Update app.py

app.py

@@ -1,64 +1,55 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("szk2024/test")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+from huggingface_hub import hf_hub_download
+import pickle
+import os
+
+# 1) 下载并加载模型（pickle 文件）
+def load_pickled_object(repo_id: str, filename: str):
+    # 如果已经下载过就不重复下载
+    cache_dir = os.path.join(".cache", repo_id.replace("/", "_"))
+    os.makedirs(cache_dir, exist_ok=True)
+    local_path = hf_hub_download(
+        repo_id=repo_id,
+        filename=filename,
+        cache_dir=cache_dir,
+        force_download=False  # 如果本地已有就不重新下
+    )
+    # 反序列化
+    with open(local_path, "rb") as f:
+        obj = pickle.load(f)
+    return obj
+
+# 在 Space 启动时就加载一次
+# 请替换成你自己的 repo id 和 pickle 文件名
+MODEL_REPO = "szk2024/test"
+MODEL_FILE = "evil_model.pkl"
+
+try:
+    model = load_pickled_object(MODEL_REPO, MODEL_FILE)
+except Exception as e:
+    # 如果出错可以打印日志
+    print("❌ 模型加载失败：", e)
+    model = None
+
+# 2) 定义预测函数（根据你的 pickle 对象改写）
+def predict(text: str):
+    if model is None:
+        return "模型加载失败，请检查日志"
+    # 假设你的 pickle 对象有一个 predict 方法
+    try:
+        res = model.predict([text])
+        return str(res)
+    except Exception as e:
+        return f"预测失败：{e}"
+
+# 3) 用 Gradio 搭个简单的文本接口
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(lines=2, placeholder="在此输入内容…"),
+    outputs="text",
+    title="Pickle 模型调用示例",
+    description="从 Hugging Face Hub 下载 pickle 并反序列化后预测"
 )
 
-
 if __name__ == "__main__":
-    demo.launch()
+    iface.launch(server_name="0.0.0.0", server_port=7860)