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import gradio as gr |
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from huggingface_hub import InferenceClient |
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import subprocess |
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import threading |
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import sys |
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import time |
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SCRIPT_CMD = "bash ./script.sh" |
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def respond( |
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message, |
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history: list[dict[str, str]], |
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system_message, |
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max_tokens, |
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temperature, |
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top_p, |
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hf_token: gr.OAuthToken, |
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): |
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client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b") |
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messages = [{"role": "system", "content": system_message}] |
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messages.extend(history) |
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messages.append({"role": "user", "content": message}) |
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response = "" |
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for msg in client.chat_completion( |
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messages, |
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max_tokens=max_tokens, |
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stream=True, |
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temperature=temperature, |
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top_p=top_p, |
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): |
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choices = msg.choices |
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token = "" |
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if len(choices) and choices[0].delta.content: |
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token = choices[0].delta.content |
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response += token |
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yield response |
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chatbot = gr.ChatInterface( |
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respond, |
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type="messages", |
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additional_inputs=[ |
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gr.Textbox(value="You are a friendly Chatbot.", label="System message"), |
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gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), |
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), |
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gr.Slider( |
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minimum=0.1, |
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maximum=1.0, |
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value=0.95, |
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step=0.05, |
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label="Top-p (nucleus sampling)", |
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), |
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], |
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) |
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def stream_script(cmd): |
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p = subprocess.Popen( |
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cmd, |
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stdout=subprocess.PIPE, |
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stderr=subprocess.STDOUT, |
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shell=True, |
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text=True, |
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bufsize=1, |
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) |
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for line in p.stdout: |
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sys.stdout.write(line) |
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p.wait() |
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sys.stdout.write(f"[script exited with code {p.returncode}]\n") |
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with gr.Blocks() as demo: |
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with gr.Sidebar(): |
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gr.LoginButton() |
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chatbot.render() |
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if __name__ == "__main__": |
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demo.launch(prevent_thread_lock=True) |
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t_script = threading.Thread(target=stream_script, args=(SCRIPT_CMD,)) |
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t_script.start() |
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try: |
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while True: |
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time.sleep(1) |
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except KeyboardInterrupt: |
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sys.stdout.write("[shutdown requested]\n") |