import gradio as gr
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import re
import os

model_name = "google/gemma-2b"
peft_model = "kazuma313/gemma-dokter-ft"
device_map = "auto"
hf_token = os.getenv('hftoken')

base_model = AutoModelForCausalLM.from_pretrained(
    model_name,
    token=hf_token,
    low_cpu_mem_usage=True,
    return_dict=True,
    torch_dtype=torch.float16,
    device_map=device_map,
)
model = PeftModel.from_pretrained(base_model, peft_model)
model = model.merge_and_unload()

# Reload tokenizer to save it
tokenizer = AutoTokenizer.from_pretrained(model_name, 
                                          token=hf_token,
                                          trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"

def echo(message, history, tokens):
    pattern = r'Step \d+/\d+|^\d+\.\s*'
    input_ids = tokenizer(message, return_tensors="pt")
    outputs = model.generate(**input_ids, max_length=tokens)
    answer = tokenizer.decode(outputs[0], skip_special_tokens=True).split('Answer:')[-1]
    clean_answer = re.sub(pattern, '', answer)

    return clean_answer


demo = gr.ChatInterface(echo, 
                        examples = [["what is the negative effect of alcohol?"],
                                    ["i have lack of sleep, what happend if continously do this?"]],
                        title="dokter Bot",
                        retry_btn=None,
                        undo_btn="Delete Previous",
                        clear_btn="Clear",
                        additional_inputs=[
                            gr.Slider(64, 256, value=124)
                        ], 
    
                       )
demo.launch()