4b9fa0579e
In order to compare the (not yet implemented) SQL query generated by the LLM with an actual query, another text field was added that parses the query to `pyodbc`, which connects to our database, stores the resulting rows in a `pandas` dataframe and then visualizes it as a table in plotly dash. The SQL functionalities are implemented in the `sql_utils.py` module. Additionally, some minor updates to the overall behavior and layout of the app were implemented.
66 lines
2.0 KiB
Python
66 lines
2.0 KiB
Python
import os
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from typing import Union
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import pandas as pd
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import pyodbc
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def test_db_connection() -> bool:
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"""Test the connection to Azure SQL Database.
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This function attempts to establish a connection to an Azure SQL Database
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using the connection string stored in the environment variable
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'AZURE_SQL_CONNECTION_STRING'. It makes up to 5 attempts to connect,
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with a timeout of 240 seconds for each attempt.
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Returns
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-------
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bool
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True if the connection was successful, False otherwise.
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"""
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connection_string = os.environ.get("AZURE_SQL_CONNECTION_STRING")
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for i in range(5):
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print(f"Trying to connect to Azure SQL Database... Attempt {i + 1}")
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try:
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pyodbc.connect(connection_string, timeout=240)
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print("Connected to Azure SQL Database successfully!")
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connected = True
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break
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except pyodbc.Error as e:
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print(f"Error connecting to Azure SQL Database: {e}")
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connected = False
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return connected
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def execute_query(query: str) -> Union[pd.DataFrame, str]:
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"""Execute a SQL query on an Azure SQL Database and return the results.
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This function connects to an Azure SQL Database using the connection string
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stored in the environment variable 'AZURE_SQL_CONNECTION_STRING', executes
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the provided SQL query, and returns the results as a pandas DataFrame.
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Parameters
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----------
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query : str
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The SQL query to execute.
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Returns
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-------
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Union[pd.DataFrame, str]
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A pandas DataFrame containing the query results if successful,
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or a string containing the error message if an exception occurs.
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"""
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try:
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connection_string = os.environ.get("AZURE_SQL_CONNECTION_STRING")
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conn = pyodbc.connect(connection_string, timeout=240)
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df = pd.read_sql(query, conn)
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conn.close()
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return df
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except Exception as e:
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return str(e)
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finally:
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if conn in locals():
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conn.close()
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