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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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SYSTEM_PROMPT = """You are an expert assistant who can solve any task using code blobs. You will be given a task to solve as best you can. |
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To do so, you must follow a structured reasoning process in a cycle of: |
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1. **Thought:** |
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- Analyze the problem and explain your reasoning. |
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- Identify any necessary tools or techniques. |
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2. **Code:** |
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- Implement the solution using Python. |
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- Enclose the code block with `<end_code>`. |
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3. **Observation:** |
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- Explain the output and verify correctness. |
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4. **Final Answer:** |
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- Summarize the solution clearly. |
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Always adhere to the **Thought → Code → Observation → Final Answer** structure. |
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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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system_message = SYSTEM_PROMPT |
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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() |
