app.py

import os
import gradio as gr
from sqlalchemy import text
from smolagents import tool, CodeAgent, HfApiModel
import spaces
import pandas as pd
from database import engine, receipts

# Fetch all data from the 'receipts' table
def get_receipts_table():
    """
    Fetch all rows from the receipts table and return as a Pandas DataFrame.
    """
    try:
        with engine.connect() as con:
            result = con.execute(text("SELECT * FROM receipts"))
            rows = result.fetchall()
        
        if not rows:
            return pd.DataFrame(columns=["receipt_id", "customer_name", "price", "tip"])

        return pd.DataFrame(rows, columns=["receipt_id", "customer_name", "price", "tip"])
    except Exception as e:
        return pd.DataFrame({"Error": [str(e)]})

@tool
def sql_engine(query: str) -> str:
    """
    Executes an SQL query on the database and returns the result.

    Args:
        query (str): The SQL query to execute.
    
    Returns:
        str: Query result as a formatted string.
    """
    try:
        with engine.connect() as con:
            rows = con.execute(text(query)).fetchall()
        
        if not rows:
            return "No results found."
        
        if len(rows) == 1 and len(rows[0]) == 1:
            return str(rows[0][0])
        
        return "\n".join([", ".join(map(str, row)) for row in rows])
    except Exception as e:
        return f"Error: {str(e)}"

def query_sql(user_query: str) -> str:
    schema_info = (
        "The database has a table named 'receipts' with the following schema:\n"
        "- receipt_id (INTEGER, primary key)\n"
        "- customer_name (VARCHAR(16))\n"
        "- price (FLOAT)\n"
        "- tip (FLOAT)\n"
        "Generate a valid SQL SELECT query using ONLY these column names."
        "DO NOT explain your reasoning, and DO NOT return anything other than the SQL query itself."
    )
    
    generated_sql = agent.run(f"{schema_info} Convert this request into SQL: {user_query}")
    
    if not isinstance(generated_sql, str):
        return f"{generated_sql}"
    
    if not generated_sql.strip().lower().startswith(("select", "show", "pragma")):
        return "Error: Only SELECT queries are allowed."
    
    result = sql_engine(generated_sql)
    
    try:
        float_result = float(result)
        return f"{float_result:.2f}"
    except ValueError:
        return result

def handle_query(user_input: str) -> str:
    return query_sql(user_input)

agent = CodeAgent(
    tools=[sql_engine],
    model=HfApiModel(model_id="Qwen/Qwen2.5-Coder-32B-Instruct"),
)

with gr.Blocks() as demo:
    gr.Markdown("""
## Plain Text Query Interface

This tool allows you to query a receipts database using natural language. Simply type your question into the input box, press **Run**, and the tool will generate and execute an SQL query to retrieve relevant data. The results will be displayed in the output box.

### Usage:
1. Enter a question related to the receipts data in the text box.
2. Click **Run** to execute the query.
3. The result will be displayed in the output box.

> The current receipts table is also displayed for reference.
""")

    with gr.Row():
        with gr.Column(scale=1):
            user_input = gr.Textbox(label="Ask a question about the data")
            run_button = gr.Button("Run", variant="primary")  # Purple button
            query_output = gr.Textbox(label="Result")
        
        with gr.Column(scale=2):
            gr.Markdown("### Receipts Table")
            receipts_table = gr.Dataframe(value=get_receipts_table(), label="Receipts Table")

    run_button.click(fn=handle_query, inputs=user_input, outputs=query_output)  # Trigger only on button press
    demo.load(fn=get_receipts_table, outputs=receipts_table)

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)