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import gradio as gr
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from openai import OpenAI
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import os
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css = '''
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.gradio-container{max-width: 1000px !important}
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h1{text-align:center}
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footer {
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visibility: hidden
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}
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'''
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ACCESS_TOKEN = os.getenv("HF_TOKEN")
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client = OpenAI(
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base_url="https://api-inference.huggingface.co/v1/",
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api_key=ACCESS_TOKEN,
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)
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def respond(
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message,
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history: list[tuple[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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):
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messages = [{"role": "system", "content": system_message}]
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for val in history:
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if val[0]:
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messages.append({"role": "user", "content": val[0]})
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if val[1]:
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messages.append({"role": "assistant", "content": val[1]})
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messages.append({"role": "user", "content": message})
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response = ""
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for message in client.chat.completions.create(
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model="meta-llama/Meta-Llama-3.1-8B-Instruct",
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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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messages=messages,
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):
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token = message.choices[0].delta.content
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response += token
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yield response
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demo = gr.ChatInterface(
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respond,
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additional_inputs=[
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gr.Textbox(value="", 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",
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),
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],
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css=css
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)
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if __name__ == "__main__":
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demo.launch() |