Update app.py

app.py

@@ -1,61 +1,62 @@
-import gradio as gr
-from gradio_client import Client, handle_file
-import random
+import requests
 import os
 
 HF_TOKEN = os.environ.get("girlToken")
-
-space_client = Client(
-    "prithivMLmods/Qwen-Image-Edit-2511-LoRAs-Fast",
-    token=HF_TOKEN
-    )
-
-LORA_STYLES = [
-    'Multiple-Angles', 'Photo-to-Anime', 'Anime-V2', 'Light-Migration',
-    'Upscaler', 'Style-Transfer', 'Manga-Tone', 'Anything2Real',
-    'Fal-Multiple-Angles', 'Polaroid-Photo', 'Unblur-Anything',
-    'Midnight-Noir-Eyes-Spotlight', 'Hyper-Realistic-Portrait',
-    'Ultra-Realistic-Portrait', 'Pixar-Inspired-3D', 'Noir-Comic-Book',
-    'Any-light', 'Studio-DeLight', 'Cinematic-FlatLog',
-]
-MAX_SEED = 2**31 - 1
-
-
-def infer(
-    image,
-    prompt,
-    lora_adapter,
-    seed,
-    randomize_seed,
-    guidance_scale,
-    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}")
+TARGET_SPACE_URL = "https://prithivmlmods-qwen-image-edit-2511-loras-fast.hf.space"
+
+def upload_file_to_space(local_path):
+    """手动上传文件到目标 Space，返回远端 URL"""
+    upload_url = f"{TARGET_SPACE_URL}/upload"
+    
+    headers = {}
+    if HF_TOKEN:
+        headers["Authorization"] = f"Bearer {HF_TOKEN}"
+    
+    with open(local_path, "rb") as f:
+        response = requests.post(
+            upload_url,
+            headers=headers,
+            files={"files": (os.path.basename(local_path), f, "image/jpeg")},
+        )
+    
+    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()
+        # 返回的是文件路径列表，取第一个
+        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}")
+
+
+def infer(image, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps, progress=gr.Progress(track_tqdm=True)):
+    if image is None or not os.path.exists(image):
         return None, seed
 
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
-    # 关键：用 handle_file 上传到目标 Space，得到远端可访问的文件对象
-    uploaded = handle_file(image)
-
-    # Gallery 元素格式：{"image": <上传后的文件对象>, "caption": None}
-    images_input = [{"image": uploaded, "caption": None}]
-
-    print("[调用API] 输入参数：")
-    print(f"  image path: {image}")
-    print(f"  uploaded: {uploaded}")
-    print(f"  prompt: {prompt}")
-    print(f"  lora_adapter: {lora_adapter}")
-    print(f"  seed: {seed}")
-    print(f"  guidance_scale: {guidance_scale}")
-    print(f"  steps: {steps}")
+    # 手动上传，拿到远端路径
+    remote_path = upload_file_to_space(image)
+    print(f"远端路径: {remote_path}")
+
+    # 用远端路径构造 images 参数
+    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": "image/jpeg",
+            "is_stream": False,
+            "meta": {}
+        },
+        "caption": None
+    }]
+
+    print(f"images_input: {images_input}")
 
     try:
         result = space_client.predict(
@@ -69,86 +70,12 @@ def infer(
             api_name="/infer",
         )
 
-        print(f"[调用API] 返回值: {result}")
-
         image_info, seed_used = result
-
-        if isinstance(image_info, dict):
-            img_out = image_info.get("path") or image_info.get("url")
-        else:
-            img_out = image_info
-
+        img_out = image_info.get("path") or image_info.get("url")
         return img_out, int(seed_used)
 
     except Exception as e:
         import traceback
         traceback.print_exc()
         print(f"[调用API] 异常: {e}")
-        return None, seed
-
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown("# 图像编辑 Demo\n基于 prithivMLmods/Qwen-Image-Edit-2511-LoRAs-Fast")
-
-        image = gr.Image(
-            label="上传图片",
-            sources=["upload"],
-            type="filepath",
-        )
-
-        prompt = gr.Text(
-            label="编辑描述（Prompt）",
-            placeholder="请输入图片编辑描述...",
-        )
-
-        lora_adapter = gr.Dropdown(
-            label="编辑风格（Style）",
-            choices=LORA_STYLES,
-            value="Photo-to-Anime"
-        )
-
-        run_button = gr.Button("执行编辑", variant="primary")
-
-        result = gr.Image(label="结果图片", show_label=True)
-
-        with gr.Accordion("高级设置", open=False):
-            seed = gr.Slider(
-                label="随机种子",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-            randomize_seed = gr.Checkbox(label="随机化种子", value=True)
-            guidance_scale = gr.Slider(
-                label="引导强度 (Guidance Scale)",
-                minimum=0.1,
-                maximum=10.0,
-                step=0.1,
-                value=1.0,
-            )
-            steps = gr.Slider(
-                label="推理步数 (Steps)",
-                minimum=1,
-                maximum=50,
-                step=1,
-                value=4,
-            )
-
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[image, prompt, lora_adapter, seed, randomize_seed, guidance_scale, steps],
-        outputs=[result, seed],
-    )
-
-if __name__ == "__main__":
-    demo.launch(ssr_mode=False, share=True)
+        return None, seed