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


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


base_model_id = "mistralai/Mistral-7B-v0.1"
ft_model_id = "asusevski/mistraloo-sft"


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


base_model_id = "mistralai/Mistral-7B-v0.1"
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    quantization_config=bnb_config,
    device_map="auto",
    trust_remote_code=True,
    token=True
)

model = PeftModel.from_pretrained(base_model, ft_model_id).to(device)
model.eval()


def uwaterloo_output(post_title, post_text):
    prompt = f"""
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
            
### Instruction:
Respond to the reddit post in the style of a University of Waterloo student.

### Input:
{post_title}
{post_text}

### Response:
"""
    model_input = tokenizer(prompt, return_tensors="pt").to(device)
    with torch.no_grad():
        model_output = model.generate(**model_input, max_new_tokens=256, repetition_penalty=1.15)[0]
    output = tokenizer.decode(model_output, skip_special_tokens=True)
    return output.split('### Response:\n')[-1]


iface = gr.Interface(
    fn=uwaterloo_output,
    inputs=[
        gr.Textbox("", label="Post Title"),
        gr.Textbox("", label="Post Text"),
    ],
    outputs=gr.Textbox("", label="Mistraloo-SFT")
)

iface.launch()




# base_model_id = "mistralai/Mistral-7B-v0.1"
# bnb_config = BitsAndBytesConfig(
#     load_in_4bit=True,
#     bnb_4bit_use_double_quant=True,
#     bnb_4bit_quant_type="nf4",
#     bnb_4bit_compute_dtype=torch.bfloat16
# )


# base_model = AutoModelForCausalLM.from_pretrained(
#     base_model_id,  # Mistral, same as before
#     quantization_config=bnb_config,  # Same quantization config as before
#     device_map="auto",
#     trust_remote_code=True,
#     use_auth_token=True
# )


# ft_model = PeftModel.from_pretrained(base_model, "mistral-mistraloo/checkpoint-500")