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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline |
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import gradio as gr |
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import spaces |
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title = """# 🙋🏻♂️Welcome to 🌟Tonic's Defog 🌬️🌁🌫️SqlCoder-34B-Alpha |
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You can use this Space to test out the current model [defog/sqlcoder-34b-alpha](https://huggingface.co/defog/sqlcoder-34b-alpha). [defog/sqlcoder-34b-alpha](https://huggingface.co/defog/sqlcoder-34b-alpha) is a 34B parameter model that outperforms 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. SQLCoder-34B is fine-tuned on a base CodeLlama model. |
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You can also use 👨🏻⚕️❤️🩹🧑🏻⚕️Meditron by cloning this space. 🧬🔬🔍 Simply click here: <a style="display:inline-block" href="https://huggingface.co/spaces/Tonic/Meditron70B-AWQ?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> |
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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 🤗 |
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""" |
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class SQLQueryGenerator: |
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def __init__(self, model_name, prompt_file="prompt.md", metadata_file="metadata.sql"): |
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self.tokenizer, self.model = self.get_tokenizer_model(model_name) |
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self.prompt_file = prompt_file |
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self.metadata_file = metadata_file |
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def get_tokenizer_model(self, model_name): |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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trust_remote_code=True, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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use_cache=True, |
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) |
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return tokenizer, model |
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def generate_prompt(self, question): |
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with open(self.prompt_file, "r") as f: |
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prompt = f.read() |
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with open(self.metadata_file, "r") as f: |
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table_metadata_string = f.read() |
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prompt = prompt.format( |
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user_question=question, table_metadata_string=table_metadata_string |
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) |
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return prompt |
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def run_inference(self, question): |
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prompt = self.generate_prompt(question) |
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eos_token_id = self.tokenizer.eos_token_id |
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pipe = pipeline( |
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"text-generation", |
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model=self.model, |
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tokenizer=self.tokenizer, |
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max_new_tokens=300, |
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do_sample=False, |
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num_beams=5, |
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) |
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generated_query = ( |
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pipe( |
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prompt, |
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num_return_sequences=1, |
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eos_token_id=eos_token_id, |
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pad_token_id=eos_token_id, |
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)[0]["generated_text"] |
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.split("```sql")[-1] |
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.split("```")[0] |
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.split(";")[0] |
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.strip() |
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+ ";" |
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) |
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return generated_query |
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def main(): |
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model_name = "defog/sqlcoder-34b-alpha" |
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sql_query_generator = SQLQueryGenerator(model_name) |
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@spaces.GPU |
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def generate_sql(question): |
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return sql_query_generator.run_inference(question) |
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with gr.Blocks() as demo: |
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gr.Markdown(title) |
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question = gr.Textbox(label="Enter your question") |
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submit = gr.Button("Generate SQL Query") |
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output = gr.Textbox(label="🌬️🌁🌫️SqlCoder-34B-alpha") |
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submit.click(fn=generate_sql, inputs=question, outputs=output) |
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demo.launch() |
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if __name__ == "__main__": |
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main() |
