app.py

import os
import torch
import duckdb
import spaces
import gradio as gr
import pandas as pd
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig



# Height of the Tabs Text Area
TAB_LINES = 8
# Load Token
md_token = os.getenv('MD_TOKEN')

print('Connecting to DB...')
# Connect to DB
conn = duckdb.connect(f"md:my_db?motherduck_token={md_token}")

if torch.cuda.is_available():
    device = torch.device("cuda")
    print(f"Using GPU: {torch.cuda.get_device_name(device)}")
else:
    device = torch.device("cpu")
    print("Using CPU")

print('Loading Model...')

tokenizer = AutoTokenizer.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1")

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type= "nf4")

model = AutoModelForCausalLM.from_pretrained("motherduckdb/DuckDB-NSQL-7B-v0.1", quantization_config=quantization_config,
                                             device_map="auto", torch_dtype=torch.bfloat16)
print('Model Loaded...')
print(f'Model Device: {model.device}')

# Get Databases
def get_databases():
    databases = conn.execute("PRAGMA show_databases").fetchall()
    return [item[0] for item in databases] 

# Get Tables
def get_tables(database):
    conn.execute(f"USE {database}")
    tables = conn.execute("SHOW TABLES").fetchall()
    return [table[0] for table in tables]

# Update Tables
def update_tables(selected_db):
    tables = get_tables(selected_db)
    return gr.update(choices=tables)

# Get Schema
def get_schema(table):
    conn.execute(f"SELECT * FROM '{table}' LIMIT 1;")
    result = conn.sql(f"SELECT sql FROM duckdb_tables() where table_name ='{table}';").df()
    ddl_create = result.iloc[0,0]
    return ddl_create

# Get Prompt
def get_prompt(schema, query_input):
    text = f"""
    ### Instruction:
    Your task is to generate valid duckdb SQL query to answer the following question.
    ### Input:
    Here is the database schema that the SQL query will run on:
    {schema}
    
    ### Question:
    {query_input}
    ### Response (use duckdb shorthand if possible):
    """
    return text

# Generate SQL
@spaces.GPU(duration=120)
def text2sql(table, query_input):
    if table is None:
        return {
            table_schema: "",
            input_prompt: "",
            generated_query: "",
            result_output:pd.DataFrame([{"error": f"❌ Unable to get the SQL query based on the text. {e}"}])
        }

    schema = get_schema(table)
    print(f'Schema Generated...')
    prompt = get_prompt(schema, query_input)
    print(f'Prompt Generated...')
    try:
        print(f'Generating SQL... {model.device}')
        input_ids = tokenizer(prompt, return_tensors="pt").input_ids
        input_token_len = input_ids.shape[1]
        outputs = model.generate(input_ids.to(model.device), max_new_tokens=1024)
        result = tokenizer.decode(outputs[0][input_token_len:], skip_special_tokens=True)
        print('SQL Generated...')
    except Exception as e:
        return {
            table_schema: schema,
            input_prompt: prompt,
            generated_query: "",
            result_output:pd.DataFrame([{"error": f"❌ Unable to get the SQL query based on the text. {e}"}])
        }
    try:
        query_result = conn.sql(result).df()
        
    except Exception as e:
        return {
            table_schema: schema,
            input_prompt: prompt,
            generated_query: result,
            result_output:pd.DataFrame([{"error": f"❌ Unable to get the SQL query based on the text. {e}"}])
        }
        
    return {
        table_schema: schema,
        input_prompt: prompt,
        generated_query: result,
        result_output:query_result
    }

# Load Databases Names
databases = get_databases()

# Custom CSS styling
custom_css = """
.gradio-container {
    background-color: #f0f4f8;
}
.logo {
    max-width: 200px;
    margin: 20px auto;
    display: block;
}
.gr-button {
    background-color: #4a90e2 !important;
}
.gr-button:hover {
    background-color: #3a7bc8 !important;
}
"""

with gr.Blocks(theme=gr.themes.Soft(primary_hue="purple", secondary_hue="indigo"), css=custom_css) as demo:
    gr.Image("logo.png", label=None, show_label=False, container=False, height=100)
    
    gr.Markdown("""
    <div style='text-align: center;'>
    <strong style='font-size: 36px;'>Datajoi SQL Agent</strong>
    <br>
    <span style='font-size: 20px;'>Generate and Run SQL queries based on a given text for the dataset.</span>
    </div>
    """)

    with gr.Row():

        with gr.Column(scale=1, variant='panel'):
            database_dropdown = gr.Dropdown(choices=databases, label="Select Database", interactive=True)
            tables_dropdown = gr.Dropdown(choices=[], label="Available Tables", value=None)

        with gr.Column(scale=2):
            query_input = gr.Textbox(lines=5, label="Text Query", placeholder="Enter your text query here...")
            generate_query_button = gr.Button("Run Query", variant="primary")

            with gr.Tabs():
                with gr.Tab("Result"):
                    result_output = gr.DataFrame(label="Query Results", value=[], interactive=False)
                with gr.Tab("SQL Query"):
                    generated_query = gr.Textbox(lines=TAB_LINES, label="Generated SQL Query", value="", interactive=False)
                with gr.Tab("Prompt"):
                    input_prompt = gr.Textbox(lines=TAB_LINES, label="Input Prompt", value="", interactive=False)
                with gr.Tab("Schema"):
                    table_schema = gr.Textbox(lines=TAB_LINES, label="Schema", value="", interactive=False)
 
        database_dropdown.change(update_tables, inputs=database_dropdown, outputs=tables_dropdown)
        generate_query_button.click(text2sql, inputs=[tables_dropdown, query_input], outputs=[table_schema, input_prompt, generated_query, result_output])

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