fix(comfyui): restore missing custom nodes and worker functionality

- Restore __init__.py with full node registration and SQS worker startup
- Add missing controllers.py with NilorPreset and NilorGroup nodes
- Add missing user_input.py with NilorUserInput_* nodes
- Add missing web/js/media_stream.js for MediaStreamOutput UI extensions
- Add missing web/js/controllers.js for controller node UI extensions
- Add missing worker_consumer.py for SQS job processing
- Add missing .env.example for environment configuration
- Fixes missing custom nodes and SQS worker functionality on working branch

.gitignore

@@ -157,4 +157,4 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-.idea/
+#.idea/

README.md

@@ -2,10 +2,6 @@
 
 A collection of utility nodes for ComfyUI focusing on list manipulation, batch operations, and advanced I/O functionality.
 
-## Prerequisites
-
-- `comfyui-kjnodes` custom_nodes repo
-
 ## 🏭 Generators
 
 <details>
@@ -192,31 +188,4 @@ Uploads video files to a HuggingFace dataset.
 | filename_prefix | STRING | Prefix for saved files |
 
 **Notes**: Handles batch upload of multiple video files.
-</details>
-
-## 📡 Core Nilor Services
-
-<details>
-<summary><b>Worker Consumer Service</b></summary>
-
-The `worker_consumer.py` script is a background service that runs on each ComfyUI worker. It is responsible for pulling jobs from the central ElasticMQ `jobs_to_process` queue and submitting them to its local ComfyUI instance for processing. This service is essential for the distributed architecture of the system.
-
-**Key Responsibilities:**
--   Continuously polls the `jobs_to_process` queue for new jobs using long polling.
--   When a job is received, it extracts the workflow data and submits it to the local ComfyUI server.
--   Deletes the job message from the queue upon successful submission to prevent reprocessing.
--   If submission fails, the message remains on the queue to be picked up by another worker.
-
-</details>
-
-<details>
-<summary><b>Environment Variables</b></summary>
-
-The `nilor-nodes` require a `.env` file to be present in the `ComfyUI` directory to configure the connection to the core services (MinIO, ElasticMQ, and the Brain API). To set it up, create a file named `.env` in the root of your `ComfyUI` directory by copying the `.env.example` template.
-
-**Instructions:**
-1.  Create a new file named `.env` in the `ComfyUI` directory.
-2.  Copy the contents of the `.env.example` file into your new `.env` file.
-3.  Replace the placeholder values with your actual credentials and endpoint URLs for your local or production environment.
-
 </details>

brain_api_client.py

@@ -247,92 +247,6 @@ class BrainApiClient:
             logging.error(f"Unexpected error deleting file '{filename}': {e}")
             raise
     
-    def get_presigned_upload_url(self, filename: str, content_type: str, minio_endpoint: str) -> Dict[str, Any]:
-        """
-        Get a presigned upload URL from Brain API for direct MinIO upload.
-        
-        Args:
-            filename: Name of the file to upload
-            content_type: MIME type of the file
-            minio_endpoint: MinIO endpoint that ComfyUI can access
-            
-        Returns:
-            Dict containing storage_id, upload_url, and object_key
-            
-        Raises:
-            requests.RequestException: If request fails
-        """
-        url = f"{self.base_url}/storage/generate-upload-url"
-        payload = {
-            "filename": filename,
-            "content_type": content_type,
-            "minio_endpoint": minio_endpoint
-        }
-        
-        try:
-            logging.info(f"Requesting presigned upload URL for '{filename}' from Brain API...")
-            response = requests.post(
-                url,
-                json=payload,
-                headers=self.headers,
-                timeout=30
-            )
-            response.raise_for_status()
-            
-            result = response.json()
-            logging.info(f"Presigned upload URL generated. Storage ID: {result.get('storage_id')}")
-            return result
-            
-        except requests.RequestException as e:
-            logging.error(f"Failed to get presigned upload URL for '{filename}': {e}")
-            raise
-        except Exception as e:
-            logging.error(f"Unexpected error getting presigned upload URL for '{filename}': {e}")
-            raise
-    
-    def get_presigned_download_url(self, storage_id: str, filename: str, minio_endpoint: str) -> Dict[str, Any]:
-        """
-        Get a presigned download URL from Brain API for direct MinIO download.
-        
-        Args:
-            storage_id: Storage ID of the file to download
-            filename: Name of the file to download
-            minio_endpoint: MinIO endpoint that ComfyUI can access
-            
-        Returns:
-            Dict containing download_url
-            
-        Raises:
-            requests.RequestException: If request fails
-        """
-        url = f"{self.base_url}/storage/generate-download-url"
-        payload = {
-            "storage_id": storage_id,
-            "filename": filename,
-            "minio_endpoint": minio_endpoint
-        }
-        
-        try:
-            logging.info(f"Requesting presigned download URL for '{filename}' (storage_id: {storage_id}) from Brain API...")
-            response = requests.post(
-                url,
-                json=payload,
-                headers=self.headers,
-                timeout=30
-            )
-            response.raise_for_status()
-            
-            result = response.json()
-            logging.info(f"Presigned download URL generated for '{filename}'")
-            return result
-            
-        except requests.RequestException as e:
-            logging.error(f"Failed to get presigned download URL for '{filename}' (storage_id: {storage_id}): {e}")
-            raise
-        except Exception as e:
-            logging.error(f"Unexpected error getting presigned download URL for '{filename}': {e}")
-            raise
-
     def health_check(self) -> bool:
         """
         Check if the Brain API is accessible and authentication is working.

media_stream.py

@@ -78,22 +78,14 @@ class MediaStreamInput:
             f"ℹ️\u2009 Nilor-Nodes: MediaStreamInput: Downloading file '{filename}' (storage_id: {storage_id}) for input '{input_name}' with format '{format}'"
         )
         try:
-            # Get Brain API client and MinIO endpoint
+            # Get Brain API client
             brain_client = get_brain_api_client()
-            minio_endpoint = os.getenv("MINIO_ENDPOINT")
-            if not minio_endpoint:
-                raise ValueError("MINIO_ENDPOINT environment variable is required but not set")
             
