app.py

from huggingface_hub import InferenceClient
import gradio as gr

client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")


# This allows the model to consider its prior context
def format_prompt(message, history):
    prompt = "<s>"
    for user_prompt, bot_response in history:
        prompt += f"[INST] {user_prompt} [/INST]"
        prompt += f" {bot_response}</s> "
    prompt += f"[INST] {message} [/INST]"
    return prompt


def generate(
    prompt,
    history,
    system_prompt,
    temperature=0.9,
    max_new_tokens=256,
    top_p=0.95,
    repetition_penalty=1.0,
):
    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )

    formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", history)
    stream = client.text_generation(
        formatted_prompt,
        **generate_kwargs,
        stream=True,
        details=True,
        return_full_text=False,
    )
    output = ""

    # How does streaming works
    for response in stream:
        output += response.token.text
        yield output
    return output


additional_inputs = [
    gr.Textbox(
        label="System Prompt",
        max_lines=1,
        interactive=True,
    ),
    gr.Slider(
        label="Temperature",
        value=0.9,
        minimum=0.0,
        maximum=1.0,
        step=0.05,
        interactive=True,
        info="Higher values produce more diverse outputs",
    ),
    gr.Slider(
        label="Max new tokens",
        value=256,
        minimum=0,
        maximum=1048,
        step=64,
        interactive=True,
        info="The maximum numbers of new tokens",
    ),
    gr.Slider(
        label="Top-p (nucleus sampling)",
        value=0.90,
        minimum=0.0,
        maximum=1,
        step=0.05,
        interactive=True,
        info="Higher values sample more low-probability tokens",
    ),
    gr.Slider(
        label="Repetition penalty",
        value=1.2,
        minimum=1.0,
        maximum=2.0,
        step=0.05,
        interactive=True,
        info="Penalize repeated tokens",
    ),
]

examples = [
    [
        "I'm planning a vacation to Japan. Can you suggest a one-week itinerary including must-visit places and local cuisines to try?",
        None,
        None,
        None,
        None,
        None,
    ],
    [
        "Can you write a short story about a time-traveling detective who solves historical mysteries?",
        None,
        None,
        None,
        None,
        None,
    ],
    [
        "I'm trying to learn French. Can you provide some common phrases that would be useful for a beginner, along with their pronunciations?",
        None,
        None,
        None,
        None,
        None,
    ],
    [
        "I have chicken, rice, and bell peppers in my kitchen. Can you suggest an easy recipe I can make with these ingredients?",
        None,
        None,
        None,
        None,
        None,
    ],
    [
        "Can you explain how the QuickSort algorithm works and provide a Python implementation?",
        None,
        None,
        None,
        None,
        None,
    ],
    [
        "What are some unique features of Rust that make it stand out compared to other systems programming languages like C++?",
        None,
        None,
        None,
        None,
        None,
    ],
]

iface = gr.ChatInterface(
    fn=generate,
    chatbot=gr.Chatbot(
        show_label=False,
        show_share_button=False,
        show_copy_button=True,
        likeable=True,
        layout="panel",
    ),
    additional_inputs=additional_inputs,
    title="Mixtral 46.7B",
    examples=examples,
    concurrency_limit=20,
)
iface.launch(show_api=False)