Update app.py

app.py

@@ -76,141 +76,154 @@ def init_face_parser():
     if FACE_PARSING_AVAILABLE:
         return True
     
-    print("[FaceParsing] FORCING jonathandinu/face-parsing load...")
+    print("[FaceParsing] 🚀 FINAL: Loading complete model...")
     
-    # روش ۱: استفاده از Hugging Face CLI برای دانلود
     try:
-        print("[FaceParsing] Method 1: Using huggingface_hub directly...")
-        from huggingface_hub import snapshot_download
-        
-        # دانلود تمام فایل‌های مدل
-        local_path = snapshot_download(
-            repo_id="jonathandinu/face-parsing",
-            repo_type="model",
-            local_dir="./face_parsing_model",
-            local_dir_use_syms=False,
-            ignore_patterns=["*.md", "*.txt", "*.gitattributes"]
-        )
-        
-        print(f"[FaceParsing] Model downloaded to: {local_path}")
-        
-        # حالا از پوشه محلی لود کن
-        from transformers import AutoModelForImageSegmentation, AutoImageProcessor
+        import os
+        import json
+        import shutil
         
-        model = AutoModelForImageSegmentation.from_pretrained(local_path)
-        processor = AutoImageProcessor.from_pretrained(local_path)
+        # ۱. ایجاد پوشه برای مدل کامل
+        complete_dir = "./complete_face_parsing"
+        os.makedirs(complete_dir, exist_ok=True)
         
-        FACE_PARSER = {
-            'model': model,
-            'processor': processor,
-            'path': local_path,
-            'source': 'direct_download'
-        }
+        print("[FaceParsing] Step 1: Setting up model directory...")
         
-        FACE_PARSING_AVAILABLE = True
-        print("[FaceParsing] ✓ Model loaded from direct download!")
-        return True
+        # ۲. کپی weights اگر وجود دارد
+        weights_sources = [
+            "./manual_model/pytorch_model.bin",
+            "./manual_model/model.safetensors",
+            "./pytorch_model.bin"
+        ]
         
-    except Exception as e:
-        print(f"[FaceParsing] Method 1 failed: {str(e)[:200]}")
-    
-    # روش ۲: ساخت config دستی
-    try:
-        print("[FaceParsing] Method 2: Creating manual config...")
+        weights_copied = False
+        for source in weights_sources:
+            if os.path.exists(source):
+                shutil.copy(source, os.path.join(complete_dir, os.path.basename(source)))
+                print(f"[FaceParsing] ✓ Copied weights from {source}")
+                weights_copied = True
+                break
         
-        # ایجاد config.json دستی
-        import json
-        import os
+        if not weights_copied:
+            print("[FaceParsing] ⚠ No weights file found, downloading...")
+            # دانلود weights اگر نیست
+            from huggingface_hub import hf_hub_download
+            hf_hub_download(
+                repo_id="jonathandinu/face-parsing",
+                filename="pytorch_model.bin",
+                local_dir=complete_dir
+            )
         
-        config = {
+        # ۳. ذخیره config.json (همین که دارید)
+        config_data = {
             "_name_or_path": "jonathandinu/face-parsing",
             "architectures": ["SegformerForSemanticSegmentation"],
+            "attention_probs_dropout_prob": 0.0,
+            "classifier_dropout_prob": 0.1,
+            "decoder_hidden_size": 768,
+            "depths": [3, 6, 40, 3],
+            "downsampling_rates": [1, 4, 8, 16],
+            "drop_path_rate": 0.1,
+            "hidden_act": "gelu",
+            "hidden_dropout_prob": 0.0,
+            "hidden_sizes": [64, 128, 320, 512],
+            "id2label": {
+                "0": "background", "1": "skin", "2": "nose", "3": "eye_g",
+                "4": "l_eye", "5": "r_eye", "6": "l_brow", "7": "r_brow",
+                "8": "l_ear", "9": "r_ear", "10": "mouth", "11": "u_lip",
+                "12": "l_lip", "13": "hair", "14": "hat", "15": "ear_r",
+                "16": "neck_l", "17": "neck", "18": "cloth"
+            },
+            "image_size": 224,
+            "initializer_range": 0.02,
+            "label2id": {
+                "background": 0, "skin": 1, "nose": 2, "eye_g": 3,
+                "l_eye": 4, "r_eye": 5, "l_brow": 6, "r_brow": 7,
+                "l_ear": 8, "r_ear": 9, "mouth": 10, "u_lip": 11,
+                "l_lip": 12, "hair": 13, "hat": 14, "ear_r": 15,
+                "neck_l": 16, "neck": 17, "cloth": 18
+            },
+            "layer_norm_eps": 1e-06,
+            "mlp_ratios": [4, 4, 4, 4],
             "model_type": "segformer",
-            "num_labels": 19,  # برای face parsing معمولاً 19 کلاس
-            "image_size": 512,
-            "hidden_sizes": [32, 64, 160, 256],
             "num_attention_heads": [1, 2, 5, 8],
-            "depths": [2, 2, 2, 2]
+            "num_channels": 3,
+            "num_encoder_blocks": 4,
+            "patch_sizes": [7, 3, 3, 3],
+            "reshape_last_stage": True,
+            "semantic_loss_ignore_index": 255,
+            "sr_ratios": [8, 4, 2, 1],
+            "strides": [4, 2, 2, 2],
+            "transformers_version": "4.37.0.dev0"
         }
         
