app.py

import gradio as gr
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""

def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, min_p,):
    messages = [{"role": "system", "content": system_message}]

    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": message})

    model = AutoPeftModelForCausalLM.from_pretrained(
        "eforse01/lora_model", 
    )

    tokenizer = AutoTokenizer.from_pretrained("eforse01/lora_model")
    inputs = tokenizer.apply_chat_template(
        messages,
        tokenize = True,
        add_generation_prompt = True, 
        return_tensors = "pt",
    )

    output = model.generate(input_ids = inputs, max_new_tokens = max_tokens,
                    use_cache = True, temperature = temperature, min_p = min_p)
    
    response = tokenizer.batch_decode(output, skip_special_tokens=True)[0]

    yield response.split('assistant')[-1]

"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=2048, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=1.5, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.99,
            step=0.01,
            label="Min-p",
        ),
    ],
)

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