Update app.py

app.py

@@ -6,30 +6,22 @@ import gradio as gr
 from huggingface_hub import hf_hub_download
 
 REPO_ID = "A123123/AnimeAutoCensor" 
-
 HF_TOKEN = os.getenv("HF_TOKEN")
 
-try:
-
-    model_path = hf_hub_download(
-        repo_id=REPO_ID, 
-        filename="model.onnx", 
-        token=HF_TOKEN
-    )
+target_size = 640  
+session = None
+input_name = None
 
-    hf_hub_download(
-        repo_id=REPO_ID, 
-        filename="model.onnx_data", 
-        token=HF_TOKEN
-    )
+try:
+    print("Downloading model files...")
+    model_path = hf_hub_download(repo_id=REPO_ID, filename="model.onnx", token=HF_TOKEN)
+    hf_hub_download(repo_id=REPO_ID, filename="model.onnx_data", token=HF_TOKEN)
     
-    # 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  
+    print("Model loaded successfully!")
 except Exception as e:
-    print(f"Model loading failed. Please check Token and Repository Path: {e}")
-
+    print(f"CRITICAL ERROR: {e}")
 
 def apply_mosaic_mask(image_rgb, mask, mosaic_level=16):
     h, w = image_rgb.shape[:2]
@@ -41,8 +33,8 @@ def apply_mosaic_mask(image_rgb, mask, mosaic_level=16):
     return output_image
 
 def process_image(input_img):
-    if input_img is None:
-        return None
+    if input_img is None or session is None:
+        return input_img
     
     h_orig, w_orig = input_img.shape[:2]
     
@@ -71,7 +63,7 @@ def process_image(input_img):
 
 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.")
+    gr.Markdown("This tool uses AI to detect and censor content. The model is protected.")
     
     with gr.Row():
         with gr.Column():
@@ -81,9 +73,6 @@ with gr.Blocks(title="AI Anime Auto-Censor") as demo:
             output_i = gr.Image(type="numpy", label="Censored Result")
             
     run_btn.click(fn=process_image, inputs=input_i, outputs=output_i)
-    
-    gr.Markdown("---")
-    gr.Markdown("### Instructions:\n1. Upload an image.\n2. Click 'Start Processing'.\n3. Download the result if satisfied.")
 
 if __name__ == "__main__":
     demo.launch()