             # Two-phase download for batches: manifest first, then assets
             if format == "image_batch":
-                # Get presigned download URL for manifest
-                manifest_url_response = brain_client.get_presigned_download_url(storage_id, filename, minio_endpoint)
-                manifest_url = manifest_url_response["download_url"]
-                
-                # Download manifest file directly from MinIO
-                manifest_response = requests.get(manifest_url, timeout=300)
-                manifest_response.raise_for_status()
-                manifest = json.loads(manifest_response.content.decode('utf-8'))
+                # Download manifest file first
+                manifest_bytes = brain_client.get_file_from_storage(storage_id, filename)
+                manifest = json.loads(manifest_bytes.decode('utf-8'))
 
                 logging.info(
                     f"ℹ️\u2009 Nilor-Nodes: Processing manifest for '{manifest.get('input_name')}' with {len(manifest.get('files', []))} assets."
@@ -104,23 +96,18 @@ class MediaStreamInput:
                     manifest.get("files", []), key=lambda x: x.get("sequence", 0)
                 )
 
-                # Download all assets using presigned URLs
+                # Download all assets using Brain API client
                 asset_responses = []
                 for file_info in sorted_files:
                     try:
+                        # Each file_info should now contain storage_id and filename instead of presigned_url
                         file_storage_id = file_info.get("storage_id")
                         file_filename = file_info.get("filename")
                         if not file_storage_id or not file_filename:
                             raise ValueError(f"Missing storage_id or filename in manifest file info: {file_info}")
                         
-                        # Get presigned download URL for this asset
-                        asset_url_response = brain_client.get_presigned_download_url(file_storage_id, file_filename, minio_endpoint)
-                        asset_url = asset_url_response["download_url"]
-                        
-                        # Download asset directly from MinIO
-                        asset_response = requests.get(asset_url, timeout=300)
-                        asset_response.raise_for_status()
-                        asset_responses.append(asset_response.content)
+                        file_bytes = brain_client.get_file_from_storage(file_storage_id, file_filename)
+                        asset_responses.append(file_bytes)
                     except Exception as e:
                         logging.error(
                             f"🛑\u2009 Nilor-Nodes: Failed to download asset {file_info.get('filename')}: {e}"
@@ -130,14 +117,7 @@ class MediaStreamInput:
                 return self._process_image_batch(asset_responses)
 
             # --- Single-file download ---
-            # Get presigned download URL
-            download_url_response = brain_client.get_presigned_download_url(storage_id, filename, minio_endpoint)
-            download_url = download_url_response["download_url"]
-            
-            # Download file directly from MinIO
-            media_response = requests.get(download_url, timeout=300)
-            media_response.raise_for_status()
-            media_bytes = media_response.content
+            media_bytes = brain_client.get_file_from_storage(storage_id, filename)
 
             if format == "video":
                 return self._process_video(media_bytes)
@@ -293,15 +273,15 @@ class MediaStreamOutput:
             storage_result = self._upload_video(images, brain_client, framerate, output_name)
 
         # Use the storage_id from the upload result for the SQS message
-        if not storage_result:
+        if not storage_result or not storage_result.get('storage_id'):
             logging.error(
                 f"🛑\u2009 Nilor-Nodes (MediaStreamOutput): FATAL -- Upload failed or no storage_id returned."
             )
             # Send an empty dictionary to signal failure.
             final_outputs_for_sqs = {}
         else:
-            # Use storage_id directly (it's now a string, not a dict)
-            storage_id = storage_result
+            # Use storage_id instead of object key
+            storage_id = storage_result['storage_id']
             final_outputs_for_sqs = {output_name: storage_id}
 
         # After upload, send the filtered dictionary of outputs to the SQS queue.
@@ -353,27 +333,7 @@ class MediaStreamOutput:
         buffer.seek(0)
 
         filename = f"{output_name}.png"
-        minio_endpoint = os.getenv("MINIO_ENDPOINT")
-        if not minio_endpoint:
-            raise ValueError("MINIO_ENDPOINT environment variable is required but not set")
-        
-        # Get presigned upload URL
-        upload_url_response = brain_client.get_presigned_upload_url(filename, "image/png", minio_endpoint)
-        upload_url = upload_url_response["upload_url"]
-        storage_id = upload_url_response["storage_id"]
-        
-        # Upload directly to MinIO
-        buffer.seek(0)
-        upload_response = requests.put(
-            upload_url,
-            data=buffer.getvalue(),
-            headers={"Content-Type": "image/png"},
-            timeout=300
-        )
-        upload_response.raise_for_status()
-        
-        logging.info(f"✅ Nilor-Nodes (MediaStreamOutput): PNG image uploaded successfully. Storage ID: {storage_id}")
-        return storage_id
+        return brain_client.upload_fileobj_to_storage(buffer, filename, "image/png")
 
     def _upload_video(self, image_batch_tensor, brain_client, framerate, output_name):
         logging.info(
@@ -390,27 +350,7 @@ class MediaStreamOutput:
         buffer.seek(0)
 
         filename = f"{output_name}.mp4"
-        minio_endpoint = os.getenv("MINIO_ENDPOINT")
-        if not minio_endpoint:
-            raise ValueError("MINIO_ENDPOINT environment variable is required but not set")
-        
-        # Get presigned upload URL
-        upload_url_response = brain_client.get_presigned_upload_url(filename, "video/mp4", minio_endpoint)
-        upload_url = upload_url_response["upload_url"]
-        storage_id = upload_url_response["storage_id"]
-        
-        # Upload directly to MinIO
-        buffer.seek(0)
-        upload_response = requests.put(
-            upload_url,
-            data=buffer.getvalue(),
-            headers={"Content-Type": "video/mp4"},
-            timeout=300
-        )
-        upload_response.raise_for_status()
-        
-        logging.info(f"✅ Nilor-Nodes (MediaStreamOutput): MP4 video uploaded successfully. Storage ID: {storage_id}")
-        return storage_id
+        return brain_client.upload_fileobj_to_storage(buffer, filename, "video/mp4")
 
 
 

nilornodes.py

@@ -16,24 +16,7 @@ import torch
 import builtins
 from pathlib import Path
 import cv2
-import warnings
 from .utils import pil2tensor, tensor2pil
-import logging
-from comfy.utils import common_upscale
-from comfy import model_management
-import sys
-from os.path import dirname, join
-
-# Attempt to import ImagePadKJ from comfyui-kjnodes if available
-_kj_nodes_path = join(dirname(__file__), "..", "comfyui-kjnodes", "nodes")
-if _kj_nodes_path not in sys.path:
-    sys.path.append(_kj_nodes_path)
-try:
-    from image_nodes import ImagePadKJ  # type: ignore
-except Exception as _e:
-    logging.warning(
-        f"⚠️\u2009 Nilor-Nodes (nilornodes): Could not import ImagePadKJ from comfyui-kjnodes ({_kj_nodes_path}): {_e}"
-    )
 
