app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import sqlparse
# from modelscope import snapshot_download

# 加载模型和分词器
model_name = "defog/llama-3-sqlcoder-8b"  # 使用更新的模型以提高性能
# model_name = snapshot_download("stevie/llama-3-sqlcoder-8b")
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,
)

def generate_sql(user_question, instructions, create_table_statements):
    prompt = f"""<|begin_of_text|><|start_header_id|>user<|end_header_id|>

Generate a SQL query to answer this question: `{user_question}`
{instructions}

DDL statements:
{create_table_statements}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

The following SQL query best answers the question `{user_question}`:
```sql
"""
    
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
    generated_ids = model.generate(
        **inputs,
        num_return_sequences=1,
        eos_token_id=tokenizer.eos_token_id,
        pad_token_id=tokenizer.eos_token_id,
        max_new_tokens=400,
        do_sample=False,
        num_beams=1,
    )
    outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)

    torch.cuda.empty_cache()
    torch.cuda.synchronize()
    # empty cache so that you do generate more results w/o memory crashing
    # particularly important on Colab – memory management is much more straightforward
    # when running on an inference service
    return sqlparse.format(outputs[0].split("[SQL]")[-1], reindent=True)


question = f"What are our top 3 products by revenue in the New York region?"
instructions = f"""- if the question cannot be answered given the database schema, return "I do not know"
- recall that the current date in YYYY-MM-DD format is 2024-09-15
"""
schema = f"""CREATE TABLE products (
  product_id INTEGER PRIMARY KEY, -- Unique ID for each product
  name VARCHAR(50), -- Name of the product
  price DECIMAL(10,2), -- Price of each unit of the product
  quantity INTEGER  -- Current quantity in stock
);

CREATE TABLE customers (
  customer_id INTEGER PRIMARY KEY, -- Unique ID for each customer
  name VARCHAR(50), -- Name of the customer
  address VARCHAR(100) -- Mailing address of the customer
);

CREATE TABLE salespeople (
  salesperson_id INTEGER PRIMARY KEY, -- Unique ID for each salesperson 
  name VARCHAR(50), -- Name of the salesperson
  region VARCHAR(50) -- Geographic sales region 
);

CREATE TABLE sales (
  sale_id INTEGER PRIMARY KEY, -- Unique ID for each sale
  product_id INTEGER, -- ID of product sold
  customer_id INTEGER,  -- ID of customer who made purchase
  salesperson_id INTEGER, -- ID of salesperson who made the sale
  sale_date DATE, -- Date the sale occurred 
  quantity INTEGER -- Quantity of product sold
);

CREATE TABLE product_suppliers (
  supplier_id INTEGER PRIMARY KEY, -- Unique ID for each supplier
  product_id INTEGER, -- Product ID supplied
  supply_price DECIMAL(10,2) -- Unit price charged by supplier
);

-- sales.product_id can be joined with products.product_id
-- sales.customer_id can be joined with customers.customer_id 
-- sales.salesperson_id can be joined with salespeople.salesperson_id
-- product_suppliers.product_id can be joined with products.product_id
"""

demo = gr.Interface(
    fn=generate_sql,
    title="SQLCoder-8b",
    description="Defog's SQLCoder-8B is a state of the art-models for generating SQL queries from natural language. ",
    inputs=[
        gr.Textbox(label="User Question", placeholder="Enter your question here...", value=question),
        gr.Textbox(label="Instructions (optional)", placeholder="Enter any additional instructions here...", value=instructions),
        gr.Textbox(label="Create Table Statements", placeholder="Enter DDL statements here...", value=schema),
    ],
    outputs="text",
)


if __name__ == "__main__":
    demo.launch(share=True)