Update app.py

app.py

@@ -3,6 +3,8 @@ 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")
@@ -30,24 +32,62 @@ LORA_STYLES = [
 ]
 MAX_SEED = 2**31 - 1
 
-def encode_image_to_gallery_dict(image_path):
+def encode_image_to_gallery_dict(image_data):
     """
-    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.
+    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".
     """
-    if not image_path or not isinstance(image_path, str) or not os.path.isfile(image_path):
-        return None
+    # 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
     try:
-        # 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
+        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
 
 def infer(
     image,
@@ -62,19 +102,20 @@ 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: 路径无效或无法读取: {im}")
+                    print(f"警告: 输入图片类型无法处理: {im}")
         else:
             img_obj = encode_image_to_gallery_dict(image)
             if img_obj:
                 images_input.append(img_obj)
             else:
-                print(f"警告: 路径无效或无法读取: {image}")
+                print(f"警告: 输入图片类型无法处理: {image}")
 
     if len(images_input) == 0:
         print("未检测到有效图片，未能上传图片。")
@@ -94,7 +135,6 @@ 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,
@@ -106,7 +146,6 @@ 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)
@@ -162,7 +201,7 @@ with gr.Blocks() as demo:
             image = gr.Image(
                 label="上传图片",
                 sources=["upload"],
-                type="filepath",
+                type="numpy",  # 修改为 numpy，自动适配array/PIL/PATH，兼容HF Spaces
                 elem_id="input-image"
             )
         with gr.Row():