 BIGMIN = -(2**53 - 1)
 BIGMAX = 2**53 - 1
@@ -183,9 +166,7 @@ class NilorRemapFloatList:
     ):
         # Avoid division by zero
         if max_input - min_input == 0:
-            raise ValueError(
-                "🛑\u2009 Nilor-Nodes (RemapFloatList): max_input and min_input cannot be the same value."
-            )
+            raise ValueError("max_input and min_input cannot be the same value.")
 
         scale = (max_output - min_output) / (max_input - min_input)
         return ([min_output + (x - min_input) * scale for x in list_of_floats],)
@@ -240,9 +221,7 @@ class NilorInverseMapFloatList:
 
     def inverse_map_float_list(self, list_of_floats):
         if not list_of_floats:
-            raise ValueError(
-                "🛑\u2009 Nilor-Nodes (InverseMapFloatList): The input list_of_floats cannot be empty."
-            )
+            raise ValueError("The input list_of_floats cannot be empty.")
 
         min_input = min(list_of_floats)
         max_input = max(list_of_floats)
@@ -338,9 +317,7 @@ class NilorCountImagesInDirectory:
 
     def count_images_in_directory(self, directory):
         if not os.path.isdir(directory):
-            raise FileNotFoundError(
-                f"🛑\u2009 Nilor-Nodes (NilorCountImagesInDirectory): Directory '{directory}' cannot be found."
-            )
+            raise FileNotFoundError(f"Directory '{directory} cannot be found.")
 
         list_dir = []
         list_dir = os.listdir(directory)
@@ -388,9 +365,7 @@ class NilorSelectIndexFromList:
 
         # Ensure the index is within bounds
         if index < 0 or index >= len(actual_list):
-            raise ValueError(
-                "🛑\u2009 Nilor-Nodes (SelectIndexFromList): Index is outside the bounds of the array."
-            )
+            raise ValueError("Index is outside the bounds of the array.")
 
         # Returns the value at the given index
         return (actual_list[index],)
@@ -428,9 +403,7 @@ class NilorSaveEXRArbitrary:
         self, channels=None, filename_prefix="output", prompt=None, extra_pnginfo=None
     ):
 
-        logging.info(
-            "ℹ️\u2009 Nilor-Nodes (SaveEXRArbitrary): Running save_exr_arbitrary"
-        )
+        print("Running save_exr_arbitrary")
         # print(f"channels: {channels}")
         # print(f"filename_prefix: {filename_prefix}")
 
@@ -442,9 +415,7 @@ class NilorSaveEXRArbitrary:
         try:
             actual_channels[0]
         except TypeError:
-            logging.error(
-                "🛑\u2009 Nilor-Nodes (SaveEXRArbitrary): actual_channels is not subscriptable"
-            )
+            print("actual_channels is not subscriptable")
             return
 
         # File path handling
@@ -481,9 +452,7 @@ class NilorSaveEXRArbitrary:
             height, width = image_channels[0].shape[-2:]
             for tensor in image_channels:
                 if tensor.shape[-2:] != (height, width):
-                    raise ValueError(
-                        "🛑\u2009 Nilor-Nodes (SaveEXRArbitrary): All input tensors must have the same dimensions"
-                    )
+                    raise ValueError("All input tensors must have the same dimensions")
 
             # Channel naming
             default_names = ["R", "G", "B", "A"] + [
@@ -533,13 +502,9 @@ class NilorSaveEXRArbitrary:
             exr_file.writePixels(channel_data)
             exr_file.close()
 
-            logging.info(
-                f"✅\u2009 Nilor-Nodes (SaveEXRArbitrary): EXR file saved successfully to {writepath}"
-            )
+            print(f"EXR file saved successfully to {writepath}")
         except Exception as e:
-            logging.error(
-                f"🛑\u2009 Nilor-Nodes (SaveEXRArbitrary): Failed to write EXR file: {e}"
-            )
+            print(f"Failed to write EXR file: {e}")
 
 
 class NilorSaveVideoToHFDataset:
@@ -657,14 +622,12 @@ class NilorShuffleImageBatch:
 
     def _check_image_dimensions(self, images):
         if images.shape[0] == 0:
-            raise ValueError(
-                "🛑\u2009 Nilor-Nodes (ShuffleImageBatch): Input images tensor is empty."
-            )
+            raise ValueError("Input images tensor is empty.")
 
         # All images in the batch should have the same dimensions
         if len(images.shape) != 4:
             raise ValueError(
-                f"🛑\u2009 Nilor-Nodes (ShuffleImageBatch): Expected 4D tensor (batch, channels, height, width), got shape {images.shape}"
+                f"Expected 4D tensor (batch, channels, height, width), got shape {images.shape}"
             )
 
     def shuffle_image_batch(self, images: torch.Tensor, seed):
@@ -704,14 +667,12 @@ class NilorRepeatTrimImageBatch:
 
     def _check_image_dimensions(self, images):
         if images.shape[0] == 0:
-            raise ValueError(
-                "🛑\u2009 Nilor-Nodes (RepeatTrimImageBatch): Input images tensor is empty."
-            )
+            raise ValueError("Input images tensor is empty.")
 
         # All images in the batch should have the same dimensions
         if len(images.shape) != 4:
             raise ValueError(
-                f"🛑\u2009 Nilor-Nodes (RepeatTrimImageBatch): Expected 4D tensor (batch, channels, height, width), got shape {images.shape}"
+                f"Expected 4D tensor (batch, channels, height, width), got shape {images.shape}"
             )
 
     def repeat_trim_image_batch(self, images: torch.Tensor, count):
@@ -749,14 +710,12 @@ class NilorRepeatShuffleTrimImageBatch:
 
     def _check_image_dimensions(self, images):
         if images.shape[0] == 0:
-            raise ValueError(
-                "🛑\u2009 Nilor-Nodes (RepeatShuffleTrimImageBatch): Input images tensor is empty."
-            )
+            raise ValueError("Input images tensor is empty.")
 
