| import gradio as gr |
| import torch |
| from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline |
| from peft import PeftModel |
|
|
| BASE_MODEL = "codellama/CodeLlama-7b-Instruct-hf" |
| ADAPTER_ID = "tamilanda/my-sql-model" |
|
|
| |
| tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) |
|
|
| |
| dtype = torch.float16 if torch.cuda.is_available() else torch.float32 |
| device = 0 if torch.cuda.is_available() else -1 |
|
|
| model = AutoModelForCausalLM.from_pretrained( |
| BASE_MODEL, |
| torch_dtype=dtype, |
| low_cpu_mem_usage=False, |
| device_map=None |
| ) |
|
|
| |
| model = PeftModel.from_pretrained(model, ADAPTER_ID) |
|
|
| |
| if torch.cuda.is_available(): |
| model = model.to("cuda") |
| else: |
| model = model.to("cpu") |
|
|
| |
| pipe = pipeline( |
| "text-generation", |
| model=model, |
| tokenizer=tokenizer, |
| device=device |
| ) |
|
|
| |
| def generate_sql(prompt): |
| if not prompt.strip(): |
| return "⚠️ Please enter a prompt." |
| result = pipe( |
| prompt, |
| max_new_tokens=150, |
| do_sample=True, |
| temperature=0.2, |
| top_p=0.9, |
| ) |
| return result[0]["generated_text"] |
|
|
| demo = gr.Interface( |
| fn=generate_sql, |
| inputs=gr.Textbox(label="Enter Natural Language Query"), |
| outputs=gr.Textbox(label="Generated SQL"), |
| title="🧠 NL → SQL (CodeLlama + LoRA)", |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
|
|
