Update app.py

app.py

@@ -3,32 +3,24 @@
 🔥 Icon Challenge Solver - YOLO Crop + Denoise Display
 """
 
-# ===================== ASYNCIO WARNING FIX =====================
-import asyncio
-
-def _safe_event_loop_del(self):
-    try:
-        if not self.is_closed():
-            self.close()
-    except (ValueError, OSError, RuntimeError):
-        pass
-    except Exception:
-        pass
-
-asyncio.BaseEventLoop.__del__ = _safe_event_loop_del
+# ===================== ASYNCIO / UVLOOP FIX =====================
+import os
+os.environ["GRADIO_ANALYTICS_ENABLED"] = "0"
+os.environ["PYTHONASYNCIODEBUG"] = "0"
+os.environ["YOLO_CONFIG_DIR"] = "/tmp/Ultralytics"
+# Fix uvloop conflict dengan spaces
+os.environ["UVLOOP"] = "0"
 
+import asyncio
 import warnings
-warnings.filterwarnings("ignore", category=ResourceWarning)
-warnings.filterwarnings("ignore", message="Exception ignored in.*BaseEventLoop.*")
 warnings.filterwarnings("ignore")
 
 # ===================== IMPORTS =====================
-import os
 import gc
 import json
 import logging
 from pathlib import Path
-from typing import List
+from typing import List, Tuple
 
 import torch
 import torch.nn as nn
@@ -36,7 +28,7 @@ import torch.nn.functional as F
 import torchvision.transforms as T
 
 import numpy as np
-from PIL import Image, ImageFilter
+from PIL import Image
 import cv2
 
 import gradio as gr
@@ -46,10 +38,6 @@ from ultralytics import YOLO
 import open_clip
 from peft import PeftModel
 
-os.environ["PYTHONASYNCIODEBUG"] = "0"
-os.environ["GRADIO_ANALYTICS_ENABLED"] = "0"
-os.environ["YOLO_CONFIG_DIR"] = "/tmp/Ultralytics"
-
 logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
 logger = logging.getLogger(__name__)
 
@@ -69,7 +57,7 @@ class Config:
 
 CONFIG = Config()
 
-# ===================== PREPROCESS (EXACT MATCH) =====================
+# ===================== PREPROCESS (EXACT MATCH REFERENSI) =====================
 preprocess = T.Compose([
     T.Resize((256, 256), interpolation=T.InterpolationMode.NEAREST),
     T.ToTensor(),
@@ -129,7 +117,7 @@ def denoise_and_sharpen(crop_np: np.ndarray) -> np.ndarray:
     # 1. Non-local Means Denoising (hilangin pasir/noise)
     denoised = cv2.fastNlMeansDenoisingColored(crop_np, None, 10, 10, 7, 21)
 
-    # 2. Bilateral filter (edge-preserving smoothing, hapus sisa noise)
+    # 2. Bilateral filter (edge-preserving smoothing)
     smooth = cv2.bilateralFilter(denoised, d=5, sigmaColor=50, sigmaSpace=50)
 
     # 3. Unsharp Mask (sharpening)
@@ -137,7 +125,6 @@ def denoise_and_sharpen(crop_np: np.ndarray) -> np.ndarray:
     sharpened = cv2.addWeighted(smooth, 1.5, gaussian, -0.5, 0)
 
     # 4. CLAHE (Contrast Limited Adaptive Histogram Equalization)
-    # Convert ke LAB, apply CLAHE di channel L, balikin ke RGB
     lab = cv2.cvtColor(sharpened, cv2.COLOR_RGB2LAB)
     l, a, b = cv2.split(lab)
     clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
@@ -217,7 +204,7 @@ class ModelManager:
         logger.info("✅ All models ready!")
 
     # --- YOLO: Detect + Crop + Denoise ---
-    def yolo_detect_and_crop(self, image: Image.Image) -> tuple[Image.Image, Image.Image]:
+    def yolo_detect_and_crop(self, image: Image.Image) -> Tuple[Image.Image, Image.Image]:
         """
         Returns: (raw_crop, denoised_crop)
         """
@@ -302,7 +289,7 @@ def solve_gradio(challenge_img, *options):
             manager.init()
         except Exception as e:
             logger.error(f"Init failed: {e}", exc_info=True)
-            return f"❌ Init failed: {e}", None, None
+            return "❌ Init failed: " + str(e), None, None
 
     try:
         if challenge_img is None or any(o is None for o in options):
@@ -358,7 +345,7 @@ def solve_gradio(challenge_img, *options):
 
     except Exception as e:
         logger.error(str(e), exc_info=True)
-        return f"❌ Error: {str(e)}", None, None
+        return "❌ Error: " + str(e), None, None
 
 
 # ===================== GRADIO UI =====================