Update app.py

app.py

@@ -3,8 +3,6 @@ from gradio_client import Client, handle_file
 import random
 import os
 from PIL import Image
-import tempfile
-import shutil
 
 # API client for the external Space
 space_client = Client("prithivMLmods/Qwen-Image-Edit-2511-LoRAs-Fast")
@@ -32,62 +30,24 @@ LORA_STYLES = [
 ]
 MAX_SEED = 2**31 - 1
 
-def encode_image_to_gallery_dict(image_data):
+def encode_image_to_gallery_dict(image_path):
     """
-    Support both file path or np.ndarray/PIL.Image as input.
-    Return the dict structure required for GalleryData, containing "image" (binary data) and "orig_name".
+    Returns the dict structure required for GalleryData, containing "image" (binary data) and "orig_name".
+    This directly loads the image and provides the binary data instead of a path, fixing the GalleryData validation error.
     """
-    # Direct path (legacy): works locally but not on HF
-    if isinstance(image_data, str) and os.path.isfile(image_data):
-        try:
-            with open(image_data, "rb") as f:
-                image_bytes = f.read()
-            return {"image": image_bytes, "orig_name": os.path.basename(image_data)}
-        except Exception as e:
-            print(f"无法读取图片: {image_data}: {e}")
-            return None
-
-    # Gradio on Huggingface likely to pass PIL.Image or ndarray/bytes
-    # Try handling PIL.Image.Image
-    if hasattr(image_data, "save"):
-        try:
-            with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
-                image_data.save(tmp, format="PNG")
-                tmp.flush()
-                tmp_name = tmp.name
-                with open(tmp_name, "rb") as f:
-                    image_bytes = f.read()
-                result = {"image": image_bytes, "orig_name": os.path.basename(tmp_name)}
-            os.remove(tmp_name)
-            return result
-        except Exception as e:
-            print(f"无法处理PIL.Image: {e}")
-            return None
-
-    # If numpy image, treat as PIL then to bytes
+    if not image_path or not isinstance(image_path, str) or not os.path.isfile(image_path):
+        return None
     try:
-        import numpy as np
-        if isinstance(image_data, np.ndarray):
-            im_pil = Image.fromarray(image_data)
-            with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
-                im_pil.save(tmp, format="PNG")
-                tmp.flush()
-                tmp_name = tmp.name
-                with open(tmp_name, "rb") as f:
-                    image_bytes = f.read()
-                result = {"image": image_bytes, "orig_name": os.path.basename(tmp_name)}
-            os.remove(tmp_name)
-            return result
-    except ImportError:
-        pass
-
-    # If already bytes-like, accept (for more robust future-proofing)
-    if isinstance(image_data, (bytes, bytearray)):
-        return {"image": image_data, "orig_name": "uploaded.png"}
-
-    # Nothing worked
-    print(f"encode_image_to_gallery_dict: 无法处理输入: {type(image_data)}, value={image_data}")
-    return None
+        # Read image into bytes
+        with open(image_path, "rb") as f:
+            image_bytes = f.read()
+        return {
+            "image": image_bytes,
+            "orig_name": os.path.basename(image_path)
+        }
+    except Exception as e:
+        print(f"无法读取图片: {image_path}: {e}")
+        return None
 
 def infer(
     image,
@@ -102,20 +62,19 @@ def infer(
     # Prepare images_input as a list of dicts containing "image" (binary data)
     images_input = []
     if image is not None:
-        # gr.Image may return ndarray, PIL.Image, path(str), or list thereof!
         if isinstance(image, list):
             for im in image:
                 img_obj = encode_image_to_gallery_dict(im)
                 if img_obj:
                     images_input.append(img_obj)
                 else:
-                    print(f"警告: 输入图片类型无法处理: {im}")
+                    print(f"警告: 路径无效或无法读取: {im}")
         else:
             img_obj = encode_image_to_gallery_dict(image)
             if img_obj:
                 images_input.append(img_obj)
             else:
-                print(f"警告: 输入图片类型无法处理: {image}")
+                print(f"警告: 路径无效或无法读取: {image}")
 
     if len(images_input) == 0:
         print("未检测到有效图片，未能上传图片。")
@@ -135,6 +94,7 @@ def infer(
     print(f"  steps: {steps}")
 
     try:
+        # Use plain list of dicts with "image" key as images parameter
         result = space_client.predict(
             images=images_input,
             prompt=prompt,
@@ -146,6 +106,7 @@ def infer(
             api_name="/infer",
         )
         print(f"[调用API] space_client.predict 返回值: {result}")
+        # result可能不是元组形式，需增加健壮性
         if isinstance(result, dict):
             image_info = result
             seed_used = result.get("seed", seed)
@@ -201,7 +162,7 @@ with gr.Blocks() as demo:
             image = gr.Image(
                 label="上传图片",
                 sources=["upload"],
-                type="numpy",  # 修改为 numpy，自动适配array/PIL/PATH，兼容HF Spaces
+                type="filepath",
                 elem_id="input-image"
             )
         with gr.Row():