| import os |
| import gradio as gr |
| from openai import AsyncOpenAI |
| from dotenv import load_dotenv |
| import re |
|
|
| load_dotenv() |
|
|
| |
| api_key = os.getenv("GROQ_API_KEY") |
| if not api_key: |
| raise RuntimeError("GROQ_API_KEY is not set") |
|
|
| client = AsyncOpenAI( |
| api_key=api_key, |
| base_url="https://api.groq.com/openai/v1", |
| timeout=1200.0 |
| ) |
|
|
| def sanitize_messages(history): |
| sanitized = [] |
| for m in history: |
| |
| if isinstance(m, dict): |
| sanitized.append({"role": m["role"], "content": m["content"]}) |
| |
| elif isinstance(m, (list, tuple)): |
| if m[0]: sanitized.append({"role": "user", "content": m[0]}) |
| if m[1]: sanitized.append({"role": "assistant", "content": m[1]}) |
| return sanitized |
|
|
| def strip_think(text: str) -> str: |
| return re.sub(r"<think>.*?</think>", "", text, flags=re.DOTALL).strip() |
|
|
| async def respond( |
| message, |
| history, |
| system_message, |
| max_tokens, |
| temperature, |
| top_p, |
| ): |
| |
| messages = [{"role": "system", "content": system_message}] |
| messages.extend(sanitize_messages(history)) |
| messages.append({"role": "user", "content": message}) |
|
|
| try: |
| completion = await client.chat.completions.create( |
| model="qwen/qwen3-32b", |
| messages=messages, |
| max_tokens=max_tokens, |
| temperature=temperature, |
| top_p=top_p, |
| ) |
|
|
| text = completion.choices[0].message.content |
| return strip_think(text) |
|
|
| except Exception as e: |
| return f"Error: {str(e)}" |
|
|
|
|
| chatbot = 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)", |
| ), |
| ], |
| ) |
|
|
| if __name__ == "__main__": |
| chatbot.queue(default_concurrency_limit=5).launch() |
|
|
