import torch
import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM

MODEL = "docto/Docto-Bot"

tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForCausalLM.from_pretrained(MODEL)

device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token


def get_reply(user_input):

    prompt = f"Question: {user_input}\nAnswer:"

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

    outputs = model.generate(
        **inputs,
        max_new_tokens=150,
        do_sample=True,
        temperature=0.7,
        top_k=50,
        top_p=0.9,
        repetition_penalty=1.15,
        no_repeat_ngram_size=3,
        pad_token_id=tokenizer.eos_token_id,
        eos_token_id=tokenizer.eos_token_id
    )

    response = tokenizer.decode(
        outputs[0],
        skip_special_tokens=True
    )

    if "Answer:" in response:
        response = response.split("Answer:", 1)[1]

    return response.strip()


iface = gr.Interface(
    fn=get_reply,

    inputs=gr.Textbox(
        lines=2,
        placeholder="Ask a medical question..."
    ),

    outputs=gr.Textbox(
        label="Response"
    ),

    title="Docto-Bot",

    description="Medical Question Answering Bot"
)

iface.launch()