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

# models
base_model_name = "mistralai/Mistral-7B-Instruct-v0.2"
adapter_model_name = "TymofiiNas/results"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_use_double_quant=False,
)
model = AutoModelForCausalLM.from_pretrained(
    base_model_name, quantization_config=bnb_config, device_map={"": 0}
)

model = PeftModel.from_pretrained(model, adapter_model_name)

tokenizer = AutoTokenizer.from_pretrained(base_model_name)


def generate_response(text):
    text = "<s> [INST]" + text + "[/INST]"
    encoded_input = tokenizer(text, return_tensors="pt", add_special_tokens=False)
    model_inputs = encoded_input.to("cuda")

    generated_ids = model.generate(
        **model_inputs,
        max_new_tokens=400,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id,
    )

    decoded_output = tokenizer.batch_decode(generated_ids)

    return decoded_output[0][len(text) :]


demo = gr.Interface(
    fn=generate_response,
    inputs="text",
    outputs="text",
)
gr.TabbedInterface([demo]).queue().launch()