Update app.py

app.py

@@ -2,10 +2,10 @@ import gradio as gr
 from gradio_client import Client
 import random
 import os
-import requests
+import base64
+import mimetypes
 
 HF_TOKEN = os.environ.get("girlToken")
-TARGET_SPACE_URL = "https://prithivmlmods-qwen-image-edit-2511-loras-fast.hf.space"
 
 space_client = Client(
     "prithivMLmods/Qwen-Image-Edit-2511-LoRAs-Fast",
@@ -23,36 +23,28 @@ LORA_STYLES = [
 MAX_SEED = 2**31 - 1
 
 
-def upload_file_to_space(local_path):
-    """手动上传文件到目标 Space，返回远端路径"""
-    upload_url = f"{TARGET_SPACE_URL}/upload"
-
-    headers = {}
-    if HF_TOKEN:
-        headers["Authorization"] = f"Bearer {HF_TOKEN}"
-
-    mime_type = "image/jpeg"
-    if local_path.lower().endswith(".png"):
-        mime_type = "image/png"
-    elif local_path.lower().endswith(".webp"):
-        mime_type = "image/webp"
+def image_to_gallery_item(local_path):
+    """按照 API 定义，把本地图片转成 Gallery 元素"""
+    mime_type, _ = mimetypes.guess_type(local_path)
+    if not mime_type:
+        mime_type = "image/jpeg"
 
     with open(local_path, "rb") as f:
-        response = requests.post(
-            upload_url,
-            headers=headers,
-            files={"files": (os.path.basename(local_path), f, mime_type)},
-        )
+        b64 = base64.b64encode(f.read()).decode("utf-8")
 
-    print(f"上传状态码: {response.status_code}")
-    print(f"上传响应: {response.text}")
-
-    if response.status_code == 200:
-        result = response.json()
-        remote_path = result[0] if isinstance(result, list) else result
-        return remote_path
-    else:
-        raise Exception(f"上传失败: {response.status_code} {response.text}")
+    # url 字段明确支持 base64 encoded image
+    return {
+        "image": {
+            "path": None,
+            "url": f"data:{mime_type};base64,{b64}",
+            "size": os.path.getsize(local_path),
+            "orig_name": os.path.basename(local_path),
+            "mime_type": mime_type,
+            "is_stream": False,
+            "meta": {"_type": "gradio.FileData"}
+        },
+        "caption": None
+    }
 
 
 def infer(
@@ -65,45 +57,18 @@ def infer(
     steps,
     progress=gr.Progress(track_tqdm=True),
 ):
-    if image is None:
-        print("未上传图片")
-        return None, seed
-
-    if not os.path.exists(image):
-        print(f"图片路径不存在: {image}")
+    if image is None or not os.path.exists(image):
+        print("未上传图片或路径不存在")
         return None, seed
 
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
-    try:
-        remote_path = upload_file_to_space(image)
-        print(f"远端路径: {remote_path}")
-    except Exception as e:
-        print(f"上传图片失败: {e}")
-        return None, seed
-
-    mime_type = "image/jpeg"
-    if image.lower().endswith(".png"):
-        mime_type = "image/png"
-    elif image.lower().endswith(".webp"):
-        mime_type = "image/webp"
-
-    images_input = [{
-        "image": {
-            "path": remote_path,
-            "url": f"{TARGET_SPACE_URL}/file={remote_path}",
-            "size": os.path.getsize(image),
-            "orig_name": os.path.basename(image),
-            "mime_type": mime_type,
-            "is_stream": False,
-            "meta": {}
-        },
-        "caption": None
-    }]
+    images_input = [image_to_gallery_item(image)]
 
     print("[调用API] 输入参数：")
-    print(f"  remote_path: {remote_path}")
+    print(f"  orig_name: {os.path.basename(image)}")
+    print(f"  mime_type: {mimetypes.guess_type(image)[0]}")
     print(f"  prompt: {prompt}")
     print(f"  lora_adapter: {lora_adapter}")
     print(f"  seed: {seed}")