app.py

import gradio as gr
from huggingface_hub import InferenceClient

# Initialize the InferenceClient with the model name
# client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
client = InferenceClient("meta-llama/Llama-3.2-11B-Vision-Instruct")


def respond(
    message,
    history,
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    # Create a list of messages with the system message and user input
    messages = [{"role": "system", "content": system_message}, {"role": "user", "content": message}]

    # Calculate the total token count
    total_token_count = sum(len(m["content"].split()) for m in messages) + max_tokens

    # Truncate the input message if necessary
    if total_token_count > 4096:
        excess_tokens = total_token_count - 4096
        for i in range(len(messages) - 1, -1, -1):
            if len(messages[i]["content"].split()) > excess_tokens:
                messages[i]["content"] = " ".join(messages[i]["content"].split()[:-excess_tokens])
                break
            else:
                excess_tokens -= len(messages[i]["content"].split())
                messages[i]["content"] = ""

    # Get the response from the model
    response = client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=False,
        temperature=temperature,
        top_p=top_p,
    )

    # Return the response
    return response.choices[0].message.content


# Create a ChatInterface with the respond function and additional inputs
demo = 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__":
    demo.launch()