Fix: convert PIL to NumPy before ESRGAN inference

handler.py

@@ -3,6 +3,7 @@ import io
 import torch
 import base64
 import requests
+import numpy as np
 from PIL import Image
 from realesrgan import RealESRGANer
 from basicsr.archs.rrdbnet_arch import RRDBNet
@@ -85,35 +86,28 @@ class EndpointHandler:
     def preprocess(self, data):
         print(f"🔧 [PREPROCESS] Type received: {type(data)}")
 
-        # 1️⃣ Hugging Face JSON-wrapped dict {"inputs": ...}
         if isinstance(data, dict):
             print("🧩 [PREPROCESS] Detected dict input.")
             if "inputs" in data:
                 data = data["inputs"]
                 print(f"📨 [PREPROCESS] Found 'inputs' key: {type(data)}")
 
-        # 2️⃣ Direct PIL image object (the missing case!)
         if isinstance(data, Image.Image):
-            print("🖼️ [PREPROCESS] Got PIL.Image.Image directly from HF runtime!")
+            print("🖼️ [PREPROCESS] Got PIL.Image.Image directly.")
             return data.convert("RGB")
 
-        # 3️⃣ Raw bytes
         if isinstance(data, (bytes, bytearray)):
             print("🧾 [PREPROCESS] Treating input as raw bytes.")
             return Image.open(io.BytesIO(data)).convert("RGB")
 
-        # 4️⃣ Base64 string
         if isinstance(data, str):
             print(f"🧾 [PREPROCESS] Treating input as base64 string, len={len(data)}")
             decoded = base64.b64decode(data)
             return Image.open(io.BytesIO(decoded)).convert("RGB")
 
-        # 5️⃣ List (rare HF wrapper case)
         if isinstance(data, list) and len(data) > 0:
-            print("📚 [PREPROCESS] List input detected.")
             item = data[0]
             if isinstance(item, Image.Image):
-                print("📷 [PREPROCESS] List contains a PIL.Image.Image.")
                 return item.convert("RGB")
             if isinstance(item, (bytes, bytearray)):
                 return Image.open(io.BytesIO(item)).convert("RGB")
@@ -128,9 +122,18 @@ class EndpointHandler:
     def inference(self, image):
         print("🎯 [INFERENCE] Running ESRGAN upscaling...")
         print(f"📐 [INFERENCE] Input image size: {image.size}")
-        output, _ = self.upsampler.enhance(image, outscale=4)
-        print(f"✅ [INFERENCE] Output image size: {output.size}")
-        return output
+
+        # Convert PIL -> NumPy BGR for RealESRGAN
+        img_np = np.array(image)[:, :, ::-1]  # RGB -> BGR
+        print(f"🔍 [INFERENCE] Converted to NumPy: shape={img_np.shape}, dtype={img_np.dtype}")
+
+        output, _ = self.upsampler.enhance(img_np, outscale=4)
+        print(f"✅ [INFERENCE] Output NumPy shape: {output.shape}")
+
+        # Convert back to PIL RGB
+        output_rgb = Image.fromarray(output[:, :, ::-1])
+        print(f"✅ [INFERENCE] Converted back to PIL: size={output_rgb.size}")
+        return output_rgb
 
     # ==========================================================
     # POSTPROCESS