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import os
import gradio as gr
from sqlalchemy import text
from smolagents import tool, CodeAgent, HfApiModel
import spaces
# Import the persistent database
from database import engine, receipts
@tool
def sql_engine(query: str) -> str:
"""
Executes an SQL query on the 'receipts' table and returns formatted results.
Args:
query: The SQL query to execute.
Returns:
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 query returns a single value (e.g., AVG, SUM, COUNT), return as a string
if len(rows) == 1 and len(rows[0]) == 1:
return str(rows[0][0]) # Convert numerical result to string
# Convert query results into a clean, readable format
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:
"""
Converts natural language input to an SQL query using CodeAgent
and returns the execution results.
Args:
user_query: The user's request in natural language.
Returns:
The query result from the database as a formatted string.
"""
# Provide the AI with the correct schema and strict instructions
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.\n"
"DO NOT explain your reasoning, and DO NOT return anything other than the SQL query itself."
)
# Generate SQL query using the provided schema
generated_sql = agent.run(f"{schema_info} Convert this request into SQL: {user_query}")
# Log the generated SQL for debugging
print(f"Generated SQL: {generated_sql}")
# Ensure we only execute valid SELECT queries
if not generated_sql.strip().lower().startswith(("select", "show", "pragma")):
return "Error: Only SELECT queries are allowed."
# Execute the SQL query and return the result
result = sql_engine(generated_sql)
# Log the SQL query result
print(f"SQL Query Result: {result}")
try:
float_result = float(result)
return f"{float_result:.2f}"
except ValueError:
return result
def handle_query(user_input: str) -> str:
"""
Calls query_sql, captures the output, and directly returns it to the UI.
Args:
user_input: The user's natural language question.
Returns:
The SQL query result as a plain string to be displayed in the UI.
"""
return query_sql(user_input) # Directly return the processed result
# Initialize CodeAgent to generate SQL queries from natural language
agent = CodeAgent(
tools=[sql_engine], # Ensure sql_engine is properly registered
model=HfApiModel(model_id="Qwen/Qwen2.5-Coder-32B-Instruct"),
)
# Define Gradio interface using handle_query instead of query_sql
demo = gr.Interface(
fn=handle_query, # Call handle_query to return the final SQL output
inputs=gr.Textbox(label="Enter your query in plain English"),
outputs=gr.Textbox(label="Query Result"),
title="Natural Language to SQL Executor",
description="Enter a plain English request, and the AI will generate an SQL query and return the results.",
flagging_mode="never",
)
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860, share=True)
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