Upload 3 files

README.md

@@ -1,35 +1,31 @@
----
-title: WD EVA02 LoRA ONNX Tagger
-emoji: 🖼️
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 4.43.0 # Updated Gradio SDK version
-app_file: app.py
-license: apache-2.0 # Or your preferred license
-# Hardware Selection:
-# For CPU execution (recommended if GPU isn't strictly needed):
-# hardware: cpu-upgrade 
-# For GPU execution (requires compatible CUDA setup):
-# hardware: cuda-t4-small 
-pinned: false # Set to true if you want to pin the hardware
-# hf_token: YOUR_HF_TOKEN # Use secrets instead!
----
-
-# WD EVA02 LoRA ONNX Tagger
-
-This Space demonstrates image tagging using a fine-tuned WD EVA02 model (converted to ONNX format).
-
-Model Repository: [celstk/wd-eva02-lora-onnx](https://huggingface.co/celstk/wd-eva02-lora-onnx)
-
-**How to Use:**
-1. Upload an image using the upload button.
-2. Alternatively, paste an image URL into the browser (experimental paste handling).
-3. Adjust the tag thresholds if needed.
-4. Choose the output mode (Tags only or include visualization).
-5. Click the "Predict" button.
-
-**Note:**
-- This Space uses a model from a **private** repository (`celstk/wd-eva02-lora-onnx`). You might need to duplicate this space and add your Hugging Face token (`HF_TOKEN`) to the Space secrets to allow downloading the model files.
-- Image pasting behavior might vary across browsers.
-- If you require GPU acceleration, uncomment the `hardware: cuda-t4-small` line above and ensure the environment has the necessary CUDA libraries compatible with `onnxruntime-gpu`. The current setup defaults to CPU due to potential CUDA library mismatches in the standard Spaces environment. 
+---
+title: WD EVA02 LoRA ONNX Tagger
+emoji: 🖼️
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 4.43.0 # requirements.txt と合わせるか確認
+app_file: app.py
+license: apache-2.0 # または適切なライセンス
+# Pinned Hardware: T4 small (GPU) or CPU upgrade (CPU)
+# pinned: false # 必要に応じてTrueに
+# hardware: cpu-upgrade # or cuda-t4-small
+# hf_token: YOUR_HF_TOKEN # Use secrets instead!
+---
+
+# WD EVA02 LoRA ONNX Tagger
+
+This Space demonstrates image tagging using a fine-tuned WD EVA02 model (converted to ONNX format).
+
+Model Repository: [celstk/wd-eva02-lora-onnx](https://huggingface.co/celstk/wd-eva02-lora-onnx)
+
+**How to Use:**
+1. Upload an image using the upload button.
+2. Alternatively, paste an image URL into the browser (experimental paste handling).
+3. Adjust the tag thresholds if needed.
+4. Choose the output mode (Tags only or include visualization).
+5. Click the "Predict" button.
+
+**Note:**
+- This Space uses a model from a **private** repository (`celstk/wd-eva02-lora-onnx`). You might need to duplicate this space and add your Hugging Face token (`HF_TOKEN`) to the Space secrets to allow downloading the model files.
+- Image pasting behavior might vary across browsers. 

app.py

@@ -1,5 +1,5 @@
 import gradio as gr
-import spaces
+# import onnxruntime as ort # Removed
 import numpy as np
 from PIL import Image, ImageDraw, ImageFont
 import json
@@ -12,6 +12,7 @@ from huggingface_hub import hf_hub_download
 from dataclasses import dataclass
 from typing import List, Dict, Optional, Tuple
 import time
+# import spaces # Keep for @spaces.GPU
 
 import torch
 import timm
@@ -347,9 +348,11 @@ def initialize_labels_and_paths():
              print(f"Tag mapping file not found after download attempt: {tag_mapping_path_global}")
              raise gr.Error("Tag mapping file could not be downloaded or found.")
 
-@spaces.GPU()
+# --- Prediction Function (PyTorch based) ---
+# @spaces.GPU() # Removed decorator
 def predict(image_input, gen_threshold, char_threshold, output_mode):
     print("--- predict function started (GPU worker) ---")
+    """Gradioインターフェース用の予測関数 (PyTorch GPUワーカー内)"""
     initialize_labels_and_paths()
     print("Loading PyTorch model...")
     global safetensors_path_global, labels_data

requirements.txt

@@ -6,7 +6,7 @@ torchaudio
 safetensors
 transformers
 timm # Needed for EVA02 base model
-numpy # Keep numpy, let pip resolve version
+numpy # Let pip resolve NumPy version
 Pillow
 matplotlib
 requests