app.py

import streamlit as st
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch
import os
import time

def make_query(context, question):
    result_query = f'''You are a SQL expert with extensive experience, you need to create a query to answer the question.
    
### Database schema (PostgreSQL):
{context}
### Question:
{question}
### SQL Query: '''
    return result_query

st.set_page_config(page_title="SQL-to-Text with T5", page_icon="🚀")
st.title("SQL Query Generator with T5")

examples = [
    {
        "question": "Show all products with price greater than 100.",
        "description": "CREATE TABLE products (product_name VARCHAR, price INTEGER)"
    },
    {
        "question": "What is the average salary of employees in the Sales department?",
        "description": "CREATE TABLE employees (employee_name VARCHAR, department VARCHAR, salary INTEGER)"
    },
    {
        "question": "Which students have a GPA higher than 3.5?",
        "description": "CREATE TABLE students (student_id INTEGER, student_name VARCHAR, gpa FLOAT)"
    },
    {
        "question": "List all orders made by customer with ID 12345.",
        "description": "CREATE TABLE orders (order_id INTEGER, customer_id INTEGER, order_date DATE)"
    },
    {
        "question": "How many books were published after 2000?",
        "description": "CREATE TABLE books (book_title VARCHAR, author VARCHAR, publication_year INTEGER)"
    },
    {
        "question": "What is the total revenue from all completed transactions?",
        "description": "CREATE TABLE transactions (transaction_id INTEGER, amount FLOAT, status VARCHAR)"
    },
    {
        "question": "Which cities have a population between 1 million and 2 million?",
        "description": "CREATE TABLE cities (city_name VARCHAR, country VARCHAR, population INTEGER)"
    },
    {
        "question": "List all movies with rating higher than 8.0 released in 2020.",
        "description": "CREATE TABLE movies (movie_title VARCHAR, release_year INTEGER, rating FLOAT)"
    },
    {
        "question": "What is the most common job title in the company?",
        "description": "CREATE TABLE staff (employee_id INTEGER, job_title VARCHAR, department VARCHAR)"
    },
    {
        "question": "Which products are out of stock (quantity = 0)?",
        "description": "CREATE TABLE inventory (product_id INTEGER, product_name VARCHAR, quantity INTEGER)"
    }
]

@st.cache_resource
def load_model():
    script_dir = os.path.dirname(os.path.abspath(__file__))
    model_path = os.path.join(script_dir, "model")
    
    if not os.path.exists(model_path):
        raise FileNotFoundError(f"Model directory not found at {model_path}")
    
    try:
        tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-base")
        model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
        model.eval()
        return model, tokenizer
    except Exception as e:
        raise RuntimeError(f"Error loading model: {str(e)}")

try:
    model, tokenizer = load_model()
except Exception as e:
    st.error(f"Failed to load model: {str(e)}")
    st.stop()

if 'current_description' not in st.session_state:
    st.session_state.current_description = """CREATE TABLE table_name_28 (played INTEGER, points VARCHAR, position VARCHAR)"""
if 'current_question' not in st.session_state:
    st.session_state.current_question = "Which Played has a Points of 2, and a Position smaller than 8?"

def load_example(example):
    st.session_state.current_description = example["description"]
    st.session_state.current_question = example["question"]

st.subheader("Примеры:")
cols = st.columns(2)
for i, example in enumerate(examples):
    col = cols[i % 2]
    if col.button(
        f"Пример {i+1}: {example['question'][:30]}...",
        key=f"example_{i}",
    ):
        load_example(example)
        st.rerun()

with st.form("query_form"):
    description = st.text_area(
        "Описание таблицы (столбцы и их типы):",
        st.session_state.current_description,
        height=150,
        key="desc_input"
    )
    
    question = st.text_input(
        "Ваш вопрос:", 
        st.session_state.current_question,
        key="question_input"
    )
    
    submitted = st.form_submit_button("Сгенерировать запрос")

if submitted:
    if description and question:
        input_text = make_query(description, question)
        try:
            input_ids = tokenizer.encode(input_text, return_tensors="pt")
            
            animation_placeholder = st.empty()
            
            for frame in ["⠋", "⠙", "⠹", "⠸", "⠼", "⠴", "⠦", "⠧", "⠇", "⠏"]:
                animation_placeholder.markdown(f"`{frame}` Подготовка к генерации...")
                time.sleep(0.1)
            
            animation_placeholder.markdown("`⏳` Генерация SQL-запроса...")
            outputs = model.generate(
                input_ids,
                max_length=200,
                num_beams=5,
                top_p=0.95,
                early_stopping=True,
                pad_token_id=tokenizer.eos_token_id,
            )
            
            animation_placeholder.empty()

            generated_sql = tokenizer.decode(outputs[0], skip_special_tokens=True)
            st.subheader("Результат:")
            st.code(generated_sql, language="sql")
            
        except Exception as e:
            st.error(f"Ошибка при генерации: {str(e)}")
    else:
        st.warning("Пожалуйста, заполните описание таблицы и вопрос")