import re, torch, gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "kirankotha/mistral7b-sql-model"

tok = AutoTokenizer.from_pretrained(model_id)
mdl = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")

if tok.pad_token is None:
    tok.pad_token = tok.eos_token

def gen_sql(question, context):
    prompt = f"You are a text-to-SQL model.\n### Input:\n{question}\n### Context:\n{context}\n### Response:\n"
    ids = tok(prompt, return_tensors="pt").to(mdl.device)
    with torch.no_grad():
        out = mdl.generate(
            **ids,
            max_new_tokens=128,
            do_sample=False,
            pad_token_id=tok.pad_token_id
        )
    text = tok.decode(out[0], skip_special_tokens=True)
    m = re.findall(r"```(.*?)```", text, flags=re.DOTALL)
    return m[-1].strip() if m else text.strip()

demo = gr.Interface(
    fn=gen_sql,
    inputs=["text","text"],
    outputs="text",
    title="Mistral-7B Text-to-SQL Demo",
    description="Fine-tuned Mistral-7B model for text-to-SQL generation.",
    examples=[["Which product has the highest price?",
               "CREATE TABLE products (id INTEGER, name TEXT, price REAL)"]]
)

demo.launch()