app.py

import warnings
warnings.filterwarnings("ignore")

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

torch.set_num_threads(1)

# ─────────────────────
# MODEL
# ─────────────────────
BASE_MODEL = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"

print("Loading model...")

model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    device_map="cpu",
    torch_dtype=torch.float32,
    low_cpu_mem_usage=True,
)

tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)

model.eval()
print("Model ready")

# ─────────────────────
# GENERATION
# ─────────────────────
def generate_sql(question):
    if not question.strip():
        return "Enter SQL question."

    prompt = f"""
You are a SQL expert.
Convert the user request into SQL query only.

User: {question}
SQL:
"""

    inputs = tokenizer(prompt, return_tensors="pt")

    with torch.no_grad():
        output = model.generate(
            **inputs,
            max_new_tokens=120,
            temperature=0.2,
            do_sample=False,
            pad_token_id=tokenizer.eos_token_id,
        )

    text = tokenizer.decode(output[0], skip_special_tokens=True)

    return text.split("SQL:")[-1].strip()

# ─────────────────────
# UI
# ─────────────────────
demo = gr.Interface(
    fn=generate_sql,
    inputs=gr.Textbox(lines=3, label="SQL Question"),
    outputs=gr.Textbox(lines=8, label="Generated SQL"),
    title="SQL Generator (Portfolio Demo)",
    description="Fast CPU model for portfolio demo.",
    examples=[
        ["Find duplicate emails in users table"],
        ["Top 5 highest paid employees"],
        ["Orders per customer last month"],
    ],
)

demo.launch(server_name="0.0.0.0")