Create app.py

app.py

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+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+import gradio as gr
+import spaces
+
+title = """# 🙋🏻‍♂️Welcome to 🌟Tonic's Defog 🌬️🌁🌫️SqlCoder-34B-Alpha 
+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.
+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> 
+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 🤗
+"""
+
+@spaces.GPU
+class SQLQueryGenerator:
+    def __init__(self, model_name, prompt_file="prompt.md", metadata_file="metadata.sql"):
+        self.tokenizer, self.model = self.get_tokenizer_model(model_name)
+        self.prompt_file = prompt_file
+        self.metadata_file = metadata_file
+
+    def get_tokenizer_model(self, 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(self, question):
+        with open(self.prompt_file, "r") as f:
+            prompt = f.read()
+
+        with open(self.metadata_file, "r") as f:
+            table_metadata_string = f.read()
+
+        prompt = prompt.format(
+            user_question=question, table_metadata_string=table_metadata_string
+        )
+        return prompt
+
+    def run_inference(self, question):
+        prompt = self.generate_prompt(question)
+        eos_token_id = self.tokenizer.eos_token_id
+        pipe = pipeline(
+            "text-generation",
+            model=self.model,
+            tokenizer=self.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/sqlcoder-34b-alpha"
+    sql_query_generator = SQLQueryGenerator(model_name)
+
+    def generate_sql(question):
+        return sql_query_generator.run_inference(question)
+
+    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-34B-alpha")
+        submit.click(fn=generate_sql, inputs=question, outputs=output)
+
+    demo.launch()
+
+if __name__ == "__main__":
+    main()