         # All images in the batch should have the same dimensions
         if len(images.shape) != 4:
             raise ValueError(
-                f"🛑\u2009 Nilor-Nodes (RepeatShuffleTrimImageBatch): Expected 4D tensor (batch, channels, height, width), got shape {images.shape}"
+                f"Expected 4D tensor (batch, channels, height, width), got shape {images.shape}"
             )
 
     def repeat_shuffle_trim_image_batch(self, images: torch.Tensor, seed, count):
@@ -822,16 +781,12 @@ class NilorOutputFilenameString:
 
         if unique_id is not None and extra_pnginfo is not None:
             if not isinstance(extra_pnginfo, list):
-                logging.error(
-                    "🛑\u2009 Nilor-Nodes (OutputFilenameString): extra_pnginfo is not a list"
-                )
+                print("Error: extra_pnginfo is not a list")
             elif (
                 not isinstance(extra_pnginfo[0], dict)
                 or "workflow" not in extra_pnginfo[0]
             ):
-                logging.error(
-                    "🛑\u2009 Nilor-Nodes (OutputFilenameString): extra_pnginfo[0] is not a dict or missing 'workflow' key"
-                )
+                print("Error: extra_pnginfo[0] is not a dict or missing 'workflow' key")
             else:
                 workflow = extra_pnginfo[0]["workflow"]
                 node = next(
@@ -882,206 +837,7 @@ class NilorNFractionsOfInt:
         elif type == "start + end":
             return ([i * numerator // (denominator - 1) for i in range(denominator)],)
         else:
-            raise ValueError(
-                f"🛑\u2009 Nilor-Nodes (NilorNFractionsOfInt): Unknown type: {type}"
-            )
-
-
-class NilorWanTileResolution:
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "input_width": (
-                    "INT",
-                    {"default": 1920, "min": 16, "max": BIGMAX, "step": 1},
-                ),
-                "input_height": (
-                    "INT",
-                    {"default": 1080, "min": 16, "max": BIGMAX, "step": 1},
-                ),
-                "target_width": (
-                    "INT",
-                    {"default": 3840, "min": 16, "max": BIGMAX, "step": 1},
-                ),
-                "target_height": (
-                    "INT",
-                    {"default": 2160, "min": 16, "max": BIGMAX, "step": 1},
-                ),
-                "size_preference": (
-                    ["largest", "smallest"],
-                    {"default": "largest"},
-                ),
-            }
-        }
-
-    RETURN_TYPES = ("INT", "INT")
-    RETURN_NAMES = ("tile_width", "tile_height")
-
-    FUNCTION = "compute_tile_resolution"
-    CATEGORY = category + subcategories["utilities"]
-
-    MIN_TILE_DIM = 384
-    MAX_TILE_DIM = 1794
-    MIN_TILE_AREA = 384 * 384
-    MAX_TILE_AREA = 1024 * 1024
-
-    @staticmethod
-    def _clamp(value, minimum, maximum):
-        return max(minimum, min(value, maximum))
-
-    def compute_tile_resolution(
-        self,
-        input_width,
-        input_height,
-        target_width,
-        target_height,
-        size_preference="largest",
-    ):
-        """
-        Compute (Wt, Ht) tile size (multiples of 16) within
-        [MIN_TILE_DIM, MAX_TILE_DIM] while keeping area between
-        [MIN_TILE_AREA, MAX_TILE_AREA]. Emphasise aspect-ratio fidelity to
-        Wa/Ha while staying within the allowed range.
-
-        Among options with comparable aspect error, prefer tiles that do
-        not hit clamped bounds, then maximise area and width (or minimise both if
-        size_preference == "smallest").
-
-        Assumes Wa, Ha are multiples of 16.
-        """
-
-        dims = {
-            "input_width": input_width,
-            "input_height": input_height,
-            "target_width": target_width,
-            "target_height": target_height,
-        }
-
-        for name, value in dims.items():
-            if value <= 0:
-                raise ValueError(
-                    f"🛑\u2009 Nilor-Nodes (NilorWanTileResolution): {name} must be a positive integer."
-                )
-
-        if input_width % 16 != 0 or input_height % 16 != 0:
-            raise ValueError(
-                "🛑\u2009 Nilor-Nodes (NilorWanTileResolution): input_width and input_height must be multiples of 16."
-            )
-
-        if target_width < self.MIN_TILE_DIM or target_height < self.MIN_TILE_DIM:
-            raise ValueError(
-                "🛑\u2009 Nilor-Nodes (NilorWanTileResolution): target_width and target_height must be at least the minimum tile size."
-            )
-
-        min_blocks = self.MIN_TILE_DIM // 16
-        max_blocks = self.MAX_TILE_DIM // 16
-
-        max_width_blocks = min(max_blocks, target_width // 16)
-        max_height_blocks = min(max_blocks, target_height // 16)
-
-        if max_width_blocks < min_blocks or max_height_blocks < min_blocks:
-            raise ValueError(
-                "🛑\u2009 Nilor-Nodes (NilorWanTileResolution): Target dimensions do not allow a tile within the supported range."
-            )
-
-        aspect_ratio = input_width / input_height
-
-        best_score = None
-        best_dimensions = None
-
-        for height_blocks in range(min_blocks, max_height_blocks + 1):
-            width_blocks = round(aspect_ratio * height_blocks)
-            width_blocks = self._clamp(width_blocks, min_blocks, max_width_blocks)
-
-            width_px = width_blocks * 16
-            height_px = height_blocks * 16
-
-            area = width_px * height_px
-            if area < self.MIN_TILE_AREA or area > self.MAX_TILE_AREA:
-                # Skip tiles that are too small or too large
-                continue
-            aspect_error = abs((width_blocks / height_blocks) - aspect_ratio)
-
-            width_hits_bound = int(width_blocks in (min_blocks, max_width_blocks))
-            height_hits_bound = int(height_blocks in (min_blocks, max_height_blocks))
-
-            # Penalise tiles that hit the clamped bounds
-            bound_penalty = width_hits_bound + height_hits_bound
-
-            # Score tiles based on size preference
-            if size_preference == "smallest":
-                area_score = -area
-                width_score = -width_px
-            else:
-                area_score = area
-                width_score = width_px
-
-            # Combine scores
-            candidate = (-aspect_error, -bound_penalty, area_score, width_score)
-
-            if best_score is None or candidate > best_score:
-                # Update best score and dimensions if this candidate is better
-                best_score = candidate
-                best_dimensions = (width_px, height_px)
-
-        if best_dimensions is None:
-            # If no suitable tile resolution was found, raise an error
-            raise RuntimeError(
-                "🛑\u2009 Nilor-Nodes (NilorWanTileResolution): Failed to determine a suitable tile resolution."
-            )
-
-        return best_dimensions
-
-
-class NilorWanFrameTrim:
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "images": ("IMAGE",),
-            },
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("images",)
-    FUNCTION = "trim_to_wan_count"
-    CATEGORY = category + subcategories["utilities"]
-
-    def _validate_images(self, images):
-        if not isinstance(images, torch.Tensor):
-            raise TypeError(
-                "🛑\u2009 Nilor-Nodes (WanFrameTrim): images must be a torch.Tensor."
-            )
-        if images.dim() != 4:
-            raise ValueError(
-                f"🛑\u2009 Nilor-Nodes (WanFrameTrim): Expected 4D tensor (batch, height, width, channels), got shape {tuple(images.shape)}"
-            )
-        if images.shape[0] == 0:
-            raise ValueError(
-                "🛑\u2009 Nilor-Nodes (WanFrameTrim): Input images tensor is empty."
-            )
-
-    def trim_to_wan_count(self, images: torch.Tensor):
-        self._validate_images(images)
-
-        batch_count = images.shape[0]
-        # Find the largest m <= batch_count such that m ≡ 1 (mod 4)
-        wan_count = batch_count - ((batch_count - 1) % 4)
-
-        if wan_count <= 0:
-            raise ValueError(
-                "🛑\u2009 Nilor-Nodes (WanFrameTrim): Unable to compute a valid 4N+1 frame count from input."
-            )
-
-        trimmed = images[:wan_count]
-        return (trimmed,)
+            raise ValueError(f"Unknown type: {type}")
 
