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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 👻[](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() |