Create app.py

app.py

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+import os
+import cv2
+import torch
+import glob
+import requests
+import numpy as np
+import gradio as gr
+from basicsr.archs.rrdbnet_arch import RRDBNet
+from realesrgan import RealESRGANer
+
+# ==========================================
+# 1. AUTO-PATCHER (Bedah Kode Otomatis)
+# ==========================================
+# Mengatasi error 'torchvision' yang sering terjadi di server baru
+def patch_basicsr():
+    try:
+        import basicsr
+        pkg_path = os.path.dirname(basicsr.__file__)
+        target_file = os.path.join(pkg_path, 'data', 'degradations.py')
+        
+        if os.path.exists(target_file):
+            with open(target_file, 'r') as f: data = f.read()
+            
+            old_str = 'from torchvision.transforms.functional_tensor import rgb_to_grayscale'
+            new_str = 'from torchvision.transforms.functional import rgb_to_grayscale'
+            
+            if old_str in data:
+                data = data.replace(old_str, new_str)
+                with open(target_file, 'w') as f: f.write(data)
+                print("✅ Auto-Patch: Library basicsr berhasil diperbaiki.")
+    except Exception as e:
+        print(f"⚠️ Auto-Patch Warning: {e}")
+
+patch_basicsr()
+
+# ==========================================
+# 2. PERSIAPAN MODEL
+# ==========================================
+# Cek ketersediaan GPU (Hugging Face Free Tier biasanya CPU, Paid dapat GPU)
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+print(f"🚀 Running on: {device}")
+
+# Download Model Pre-trained jika belum ada
+model_url = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth'
+model_path = 'RealESRGAN_x4plus.pth'
+
+if not os.path.exists(model_path):
+    print("⏳ Mendownload model AI...")
+    response = requests.get(model_url)
+    with open(model_path, 'wb') as f:
+        f.write(response.content)
+    print("✅ Model siap!")
+
+# Load Model ke Memory
+def load_model():
+    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
+    netscale = 4
+    
+    upsampler = RealESRGANer(
+        scale=netscale,
+        model_path=model_path,
+        dni_weight=None,
+        model=model,
+        tile=200,       # Tile kecil agar aman untuk CPU/RAM kecil
+        tile_pad=10,
+        pre_pad=0,
+        half=False,     # False agar aman di CPU (fp32)
+        device=device
+    )
+    return upsampler
+
+# Inisialisasi Upsampler Global
+upsampler = load_model()
+
+# ==========================================
+# 3. LOGIKA UTAMA (PIPELINE)
+# ==========================================
+def process_image(img, scale, face_enhance):
+    if img is None:
+        return None
+
+    # --- MODE DOWNSCALE (MENGECILKAN) ---
+    if scale < 1.0:
+        h, w = img.shape[:2]
+        new_w, new_h = int(w * scale), int(h * scale)
+        # Gunakan INTER_AREA untuk hasil pengecilan terbaik (tajam)
+        output = cv2.resize(img, (new_w, new_h), interpolation=cv2.INTER_AREA)
+        return output
+
+    # --- MODE UPSCALE (MEMBESARKAN) ---
+    else:
+        try:
+            # Upscale menggunakan Real-ESRGAN
+            # outscale=scale akan otomatis resize hasil akhir sesuai keinginan user
+            output, _ = upsampler.enhance(img, outscale=scale)
+            
+            # Fitur Perbaikan Wajah (Optional)
+            if face_enhance:
+                from gfpgan import GFPGANer
+                face_enhancer = GFPGANer(
+                    model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth',
+                    upscale=scale,
+                    arch='clean',
+                    channel_multiplier=2,
+                    bg_upsampler=upsampler
+                )
+                _, _, output = face_enhancer.enhance(output, has_aligned=False, only_center_face=False, paste_back=True)
+            
+            return output
+            
+        except RuntimeError as e:
+            # Tangani error jika RAM tidak cukup
+            if 'out of memory' in str(e):
+                return None
+            raise e
+
+# ==========================================
+# 4. ANTARMUKA GRADIO
+# ==========================================
+title = "Smart Image Resizer & Upscaler"
+description = """
+**Fitur Utama:**
+1. **Upscale Cerdas:** Menggunakan Real-ESRGAN untuk memperbesar gambar tanpa pecah.
+2. **Downscale Tajam:** Menggunakan algoritma resampling area untuk memperkecil gambar dengan detail tinggi.
+3. **Face Restore:** Memperbaiki wajah yang buram (opsional).
+
+*Catatan: Jika berjalan di CPU (HF Free Tier), proses upscale gambar besar mungkin memakan waktu 30-60 detik.*
+"""
+
+iface = gr.Interface(
+    fn=process_image,
+    inputs=[
+        gr.Image(label="Upload Gambar", type="numpy"),
+        gr.Slider(0.1, 4.0, value=2.0, step=0.1, label="Skala Resolusi (0.5 = Kecil, 2.0 = Besar)"),
+        gr.Checkbox(label="Perbaiki Wajah (Face Restoration)", value=False)
+    ],
+    outputs=gr.Image(label="Hasil Proses"),
+    title=title,
+    description=description,
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    iface.launch()