 
 class NilorCategorizeString:
@@ -1203,9 +959,7 @@ class NilorRandomString:
             if item.strip()
         ]
         if not options:
-            raise ValueError(
-                "🛑\u2009 Nilor-Nodes (NilorRandomString): No valid choices provided."
-            )
+            raise ValueError("No valid choices provided.")
 
         # Limit to the first 'max_options' entries if there are more options
         if len(options) > max_options:
@@ -1247,9 +1001,7 @@ class NilorLoadImageByIndex:
 
     def load_image_by_index(self, image_directory, seed, sort_mode, reverse_sort):
         if not os.path.exists(image_directory):
-            raise FileNotFoundError(
-                f"🛑\u2009 Nilor-Nodes (NilorLoadImageByIndex): Image directory {image_directory} does not exist"
-            )
+            raise FileNotFoundError(f"Image directory {image_directory} does not exist")
 
         # Get list of image files
         files = []
@@ -1261,9 +1013,7 @@ class NilorLoadImageByIndex:
                 files.append(file_path)
 
         if not files:
-            raise ValueError(
-                f"🛑\u2009 Nilor-Nodes (NilorLoadImageByIndex): No image files found in {image_directory}"
-            )
+            raise ValueError(f"No image files found in {image_directory}")
 
         # Sort files based on selected mode
         if sort_mode == "filename":
@@ -1313,9 +1063,7 @@ class NilorExtractFilenameFromPath:
     def extract_filename(self, filepath):
         # Ensure the input is a valid path
         if not filepath:
-            raise ValueError(
-                "🛑\u2009 Nilor-Nodes (ExtractFilenameFromPath): Filepath cannot be empty."
-            )
+            raise ValueError("Filepath cannot be empty.")
 
         path = Path(filepath)
 
@@ -1350,18 +1098,14 @@ class NilorBlurAnalysis:
         """
         # Ensure images is a 4D tensor.
         if images.dim() != 4:
-            raise ValueError(
-                "🛑\u2009 Nilor-Nodes (BlurAnalysis): Input images must be a 4D tensor (batch, channels/height, height/width, width/channels)"
-            )
+            raise ValueError("Input images must be a 4D tensor (batch, channels/height, height/width, width/channels)")
 
         # Detect if using NCHW or NHWC.
         if images.shape[1] not in (1, 3):
             if images.shape[-1] in (1, 3):
                 images = images.permute(0, 3, 1, 2)
             else:
-                raise ValueError(
-                    "🛑\u2009 Nilor-Nodes (BlurAnalysis): Cannot determine image format (expected channel to be 1 or 3)."
-                )
+                raise ValueError("Cannot determine image format (expected channel to be 1 or 3).")
 
         output_images = []
         batch_size = images.shape[0]
@@ -1372,7 +1116,9 @@ class NilorBlurAnalysis:
 
             # Convert to grayscale.
             if img_np.shape[0] >= 3:
-                gray = 0.299 * img_np[0] + 0.587 * img_np[1] + 0.114 * img_np[2]
+                gray = (0.299 * img_np[0] +
+                        0.587 * img_np[1] +
+                        0.114 * img_np[2])
             else:
                 gray = np.squeeze(img_np, axis=0)  # shape: (H, W)
 
@@ -1400,9 +1146,7 @@ class NilorBlurAnalysis:
             # Convert the single channel output to a 3-channel image.
             # This ensures downstream nodes (like MaskFromRGBCMYBW) that index into channels work properly.
             if out_img.ndim == 2:
-                out_img = np.stack(
-                    [out_img, out_img, out_img], axis=-1
-                )  # shape becomes (H, W, 3)
+                out_img = np.stack([out_img, out_img, out_img], axis=-1)  # shape becomes (H, W, 3)
 
             # Convert from PIL image (or numpy array) to tensor.
             # pil2tensor should create a tensor in a format that downstream nodes expect.
@@ -1413,7 +1157,6 @@ class NilorBlurAnalysis:
         # If each output has shape, say, (H, W, 3), stacking them gives a tensor of shape (B, H, W, 3).
         return (torch.cat(output_images, dim=0),)
 
-
 class NilorToSparseIndexMethod:
     def __init__(self):
         pass
@@ -1436,315 +1179,10 @@ class NilorToSparseIndexMethod:
 
     def convert_to_sparse_index_method(self, ints):
         indexes_str = ",".join(map(str, ints))
-
+        
         return (indexes_str,)
 
