Fix model download logic and Add it parsing class

app.py

@@ -537,7 +537,7 @@ with image_blocks as demo:
         api_name='tryon'
     )
 
-# 앱 시작 시 DensePose 모델 미리 다운로드
+# DensePose 모델 다운로드
 print("Initializing DensePose model...")
 try:
     download_all_models()

preprocess/humanparsing/run_parsing.py

@@ -3,23 +3,73 @@ from pathlib import Path
 import sys
 import os
 import onnxruntime as ort
+import requests
 PROJECT_ROOT = Path(__file__).absolute().parents[0].absolute()
 sys.path.insert(0, str(PROJECT_ROOT))
 from parsing_api import onnx_inference
 import torch
 
 
+def download_model_file(model_path, url):
+    """Download model file if it doesn't exist"""
+    if os.path.exists(model_path):
+        print(f"Model file already exists: {model_path}")
+        return True
+    
+    os.makedirs(os.path.dirname(model_path), exist_ok=True)
+    
+    try:
+        print(f"Downloading {model_path} from {url}")
+        response = requests.get(url, stream=True)
+        response.raise_for_status()
+        
+        total_size = int(response.headers.get('content-length', 0))
+        block_size = 8192
+        
+        with open(model_path, 'wb') as f:
+            downloaded = 0
+            for chunk in response.iter_content(chunk_size=block_size):
+                if chunk:
+                    f.write(chunk)
+                    downloaded += len(chunk)
+                    if total_size > 0:
+                        percent = (downloaded / total_size) * 100
+                        print(f"\rDownload progress: {percent:.1f}%", end='', flush=True)
+        
+        print(f"\nSuccessfully downloaded: {model_path}")
+        return True
+        
+    except Exception as e:
+        print(f"Failed to download {model_path}: {e}")
+        return False
+
+
 class Parsing:
     def __init__(self, gpu_id: int):
         # self.gpu_id = gpu_id
         # torch.cuda.set_device(gpu_id)
+        
+        # Model paths
+        base_path = Path(__file__).absolute().parents[2].absolute()
+        parsing_atr_path = os.path.join(base_path, 'ckpt/humanparsing/parsing_atr.onnx')
+        parsing_lip_path = os.path.join(base_path, 'ckpt/humanparsing/parsing_lip.onnx')
+        
+        # Download models if they don't exist
+        base_url = "https://huggingface.co/Longcat2957/humanparsing-onnx/resolve/main"
+        
+        if not os.path.exists(parsing_atr_path):
+            download_model_file(parsing_atr_path, f"{base_url}/parsing_atr.onnx")
+        
+        if not os.path.exists(parsing_lip_path):
+            download_model_file(parsing_lip_path, f"{base_url}/parsing_lip.onnx")
+        
         session_options = ort.SessionOptions()
         session_options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
         session_options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
         # session_options.add_session_config_entry('gpu_id', str(gpu_id))
-        self.session = ort.InferenceSession(os.path.join(Path(__file__).absolute().parents[2].absolute(), 'ckpt/humanparsing/parsing_atr.onnx'),
+        self.session = ort.InferenceSession(parsing_atr_path,
                                             sess_options=session_options, providers=['CPUExecutionProvider'])
-        self.lip_session = ort.InferenceSession(os.path.join(Path(__file__).absolute().parents[2].absolute(), 'ckpt/humanparsing/parsing_lip.onnx'),
+        self.lip_session = ort.InferenceSession(parsing_lip_path,
                                                 sess_options=session_options, providers=['CPUExecutionProvider'])