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.
Includes the first version of a rudimentary chat app, still without the
SQL capabilities that we want later. For now, we can connect to the
Azure OpenAI source and then have the response displayed in a plotly
dash webapp.
Some styling and UI elements were also added, such as logos. UI
components are designed that the user cannot enter the same query twice
and cannot click the submit button as long as the query is running.
Setting up python project files using poetry. A basic environment
is installed including dash for the app that will be implemented
later.
Also contains several dev tools, including pre-commit hooks.
Co-authored-by: Tobias Quadfasel <tobias.loesche@studium.uni-hamburg.de>
Reviewed-on: #1
