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README.md

@@ -1,29 +1,29 @@
----
-title: CL 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!
----
-
-# CL EVA02 LoRA ONNX Tagger
-
-This Space demonstrates image tagging using a fine-tuned WD EVA02 model (converted to ONNX format).
-
-**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.
+---
+title: CL 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).
+
+**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

@@ -199,14 +199,14 @@ def visualize_predictions(image: Image.Image, predictions: Dict, threshold: floa
 
     # --- Plotting Setup ---
     plt.rcParams['font.family'] = 'DejaVu Sans' # Ensure font compatibility
-    fig = plt.figure(figsize=(20, 12), dpi=100)
+    fig = plt.figure(figsize=(12, 20), dpi=100)
     gs = fig.add_gridspec(1, 2, width_ratios=[1.2, 1])
 
     # Left side: Image
-    ax_img = fig.add_subplot(gs[0, 0])
-    ax_img.imshow(image)
-    ax_img.set_title("Original Image")
-    ax_img.axis('off')
+    # ax_img = fig.add_subplot(gs[0, 0])
+    # ax_img.imshow(image)
+    # ax_img.set_title("Original Image")
+    # ax_img.axis('off')
 
     # Right side: Tags
     ax_tags = fig.add_subplot(gs[0, 1])
@@ -288,7 +288,7 @@ def visualize_predictions(image: Image.Image, predictions: Dict, threshold: floa
 REPO_ID = "cella110n/cl_tagger"
 # Use the specified ONNX model filename
 ONNX_FILENAME = "cl_eva02_tagger_v1_250426/model.onnx"
-# Keep the previously used tag mapping filename
+# Correct the tag mapping path to match the ONNX model's directory
 TAG_MAPPING_FILENAME = "cl_eva02_tagger_v1_250426/tag_mapping.json"
 CACHE_DIR = "./model_cache"
 
@@ -467,7 +467,7 @@ footer { display: none !important; }
 
 with gr.Blocks(css=css) as demo:
     gr.Markdown("# CL EVA02 ONNX Tagger")
-    gr.Markdown("Upload an image or paste an image URL to predict tags using the WD EVA02 Tagger model (ONNX), fine-tuned from [SmilingWolf/wd-eva02-large-tagger-v3](https://huggingface.co/SmilingWolf/wd-eva02-large-tagger-v3).")
+    gr.Markdown("Upload an image or paste an image URL to predict tags using the CL EVA02 Tagger model (ONNX), fine-tuned from [SmilingWolf/wd-eva02-large-tagger-v3](https://huggingface.co/SmilingWolf/wd-eva02-large-tagger-v3).")
     with gr.Row():
         with gr.Column(scale=1):
             image_input = gr.Image(type="pil", label="Input Image", elem_id="input-image")