Update app.py

app.py

@@ -1,14 +1,14 @@
 import gradio as gr
-from gradio_client import Client, handle_file
 import random
 import os
+import requests
 
 HF_TOKEN = os.environ.get("girlToken")
 
-space_client = Client(
-    "prithivMLmods/Qwen-Image-Edit-2511-LoRAs-Fast",
-    token=HF_TOKEN  #不是hf_token
-    )
+API_BASE = "https://prithivmlmods-qwen-image-edit-2511-loras-fast.hf.space"
+UPLOAD_URL = f"{API_BASE}/gradio_api/upload"
+CALL_URL = f"{API_BASE}/gradio_api/call/v2/infer"
+POLL_URL = f"{API_BASE}/gradio_api/call/infer"  # need event_id
 
 LORA_STYLES = [
     'Multiple-Angles', 'Photo-to-Anime', 'Anime-V2', 'Light-Migration',
@@ -20,6 +20,40 @@ LORA_STYLES = [
 ]
 MAX_SEED = 2**31 - 1
 
+def upload_file(image_path):
+    files = {'files': (os.path.basename(image_path), open(image_path, "rb"), "application/octet-stream")}
+    headers = {'Authorization': f'Bearer {HF_TOKEN}'}
+    resp = requests.post(UPLOAD_URL, files=files, headers=headers)
+    resp.raise_for_status()
+    res = resp.json()
+    # 返回格式可能是list或dict
+    if isinstance(res, list) and len(res) > 0:
+        return res[0]
+    return res
+
+def call_infer(payload):
+    headers = {'Authorization': f'Bearer {HF_TOKEN}'}
+    resp = requests.post(CALL_URL, json=payload, headers=headers)
+    resp.raise_for_status()
+    job = resp.json()
+    event_id = job.get("event_id")
+    return event_id
+
+def poll_infer(event_id):
+    url = f"{POLL_URL}/{event_id}"
+    headers = {'Authorization': f'Bearer {HF_TOKEN}'}
+    # 简单轮询直到拿到结果（可优化为更好策略）
+    import time
+    for _ in range(60):
+        resp = requests.get(url, headers=headers)
+        resp.raise_for_status()
+        result = resp.json()
+        if result.get("status") == "complete":
+            return result.get("data"), result.get("outputs")
+        elif result.get("status") == "error":
+            raise Exception(result.get("error"))
+        time.sleep(2)  # 2秒再次轮询
+    raise TimeoutError("等候API返回超时")
 
 def infer(
     image,
@@ -42,59 +76,65 @@ def infer(
     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)
-
-    # 补全所有必要字段
-    # uploaded["url"] = None
-    # uploaded["size"] = os.path.getsize(image)
-    # uploaded["mime_type"] = "image/jpeg"
-    # uploaded["is_stream"] = False
-
-    #images_input = [{"image": uploaded, "caption": None}]
-
-    # 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}")
-
+    # 1. 上传文件
     try:
-        result = space_client.predict(
-            images=images_input,
-            prompt=prompt,
-            lora_adapter=lora_adapter,
-            seed=int(seed),
-            randomize_seed=bool(randomize_seed),
-            guidance_scale=int(guidance_scale),
-            steps=int(steps),
-            api_name="/infer",
-        )
-
-        print(f"[调用API] 返回值: {result}")
-
-        image_info, seed_used = result
+        uploaded_info = upload_file(image)
+        # 提交给API需要包装成和官方一样的dict
+        img_obj = {
+            "path": uploaded_info["path"],
+            "meta": {"_type": "gradio.FileData"},
+            "orig_name": os.path.basename(image)
+        }
+    except Exception as e:
+        print(f"[图片上传] 失败: {e}")
+        return None, seed
 
+    # 2. 准备参数并调用推理
+    payload = {
+        "images": [{"image": img_obj, "caption": None}],
+        "prompt": prompt,
+        "lora_adapter": lora_adapter,
+        "seed": int(seed),
+        "randomize_seed": bool(randomize_seed),
+        "guidance_scale": float(guidance_scale),
+        "steps": int(steps),
+    }
+
+    print("准备调用远端API:", payload)
+    try:
+        event_id = call_infer(payload)
+        print("API返回event_id: ", event_id)
+        # 3. 结果轮询
+        data, outputs = poll_infer(event_id)
+        print("[API 完成] data:", data, "outputs:", outputs)
+        # 解析输出（通常outputs可能就是结果图片的路径）
+        # 兼容dict或list
+        image_info = None
+        seed_used = seed
+        if outputs:
+            if isinstance(outputs, list) and len(outputs) == 2:
+                image_info, seed_used = outputs
+            elif isinstance(outputs, dict):
+                image_info = outputs.get("path") or outputs.get("url")
+                seed_used = outputs.get("seed", seed)
+            else:
+                image_info = outputs
+        elif data:
+            image_info = data
         if isinstance(image_info, dict):
             img_out = image_info.get("path") or image_info.get("url")
         else:
             img_out = image_info
-
+        # 补成绝对地址
+        if img_out and not img_out.startswith("http"):
+            img_out = API_BASE + img_out
         return img_out, int(seed_used)
-
     except Exception as e:
         import traceback
         traceback.print_exc()
-        print(f"[调用API] 异常: {e}")
+        print(f"[API 调用异常] {e}")
         return None, seed
 
-
 css = """
 #col-container {
     margin: 0 auto;
@@ -104,7 +144,7 @@ css = """
 
 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")
+        gr.Markdown("# 图像编辑 Demo\n基于 prithivMLmods/Qwen-Image-Edit-2511-LoRAs-Fast (新版API)")
 
         image = gr.Image(
             label="上传图片",