 
-class NilorImageResizeV2:
-    upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "width": ("INT", {"default": 512, "min": 0, "max": BIGMAX, "step": 1}),
-                "height": ("INT", {"default": 512, "min": 0, "max": BIGMAX, "step": 1}),
-                "upscale_method": (s.upscale_methods,),
-                "keep_proportion": (
-                    [
-                        "stretch",
-                        "resize",
-                        "pad",
-                        "pad_edge",
-                        "pad_edge_pixel",
-                        "crop",
-                        "pillarbox_blur",
-                    ],
-                    {"default": False},
-                ),
-                "pad_color": ("STRING", {"default": "0, 0, 0"}),
-                "crop_position": (
-                    ["center", "top", "bottom", "left", "right"],
-                    {"default": "center"},
-                ),
-                "divisible_by": (
-                    "INT",
-                    {"default": 2, "min": 0, "max": 512, "step": 1},
-                ),
-            },
-            "optional": {
-                "mask": ("MASK",),
-                "device": (["cpu", "gpu"],),
-                "per_batch": (
-                    "INT",
-                    {
-                        "default": 16,
-                        "min": 0,
-                        "max": 4096,
-                        "step": 1,
-                        "tooltip": "Process images in sub-batches. 0 disables.",
-                    },
-                ),
-            },
-            "hidden": {"unique_id": "UNIQUE_ID"},
-        }
-
-    RETURN_TYPES = ("IMAGE", "INT", "INT", "MASK")
-    RETURN_NAMES = ("IMAGE", "width", "height", "mask")
-    FUNCTION = "resize"
-    CATEGORY = category + subcategories["utilities"]
-    DESCRIPTION = """
-Resizes images with optional aspect preservation, padding/cropping, and sub-batching to lower peak memory.
-"""
-
-    def resize(
-        self,
-        image,
-        width,
-        height,
-        keep_proportion,
-        upscale_method,
-        divisible_by,
-        pad_color,
-        crop_position,
-        unique_id,
-        device="cpu",
-        mask=None,
-        per_batch=16,
-    ):
-        B, H, W, C = image.shape
-
-        if device == "gpu":
-            if upscale_method == "lanczos":
-                raise Exception(
-                    "🛑\u2009 Nilor-Nodes (NilorImageResizeV2): Lanczos is not supported on the GPU"
-                )
-            device = model_management.get_torch_device()
-        else:
-            device = torch.device("cpu")
-
-        if width == 0:
-            width = W
-        if height == 0:
-            height = H
-
-        pillarbox_blur = keep_proportion == "pillarbox_blur"
-        if (
-            keep_proportion == "resize"
-            or keep_proportion.startswith("pad")
-            or pillarbox_blur
-        ):
-            if width == 0 and height != 0:
-                ratio = height / H
-                new_width = round(W * ratio)
-                new_height = height
-            elif height == 0 and width != 0:
-                ratio = width / W
-                new_width = width
-                new_height = round(H * ratio)
-            elif width != 0 and height != 0:
-                ratio = min(width / W, height / H)
-                new_width = round(W * ratio)
-                new_height = round(H * ratio)
-            else:
-                new_width = width
-                new_height = height
-
-            pad_left = pad_right = pad_top = pad_bottom = 0
-            if keep_proportion.startswith("pad") or pillarbox_blur:
-                if crop_position == "center":
-                    pad_left = (width - new_width) // 2
-                    pad_right = width - new_width - pad_left
-                    pad_top = (height - new_height) // 2
-                    pad_bottom = height - new_height - pad_top
-                elif crop_position == "top":
-                    pad_left = (width - new_width) // 2
-                    pad_right = width - new_width - pad_left
-                    pad_top = 0
-                    pad_bottom = height - new_height
-                elif crop_position == "bottom":
-                    pad_left = (width - new_width) // 2
-                    pad_right = width - new_width - pad_left
-                    pad_top = height - new_height
-                    pad_bottom = 0
-                elif crop_position == "left":
-                    pad_left = 0
-                    pad_right = width - new_width
-                    pad_top = (height - new_height) // 2
-                    pad_bottom = height - new_height - pad_top
-                elif crop_position == "right":
-                    pad_left = width - new_width
-                    pad_right = 0
-                    pad_top = (height - new_height) // 2
-                    pad_bottom = height - new_height - pad_top
-
-            width = new_width
-            height = new_height
-
-        if divisible_by > 1:
-            width = width - (width % divisible_by)
-            height = height - (height % divisible_by)
-
-        if per_batch and B > per_batch:
-            try:
-                bytes_per_elem = image.element_size()
-                est_total_bytes = B * height * width * C * bytes_per_elem
-                est_mb = est_total_bytes / (1024 * 1024)
-                logging.info(
-                    f"ℹ️\u2009 Nilor-Nodes (NilorImageResizeV2) Estimated output ~{est_mb:.2f} MB."
-                )
-            except:
-                pass
-
-        def _process_subbatch(in_image, in_mask):
-            out_image = in_image if in_image.device == device else in_image.to(device)
-            out_mask = (
-                None
-                if in_mask is None
-                else (in_mask if in_mask.device == device else in_mask.to(device))
-            )
-
-            if keep_proportion == "crop":
-                old_height = out_image.shape[-3]
-                old_width = out_image.shape[-2]
-                old_aspect = old_width / old_height
-                new_aspect = width / height
-                if old_aspect > new_aspect:
-                    crop_w = round(old_height * new_aspect)
-                    crop_h = old_height
-                else:
-                    crop_w = old_width
-                    crop_h = round(old_width / new_aspect)
-                if crop_position == "center":
-                    x = (old_width - crop_w) // 2
-                    y = (old_height - crop_h) // 2
-                elif crop_position == "top":
-                    x = (old_width - crop_w) // 2
-                    y = 0
-                elif crop_position == "bottom":
-                    x = (old_width - crop_w) // 2
-                    y = old_height - crop_h
-                elif crop_position == "left":
-                    x = 0
-                    y = (old_height - crop_h) // 2
-                elif crop_position == "right":
-                    x = old_width - crop_w
-                    y = (old_height - crop_h) // 2
-                out_image = out_image.narrow(-2, x, crop_w).narrow(-3, y, crop_h)
-                if out_mask is not None:
-                    out_mask = out_mask.narrow(-1, x, crop_w).narrow(-2, y, crop_h)
-
-            out_image = common_upscale(
-                out_image.movedim(-1, 1), width, height, upscale_method, crop="disabled"
-            ).movedim(1, -1)
-            if out_mask is not None:
-                if upscale_method == "lanczos":
-                    out_mask = common_upscale(
-                        out_mask.unsqueeze(1).repeat(1, 3, 1, 1),
-                        width,
-                        height,
-                        upscale_method,
-                        crop="disabled",
-                    ).movedim(1, -1)[:, :, :, 0]
-                else:
-                    out_mask = common_upscale(
-                        out_mask.unsqueeze(1),
-                        width,
-                        height,
-                        upscale_method,
-                        crop="disabled",
-                    ).squeeze(1)
-
-            if (keep_proportion.startswith("pad") or pillarbox_blur) and (
-                pad_left > 0 or pad_right > 0 or pad_top > 0 or pad_bottom > 0
-            ):
-                padded_width = width + pad_left + pad_right
-                padded_height = height + pad_top + pad_bottom
-                if divisible_by > 1:
-                    width_remainder = padded_width % divisible_by
-                    height_remainder = padded_height % divisible_by
-                    if width_remainder > 0:
-                        extra_width = divisible_by - width_remainder
-                        pad_right += extra_width
-                    if height_remainder > 0:
-                        extra_height = divisible_by - height_remainder
-                        pad_bottom += extra_height
-
-                pad_mode = (
-                    "pillarbox_blur"
-                    if pillarbox_blur
-                    else (
-                        "edge"
-                        if keep_proportion == "pad_edge"
-                        else (
-                            "edge_pixel"
-                            if keep_proportion == "pad_edge_pixel"
-                            else "color"
-                        )
-                    )
-                )
-                out_image, out_mask = ImagePadKJ.pad(
-                    self,
-                    out_image,
-                    pad_left,
-                    pad_right,
-                    pad_top,
-                    pad_bottom,
-                    0,
-                    pad_color,
-                    pad_mode,
-                    mask=out_mask,
-                )
-
-            return out_image, out_mask
-
-        if per_batch is None or per_batch == 0 or B <= per_batch:
-            out_image, out_mask = _process_subbatch(image, mask)
-        else:
-            chunks = []
-            mask_chunks = [] if mask is not None else None
-            total_batches = (B + per_batch - 1) // per_batch
-            current_batch = 0
-            for start_idx in range(0, B, per_batch):
-                current_batch += 1
-                end_idx = min(start_idx + per_batch, B)
-                sub_img = image[start_idx:end_idx]
-                sub_mask = mask[start_idx:end_idx] if mask is not None else None
-                sub_out_img, sub_out_mask = _process_subbatch(sub_img, sub_mask)
-                chunks.append(sub_out_img.cpu())
-                if mask is not None:
-                    mask_chunks.append(
-                        sub_out_mask.cpu() if sub_out_mask is not None else None
-                    )
-                try:
-                    logging.info(
-                        f"ℹ️\u2009 Nilor-Nodes (NilorImageResizeV2) Batch {current_batch}/{total_batches} · images {end_idx}/{B}"
-                    )
-                except:
-                    pass
-            out_image = torch.cat(chunks, dim=0)
-            if mask is not None and any(m is not None for m in mask_chunks):
-                out_mask = torch.cat([m for m in mask_chunks if m is not None], dim=0)
-            else:
-                out_mask = None
-
-        logging.info(f"✅\u2009 Nilor-Nodes (NilorImageResizeV2) All batches complete.")
-
-        return (
-            out_image.cpu(),
-            out_image.shape[2],
-            out_image.shape[1],
-            (
-                out_mask.cpu()
-                if out_mask is not None
-                else torch.zeros(
-                    64, 64, device=torch.device("cpu"), dtype=torch.float32
-                )
-            ),
-        )
-
-
 # Mapping class names to objects for potential export
 NODE_CLASS_MAPPINGS = {
     "Nilor Interpolated Float List": NilorInterpolatedFloatList,
@@ -1766,22 +1204,19 @@ NODE_CLASS_MAPPINGS = {
     "Nilor n Fractions of Int": NilorNFractionsOfInt,
     "Nilor Categorize String": NilorCategorizeString,
     "Nilor Random String": NilorRandomString,
-    "Nilor Wan Tile Resolution": NilorWanTileResolution,
     "Nilor Extract Filename from Path": NilorExtractFilenameFromPath,
     "Nilor Load Image By Index": NilorLoadImageByIndex,
     "Nilor Blur Analysis": NilorBlurAnalysis,
     "Nilor To Sparse Index Method": NilorToSparseIndexMethod,
-    "Nilor Image Resize v2": NilorImageResizeV2,
-    "Nilor Wan Frame Trim": NilorWanFrameTrim,
 }
 
