Upload app.py

app.py

@@ -1,28 +1,59 @@
+import os
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-# 替换为你想使用的模型
-client = InferenceClient(model="Qwen/Qwen1.5-7B-Chat")
+# 安全读取 Huggingface Token
+HF_TOKEN = os.getenv("HF_TOKEN")
+MODEL_NAME = "Qwen/Qwen3-235B-A22B"
 
-def predict(message, history):
-    messages = [{"role": "system", "content": "You are a helpful assistant."}]
-    for human, bot in history:
-        messages.append({"role": "user", "content": human})
-        messages.append({"role": "assistant", "content": bot})
+client = InferenceClient(model=MODEL_NAME, token=HF_TOKEN)
+
+def chat_fn(message, history, system_message, temperature, top_p, max_tokens, repetition_penalty):
+    # 构造消息
+    messages = []
+    if system_message:
+        messages.append({"role": "system", "content": system_message})
+    for user_msg, bot_msg in history:
+        messages.append({"role": "user", "content": user_msg})
+        messages.append({"role": "assistant", "content": bot_msg})
     messages.append({"role": "user", "content": message})
 
-    partial_text = ""
-    for chunk in client.chat_completion(model="Qwen/Qwen1.5-7B-Chat",
-                                        messages=messages,
-                                        stream=True,
-                                        temperature=0.7,
-                                        top_p=0.95):
-        if chunk.choices and chunk.choices[0].delta and "content" in chunk.choices[0].delta:
-            partial_text += chunk.choices[0].delta["content"]
-            yield partial_text
-
-chat_interface = gr.ChatInterface(fn=predict, title="🌟 Streaming Chat with Qwen",
-                                  examples=["介绍一下你自己", "讲一个冷笑话", "什么是人工智能？"],
-                                  retry_btn=None, undo_btn="撤回", clear_btn="清空")
-
-chat_interface.queue().launch()
+    # 请求模型
+    response = client.chat_completion(
+        messages=messages,
+        temperature=temperature,
+        top_p=top_p,
+        max_tokens=max_tokens,
+        repetition_penalty=repetition_penalty,
+    )
+
+    answer = response.choices[0].message["content"]
+    history.append((message, answer))
+    return history, history
+
+with gr.Blocks(theme=gr.themes.Base(), css="footer {display: none !important}") as demo:
+    gr.Markdown("# 🤖 Qwen3 Chatbot")
+    with gr.Row():
+        with gr.Column(scale=3):
+            chatbot = gr.Chatbot()
+            message = gr.Textbox(label="Your Message", placeholder="Type something...", scale=4)
+            submit = gr.Button("Send", variant="primary")
+            clear = gr.Button("Clear")
+
+            state = gr.State([])
+        with gr.Column(scale=1):
+            system_message = gr.Textbox(label="System Prompt", placeholder="You are a helpful assistant.")
+            temperature = gr.Slider(0, 1, value=0.7, step=0.05, label="Temperature")
+            top_p = gr.Slider(0.1, 1.0, value=0.9, step=0.05, label="Top-p")
+            max_tokens = gr.Slider(64, 2048, value=512, step=64, label="Max Tokens")
+            repetition_penalty = gr.Slider(1.0, 2.0, value=1.1, step=0.1, label="Repetition Penalty")
+
+    submit.click(chat_fn, 
+                 inputs=[message, state, system_message, temperature, top_p, max_tokens, repetition_penalty], 
+                 outputs=[chatbot, state])
+    message.submit(chat_fn, 
+                   inputs=[message, state, system_message, temperature, top_p, max_tokens, repetition_penalty], 
+                   outputs=[chatbot, state])
+    clear.click(lambda: ([], []), None, [chatbot, state])
+
+demo.launch()