import os from dotenv import load_dotenv import gradio as gr from sqlalchemy import text from smolagents import tool, CodeAgent, HfApiModel import spaces # Import database from database import engine, receipts # Load Hugging Face API token load_dotenv(override=True) hf_token = os.getenv("HF_TOKEN") if not hf_token: raise ValueError("HF_TOKEN environment variable is not set.") @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", token=hf_token), ) 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)