import spaces
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import gradio as gr

title = """# 🙋🏻‍♂️Welcome to 🌟Tonic's Defog 🌬️🌁🌫️SqlCoder-2 
You can use this Space to test out the current model [defog/sqlcoder2](https://huggingface.co/defog/sqlcoder2). [defog/sqlcoder2](https://huggingface.co/defog/sqlcoder2) is a 15B parameter model that doesn't outperform gpt-4 and gpt-4-turbo for natural language to SQL generation tasks on our sql-eval framework, and significantly outperforms all popular open-source models.
You can also use efog 🌬️🌁🌫️SqlCoder by cloning this space. 🧬🔬🔍 Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic/sqlcoder2?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a></h3> 
Join us : 🌟TeamTonic🌟 is always making cool demos! Join our active builder's🛠️community 👻[![Let's build the future of AI together! 🚀🤖](https://discordapp.com/api/guilds/1109943800132010065/widget.png)](https://discord.gg/GWpVpekp) On 🤗Huggingface: [TeamTonic](https://huggingface.co/TeamTonic) & [MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Polytonic](https://github.com/tonic-ai) & contribute to 🌟 [Poly](https://github.com/tonic-ai/poly) 🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗
"""

def load_tokenizer_model(model_name):
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        trust_remote_code=True,
        torch_dtype=torch.float16,
        device_map="auto",
        use_cache=True,
    )
    return tokenizer, model

def generate_prompt(question, prompt_file="prompt.md", metadata_file="metadata.sql"):
    with open(prompt_file, "r") as f:
        prompt = f.read()

    with open(metadata_file, "r") as f:
        table_metadata_string = f.read()

    prompt = prompt.format(
        user_question=question, table_metadata_string=table_metadata_string
    )
    return prompt

@spaces.GPU
def run_inference(question, model, tokenizer):
    model.to('cuda')
    prompt = generate_prompt(question)
    eos_token_id = tokenizer.eos_token_id
    pipe = pipeline(
        "text-generation",
        model=model,
        tokenizer=tokenizer,
        max_new_tokens=300,
        do_sample=False,
        num_beams=5,
    )
    generated_query = (
        pipe(
            prompt,
            num_return_sequences=1,
            eos_token_id=eos_token_id,
            pad_token_id=eos_token_id,
        )[0]["generated_text"]
        .split("```sql")[-1]
        .split("```")[0]
        .split(";")[0]
        .strip()
        + ";"
    )
    return generated_query

def main():
    model_name = "defog/sqlcoder2"
    tokenizer, model = load_tokenizer_model(model_name)

    with gr.Blocks() as demo:
        gr.Markdown(title)
        question = gr.Textbox(label="Enter your question")
        submit = gr.Button("Generate SQL Query")
        output = gr.Textbox(label="🌬️🌁🌫️SqlCoder-2")
        submit.click(fn=lambda x: run_inference(x, model, tokenizer), inputs=question, outputs=output)

    demo.launch()

if __name__ == "__main__":
    main()