Update app.py

app.py

@@ -1,66 +1,114 @@
-import requests
-import os
 import gradio as gr
-from gradio_client import Client, handle_file
+from gradio_client import Client
 import random
-
+import os
+import requests
 
 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",
+    hf_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 upload_file_to_space(local_path):
-    """手动上传文件到目标 Space，返回远端 URL"""
+    """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"
+
     with open(local_path, "rb") as f:
         response = requests.post(
             upload_url,
             headers=headers,
-            files={"files": (os.path.basename(local_path), f, "image/jpeg")},
+            files={"files": (os.path.basename(local_path), f, mime_type)},
         )
-    
+
     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):
+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}")
         return None, seed
 
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
-    # 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}")
+    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"
 
-    # 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",
+            "mime_type": mime_type,
             "is_stream": False,
             "meta": {}
         },
         "caption": None
     }]
 
-    print(f"images_input: {images_input}")
+    print("[调用API] 输入参数：")
+    print(f"  remote_path: {remote_path}")
+    print(f"  prompt: {prompt}")
+    print(f"  lora_adapter: {lora_adapter}")
+    print(f"  seed: {seed}")
+    print(f"  guidance_scale: {guidance_scale}")
+    print(f"  steps: {steps}")
 
     try:
         result = space_client.predict(
@@ -74,12 +122,86 @@ def infer(image, prompt, lora_adapter, seed, randomize_seed, guidance_scale, ste
             api_name="/infer",
         )
 
+        print(f"[调用API] 返回值: {result}")
+
         image_info, seed_used = result
-        img_out = image_info.get("path") or image_info.get("url")
+
+        if isinstance(image_info, dict):
+            img_out = image_info.get("path") or image_info.get("url")
+        else:
+            img_out = image_info
+
         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
+        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)