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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 results.
Table Schema:
- receipt_id: INTEGER
- customer_name: VARCHAR(16)
- price: FLOAT
- tip: FLOAT
Args:
query: The SQL query to execute.
Returns:
Query result as a string.
"""
output = ""
try:
with engine.connect() as con:
rows = con.execute(text(query))
for row in rows:
output += "\n" + str(row)
except Exception as e:
output = f"Error: {str(e)}"
return output.strip()
# 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"),
)
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.
"""
# Generate SQL from natural language
generated_sql = agent.run(f"Convert this request into SQL: {user_query}")
# Execute the SQL query and return the result
return sql_engine(generated_sql)
# Define Gradio interface
demo = gr.Interface(
fn=query_sql,
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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