 # Mapping nodes to human-readable names
 NODE_DISPLAY_NAME_MAPPINGS = {
     "Nilor Interpolated Float List": "👺 Interpolated Float List",
     "Nilor One Minus Float List": "👺 One Minus Float List",
-    "Nilor Remap Float List": "👺 Remap Float List",
-    "Nilor Remap Float List Auto Input": "👺 Remap Float List Auto Input",
-    "Nilor Inverse Map Float List": "👺 Inverse Map Float List",
+    "Nilor Remap Float List": "👺 Nilor Remap Float List",
+    "Nilor Remap Float List Auto Input": "👺 Nilor Remap Float List Auto Input",
+    "Nilor Inverse Map Float List": "👺 Nilor Inverse Map Float List",
     "Nilor Int To List Of Bools": "👺 Int To List Of Bools",
     "Nilor List of Ints": "👺 List of Ints",
     "Nilor Count Images In Directory": "👺 Count Images In Directory",
@@ -1789,18 +1224,15 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "Nilor Save Video To HF Dataset": "👺 Save Video To HF Dataset",
     "Nilor Select Index From List": "👺 Select Index From List",
     "Nilor Save EXR Arbitrary": "👺 Save EXR Arbitrary",
-    "Nilor Shuffle Image Batch": "👺 Shuffle Image Batch",
-    "Nilor Repeat & Trim Image Batch": "👺 Repeat & Trim Image Batch",
-    "Nilor Repeat, Shuffle, & Trim Image Batch": "👺 Repeat, Shuffle, & Trim Image Batch",
-    "Nilor Output Filename String": "👺 Output Filename String",
-    "Nilor n Fractions of Int": "👺 n Fractions of Int",
+    "Nilor Shuffle Image Batch": "👺 Nilor Shuffle Image Batch",
+    "Nilor Repeat & Trim Image Batch": "👺 Nilor Repeat & Trim Image Batch",
+    "Nilor Repeat, Shuffle, & Trim Image Batch": "👺 Nilor Repeat, Shuffle, & Trim Image Batch",
+    "Nilor Output Filename String": "👺 Nilor Output Filename String",
+    "Nilor n Fractions of Int": "👺 Nilor n Fractions of Int",
     "Nilor Categorize String": "👺 Categorize String",
     "Nilor Random String": "👺 Random String",
-    "Nilor Wan Tile Resolution": "👺 Wan Tile Resolution",
     "Nilor Extract Filename from Path": "👺 Extract Filename from Path",
     "Nilor Load Image By Index": "👺 Load Image By Index",
     "Nilor Blur Analysis": "👺 Blur Analysis",
     "Nilor To Sparse Index Method": "👺 To Sparse Index Method",
-    "Nilor Image Resize v2": "👺 Resize Image v2",
-    "Nilor Wan Frame Trim": "👺 Wan Frame Trim",
 }

