Update app.py

app.py

@@ -5,20 +5,18 @@ import onnxruntime as ort
 import gradio as gr
 from huggingface_hub import hf_hub_download
 
-# --- 1. Configuration & Model Loading ---
-# Replace with your "username/repo-name"
 REPO_ID = "A123123/AnimeAutoCensor" 
-# Retrieved from Space's Secrets
+
 HF_TOKEN = os.getenv("HF_TOKEN")
 
 try:
-    # Download .onnx file
+
     model_path = hf_hub_download(
         repo_id=REPO_ID, 
         filename="model.onnx", 
         token=HF_TOKEN
     )
-    # Download .onnx_data file (ONNX Runtime automatically looks for this in the same directory)
+
     hf_hub_download(
         repo_id=REPO_ID, 
         filename="model.onnx_data", 
@@ -28,11 +26,11 @@ try:
     # Initialize Inference Engine (Hugging Face Free Tier supports CPU only)
     session = ort.InferenceSession(model_path, providers=['CPUExecutionProvider'])
     input_name = session.get_inputs()[0].name
-    target_size = 640  # Your model training size
+    target_size = 640  
 except Exception as e:
     print(f"Model loading failed. Please check Token and Repository Path: {e}")
 
-# --- 2. Core Processing Functions ---
+
 def apply_mosaic_mask(image_rgb, mask, mosaic_level=16):
     h, w = image_rgb.shape[:2]
     mask = (mask > 0).astype(np.uint8)
@@ -46,10 +44,8 @@ def process_image(input_img):
     if input_img is None:
         return None
     
-    # Gradio passes images as RGB numpy arrays
     h_orig, w_orig = input_img.shape[:2]
     
-    # A. Letterbox Preprocessing
     scale = target_size / max(h_orig, w_orig)
     new_h, new_w = int(h_orig * scale), int(w_orig * scale)
     img_resized = cv2.resize(input_img, (new_w, new_h))
@@ -59,7 +55,6 @@ def process_image(input_img):
     pad_x = (target_size - new_w) // 2
     canvas[pad_y:pad_y+new_h, pad_x:pad_x+new_w] = img_resized
 
-    # B. Normalization & Inference
     input_tensor = canvas.astype(np.float32) / 255.0
     input_tensor = (input_tensor - [0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225]
     input_tensor = input_tensor.transpose(2, 0, 1)[np.newaxis, ...].astype(np.float32)
@@ -67,16 +62,13 @@ def process_image(input_img):
     outputs = session.run(None, {input_name: input_tensor})
     pred = outputs[0][0][0]
 
-    # C. Post-processing (Restore Size)
     mask_valid = pred[pad_y:pad_y+new_h, pad_x:pad_x+new_w]
     mask_final = cv2.resize(mask_valid, (w_orig, h_orig))
     binary_mask = (mask_final > 0.5).astype(np.uint8)
     
-    # D. Apply Mosaic
     result_rgb = apply_mosaic_mask(input_img, binary_mask)
     return result_rgb
 
-# --- 3. Gradio Interface ---
 with gr.Blocks(title="AI Anime Auto-Censor") as demo:
     gr.Markdown("# 🎨 AI Anime Auto-Censor (Trial Version)")
     gr.Markdown("This tool uses AI to automatically detect and censor specific content. The model weights are protected and not accessible to the public.")