-        # ذخیره config
-        os.makedirs("./manual_model", exist_ok=True)
-        with open("./manual_model/config.json", "w") as f:
-            json.dump(config, f)
-        
-        # دانلود weights
-        print("[FaceParsing] Downloading weights...")
-        from huggingface_hub import hf_hub_download
+        with open(os.path.join(complete_dir, "config.json"), "w") as f:
+            json.dump(config_data, f, indent=2)
+        print("[FaceParsing] ✓ Saved config.json")
+        
+        # ۴. ذخیره preprocessor_config.json
+        preprocessor_config = {
+            "do_normalize": True,
+            "do_reduce_labels": False,
+            "do_rescale": True,
+            "do_resize": True,
+            "image_mean": [0.485, 0.456, 0.406],
+            "image_processor_type": "SegformerFeatureExtractor",
+            "image_std": [0.229, 0.224, 0.225],
+            "resample": 2,
+            "rescale_factor": 0.00392156862745098,
+            "size": {"height": 512, "width": 512}
+        }
         
-        # سعی کن pytorch_model.bin را دانلود کنی
-        try:
-            weights_path = hf_hub_download(
-                repo_id="jonathandinu/face-parsing",
-                filename="pytorch_model.bin",
-                local_dir="./manual_model"
-            )
-        except:
-            # اگر safetensors باشد
-            weights_path = hf_hub_download(
-                repo_id="jonathandinu/face-parsing", 
-                filename="model.safetensors",
-                local_dir="./manual_model"
-            )
+        with open(os.path.join(complete_dir, "preprocessor_config.json"), "w") as f:
+            json.dump(preprocessor_config, f, indent=2)
+        print("[FaceParsing] ✓ Saved preprocessor_config.json")
         
-        # لود مدل
+        # ۵. لود مدل
+        print("[FaceParsing] Step 2: Loading model...")
         from transformers import SegformerForSemanticSegmentation
-        model = SegformerForSemanticSegmentation.from_pretrained("./manual_model")
-        
-        # processor
         from transformers import SegformerImageProcessor
-        processor = SegformerImageProcessor()
         
+        # لود model
+        model = SegformerForSemanticSegmentation.from_pretrained(
+            complete_dir,
+            local_files_only=True,
+            ignore_mismatched_sizes=False
+        )
+        
+        # لود processor
+        processor = SegformerImageProcessor.from_pretrained(complete_dir)
+        
+        # ۶. ذخیره
         FACE_PARSER = {
             'model': model,
             'processor': processor,
-            'path': "./manual_model",
-            'source': 'manual_config'
+            'path': complete_dir,
+            'num_labels': 19,
+            'id2label': config_data['id2label'],
+            'status': 'complete'
         }
         
         FACE_PARSING_AVAILABLE = True
-        print("[FaceParsing] ✓ Model loaded with manual config!")
-        return True
         
-    except Exception as e:
-        print(f"[FaceParsing] Method 2 failed: {str(e)[:200]}")
-    
-    # روش ۳: Contact نویسنده مدل
-    print("[FaceParsing] Method 3: The model might be broken...")
-    print("[FaceParsing] Contacting model author: jonathandinu")
-    print("[FaceParsing] Model URL: https://huggingface.co/jonathandinu/face-parsing")
-    
-    # روش ۴: استفاده از MediaPipe (همان کاری که الان می‌کند)
-    print("[FaceParsing] Method 4: Using MediaPipe instead...")
-    try:
-        import mediapipe as mp
+        print("[FaceParsing] ✅ SUCCESS: Model fully loaded!")
+        print(f"[FaceParsing]   Architecture: Segformer")
+        print(f"[FaceParsing]   Num labels: 19")
+        print(f"[FaceParsing]   Image size: 224")
+        print(f"[FaceParsing]   Classes: {list(config_data['id2label'].values())}")
         
-        mp_face_mesh = mp.solutions.face_mesh
-        face_mesh = mp_face_mesh.FaceMesh(
-            static_image_mode=True,
-            max_num_faces=1,
-            refine_landmarks=True,
-            min_detection_confidence=0.5
-        )
-        
-        FACE_PARSER = {
-            'model': face_mesh,
-            'processor': None,
-            'type': 'mediapipe',
-            'original_model': 'jonathandinu/face-parsing (unavailable)'
-        }
-        
-        FACE_PARSING_AVAILABLE = True
-        print("[FaceParsing] ✓ Using MediaPipe (fallback for unavailable model)")
         return True
         
     except Exception as e:
-        print(f"[FaceParsing] MediaPipe failed: {e}")
-        return False
+        print(f"[FaceParsing] ❌ FINAL load failed: {e}")
+        
+        # نمایش traceback کامل
+        import traceback
+        print(f"[FaceParsing] Traceback:\n{traceback.format_exc()[:500]}")
+        
+        # Fallback
+        print("[FaceParsing] Falling back to MediaPipe...")
+        return setup_mediapipe_fallback()
 
 def try_load_model(model_name, model_type):
     """سعی کن یک مدل را لود کنی"""