app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
from huggingface_hub import login

# Hugging Face model repo ID (must contain HF model weights, NOT .gguf)
MODEL_ID = "Selinaliu1030/lora_model"

# Load tokenizer + model
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    device_map="auto",            # uses GPU if available
    torch_dtype="auto",           # automatically picks fp16/bf16
    low_cpu_mem_usage=True,
)

def respond(
    message,
    history: list[dict[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
    hf_token,   # still required by UI signature; unused
):
    # Build prompt
    messages = [{"role": "system", "content": system_message}]
    messages.extend(history)
    messages.append({"role": "user", "content": message})

    prompt = ""
    for msg in messages:
        prompt += f"<{msg['role']}>: {msg['content']}\n"
    prompt += "<assistant>: "

    # Tokenize
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

    # Generate
    output = model.generate(
        **inputs,
        max_new_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id,
    )

    # Decode
    result = tokenizer.decode(output[0], skip_special_tokens=True)
    # Extract only the assistant's response
    assistant_reply = result.split("<assistant>:")[-1].strip()

    yield assistant_reply


# Gradio UI
chatbot = gr.ChatInterface(
    respond,
    type="messages",
    additional_inputs=[
        gr.Textbox(value="You are a helpful assistant.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=256, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=2.0, 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",
        ),
    ],
)

with gr.Blocks() as demo:
    with gr.Sidebar():
        gr.LoginButton()
    chatbot.render()

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