## https://www.kaggle.com/code/unravel/fine-tuning-of-a-sql-model

import spaces
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import gradio as gr
import torch
from transformers.utils import logging
from example_queries import small_query, long_query

logging.set_verbosity_info()
logger = logging.get_logger("transformers")

model_name='t5-small'
tokenizer = AutoTokenizer.from_pretrained(model_name)
original_model = AutoModelForSeq2SeqLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)
original_model.to('cuda')

ft_model_name="cssupport/t5-small-awesome-text-to-sql"
ft_model = AutoModelForSeq2SeqLM.from_pretrained(ft_model_name, torch_dtype=torch.bfloat16)
ft_model.to('cuda')

@spaces.GPU
def translate_text(text):
   prompt = f"{text}"
   inputs = tokenizer(prompt, return_tensors='pt')
   inputs = inputs.to('cuda')

   try:
        output = tokenizer.decode(
            original_model.generate(
                inputs["input_ids"], 
                max_new_tokens=200,
            )[0], 
            skip_special_tokens=True
        )
        ft_output = tokenizer.decode(
            ft_model.generate(
                inputs["input_ids"], 
                max_new_tokens=200,
            )[0], 
            skip_special_tokens=True
        )
        return [output, ft_output]
   except Exception as e:
       return f"Error: {str(e)}"


with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            prompt = gr.Textbox(
                value=small_query,
                lines=8,
                placeholder="Enter prompt...",
                label="Prompt"
            )
            submit_btn = gr.Button(value="Generate")
        with gr.Column():
            orig_output = gr.Textbox(label="OriginalModel", lines=2) 
            ft_output = gr.Textbox(label="FTModel", lines=8)

    submit_btn.click(
        translate_text, inputs=[prompt], outputs=[orig_output, ft_output], api_name=False
    )
    examples = gr.Examples(
        examples=[
            [small_query],
            [long_query],
        ],
        inputs=[prompt],
    )

demo.launch(show_api=False, share=True, debug=True)