init

app.py

@@ -1,11 +1,9 @@
 import spaces
-import os
 import gradio as gr
 from transformers import AutoModelForCausalLM, AutoTokenizer
-from huggingface_hub import whoami
 
 # 定义系统提示语
-system_prompt = """你是 Skywork-o1，Skywork AI 开发的思维模型，擅长通过深度思考解决涉及数学、编码和逻辑推理的复杂问题。"""
+system_prompt = """你是 Skywork-o1，Skywork AI 开发的思维模型，擅长通过深度思考解决涉及数学、编码和逻辑推理的复杂问题。面对用户请求时，你首先会进行一段漫长而深入的思考过程，探索问题的可能解决方案。完成思考后，你会在回复中详细解释解决过程。"""
 
 # 初始化模型和分词器
 model_name = "Skywork/Skywork-o1-Open-Llama-3.1-8B"
@@ -62,45 +60,19 @@ def respond(
     )
     return completion
 
-# 登录状态变量
-user_logged_in = {"status": False, "username": ""}
-
-# 定义登录按钮的回调函数
-def login_to_huggingface(hf_token):
-    from huggingface_hub import login
-
-    try:
-        login(hf_token)
-        user_info = whoami(hf_token)
-        user_logged_in["status"] = True
-        user_logged_in["username"] = user_info["name"]
-        return f"Successfully logged in as {user_info['name']}."
-    except Exception as e:
-        user_logged_in["status"] = False
-        return f"Login failed: {str(e)}"
-
 # 定义Gradio界面
-with gr.Blocks() as demo:
-    with gr.Row():
-        gr.Markdown("### Skywork-o1 Chat Interface")
-
-    with gr.Row():
-        gr.Markdown("#### Hugging Face Login")
-        hf_token_input = gr.Textbox(label="Hugging Face Token", placeholder="Enter your Hugging Face token here")
-        login_button = gr.Button("Login")
-        login_status = gr.Textbox(label="Login Status", interactive=False)
-
-    login_button.click(fn=login_to_huggingface, inputs=hf_token_input, outputs=login_status)
-
-    gr.ChatInterface(
-        fn=respond,
-        additional_inputs=[
-            gr.Textbox(value=system_prompt, 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)"),
-        ]
-    )
+demo = gr.ChatInterface(
+    fn=respond,
+    additional_inputs=[
+        gr.Textbox(value=system_prompt, 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)"
+        ),
+    ],
+    # chatbot_style="default"
+)
 
 if __name__ == "__main__":
     demo.launch()