app.py

# Inference

import gradio as gr
from huggingface_hub import InferenceClient

model = "meta-llama/Llama-3.2-3B-Instruct"
client = InferenceClient(model)

def fn(
    prompt,
    #history: list[tuple[str, str]],
    history: list,
    #system_prompt,
    max_tokens,
    temperature,
    top_p,
):
    #messages = [{"role": "system", "content": system_prompt}]
    #history.append({"role": "user", "content": prompt})
    
    messages = [{"role": "user", "content": prompt}]
    history.append(messages)

    #for val in history:
    #    if val[0]:
    #        messages.append({"role": "user", "content": val[0]})
    #    if val[1]:
    #        messages.append({"role": "assistant", "content": val[1]})
    
    #messages.append({"role": "user", "content": prompt})

    stream = client.chat.completions.create(
        model = model,
        #messages = messages,
        messages = history,
        max_tokens = max_tokens,
        temperature = temperature,
        top_p = top_p,
        stream = True
    )
    
    #response = ""
    #for chunk in stream:
    #    response += chunk.choices[0].delta.content
    #return response

    chunks = []
    for chunk in stream:
        chunks.append(chunk.choices[0].delta.content or "")
        yield "".join(chunks)

app = gr.ChatInterface(
    fn = fn,
    type = "messages",
    additional_inputs = [
        #gr.Textbox(value="You are a helpful assistant.", label="System Prompt"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max 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"),
    ],
    title = "Meta Llama",
    description = model,
)

if __name__ == "__main__":
    app.launch()