requirements.txt

@@ -1,19 +1,2 @@
-
-aiobotocore==2.24.2
-aiofiles>=23.2.1
-aiohttp==3.12.14
-boto3==1.40.15
-fastapi==0.110.0
-huggingface_hub==0.34.0
-imageio==2.37.0
-imageio-ffmpeg==0.6.0
-numpy>=1.26.4
-opencv-python>=4.6.0.66
-openexr==3.3.4
-Pillow==10.4.0
-python-dotenv==1.0.1
-python-multipart==0.0.9
-requests==2.31.0
-uvicorn==0.27.1
-websockets==11.0.3
---prefer-binary
+huggingface_hub
+openexr

test_brain_api_client.py

@@ -0,0 +1,151 @@
+#!/usr/bin/env python3
+"""
+Test script for Brain API Client
+
+This script tests the Brain API client functionality to ensure it can
+communicate with the Brain API storage endpoints correctly.
+"""
+
+import os
+import sys
+import tempfile
+import logging
+from pathlib import Path
+
+# Add the current directory to the Python path
+current_dir = Path(__file__).parent
+sys.path.insert(0, str(current_dir))
+
+from brain_api_client import get_brain_api_client
+
+# Setup logging
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
+
+def test_brain_api_client():
+    """Test the Brain API client functionality."""
+    print("🧪 Testing Brain API Client...")
+    
+    try:
+        # Initialize the client
+        client = get_brain_api_client()
+        print("✅ Brain API client initialized successfully")
+        
+        # Test health check
+        print("🔍 Testing health check...")
+        is_healthy = client.health_check()
+        if is_healthy:
+            print("✅ Brain API is accessible")
+        else:
+            print("⚠️  Brain API health check failed - this might be expected if the API is not running")
+        
+        # Test file upload
+        print("📤 Testing file upload...")
+        test_content = b"Hello, Brain API! This is a test file."
+        test_filename = "test_file.txt"
+        
+        # Create a temporary file
+        with tempfile.NamedTemporaryFile(mode='wb', delete=False, suffix='.txt') as temp_file:
+            temp_file.write(test_content)
+            temp_file_path = temp_file.name
+        
+        try:
+            # Upload the file
+            upload_result = client.upload_file_to_storage(temp_file_path, test_filename)
+            print(f"✅ File uploaded successfully. Storage ID: {upload_result.get('storage_id')}")
+            
+            storage_id = upload_result.get('storage_id')
+            if storage_id:
+                # Test file download
+                print("📥 Testing file download...")
+                downloaded_content = client.get_file_from_storage(storage_id, test_filename)
+                
+                if downloaded_content == test_content:
+                    print("✅ File downloaded successfully and content matches")
+                else:
+                    print("❌ Downloaded content does not match original")
+                
+                # Test file deletion
+                print("🗑️  Testing file deletion...")
+                client.delete_file_from_storage(storage_id, test_filename)
+                print("✅ File deleted successfully")
+            
+        finally:
+            # Clean up temporary file
+            os.unlink(temp_file_path)
+        
+        print("🎉 All tests passed!")
+        return True
+        
+    except Exception as e:
+        print(f"❌ Test failed: {e}")
+        logging.exception("Test failed with exception:")
+        return False
+
+def test_fileobj_upload():
+    """Test uploading a file-like object."""
+    print("\n🧪 Testing file object upload...")
+    
+    try:
+        client = get_brain_api_client()
+        
+        # Create a file-like object
+        import io
+        test_content = b"Hello from file object!"
+        file_obj = io.BytesIO(test_content)
+        
+        # Upload the file object
+        upload_result = client.upload_fileobj_to_storage(file_obj, "test_fileobj.txt", "text/plain")
+        print(f"✅ File object uploaded successfully. Storage ID: {upload_result.get('storage_id')}")
+        
+        storage_id = upload_result.get('storage_id')
+        if storage_id:
+            # Test download
+            downloaded_content = client.get_file_from_storage(storage_id, "test_fileobj.txt")
+            
+            if downloaded_content == test_content:
+                print("✅ File object download successful and content matches")
+            else:
+                print("❌ Downloaded content does not match original")
+            
+            # Clean up
+            client.delete_file_from_storage(storage_id, "test_fileobj.txt")
+            print("✅ File object deleted successfully")
+        
+        return True
+        
+    except Exception as e:
+        print(f"❌ File object test failed: {e}")
+        logging.exception("File object test failed with exception:")
+        return False
+
+if __name__ == "__main__":
+    print("🚀 Starting Brain API Client Tests")
+    print("=" * 50)
+    
+    # Check environment variables
+    api_key = os.getenv("BRANDO_API_KEY")
+    base_url = os.getenv("BRANDO_BRAIN_API_BASE_URL", "http://localhost:2024/api")
+    
+    print(f"API Key: {'✅ Set' if api_key else '❌ Not set'}")
+    print(f"Base URL: {base_url}")
+    print()
+    
+    if not api_key:
+        print("❌ BRANDO_API_KEY environment variable is not set!")
+        print("Please set it in your .env file or environment.")
+        sys.exit(1)
+    
+    # Run tests
+    success = True
+    success &= test_brain_api_client()
+    success &= test_fileobj_upload()
+    
+    print("\n" + "=" * 50)
+    if success:
+        print("🎉 All tests completed successfully!")
+        sys.exit(0)
+    else:
+        print("❌ Some tests failed!")
+        sys.exit(1)

utils.py

@@ -15,9 +15,8 @@ def numpy2pil(image: np.ndarray, mode=None):
 
 
 ## Helper function equivalent to Mikey's pil2tensor
-# def pil2tensor(self, image):
-#    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
-
+    #def pil2tensor(self, image):
+    #    return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0)
 
 def pil2tensor(image: Image.Image):
     return torch.from_numpy(pil2numpy(image)).unsqueeze(0)