import os
import torch
import gradio as gr
import transformers
import accelerate
import huggingface_hub
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel

huggingface_hub.login(token = os.environ['HF_TOKEN'])

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

base_model_id = "google/gemma-7b"
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    #quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True,
    token=True,
    offload_folder="offload/",
)

tokenizer = AutoTokenizer.from_pretrained(base_model_id, add_bos_token=True, trust_remote_code=True)

ft_model = PeftModel.from_pretrained(base_model, "msinghy/gemma-7b-ft-80row-alpaca-correcting-mistakes", offload_folder="offload/")

def respond(query):
    eval_prompt = "###Input: " + query + "\n\n###Output: "
    model_input = tokenizer(eval_prompt, return_tensors="pt")#.to("cuda")
    output = ft_model.generate(input_ids=model_input["input_ids"]#.to(device),
                           ,attention_mask=model_input["attention_mask"], 
                           max_new_tokens=500)
    result = tokenizer.decode(output[0], skip_special_tokens=True).replace(eval_prompt, "")
    return result

def chat_response(message, history):
    return respond(message)

demo = gr.ChatInterface(chat_response)

demo.launch()