception as e_main:
+        print(f"TOP LEVEL PYTHON ERROR IN generate_model_from_images_and_upload: {e_main}")
+        traceback.print_exc()
+        raise gr.Error(f"A critical error occurred in the Python backend: {e_main}")
 
 # Interfaz Gradio
 with gr.Blocks(delete_cache=(600, 600)) as demo: