This project is an exploration of how to use LLMs to make searching for movie recommendations easier and more natural.
- Scrape the all plot summaries from IMDB for each movie in the IM_S_DB with the following. It will save
movie_summaries.csv
todata/
.python get_movie_plot_outlines.py.py
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This project was built in Python 3.11.4. We recommend using Pyenv to manage Python installations.
-
We use Poetry to manage dependencies, but we have included a
requirements.txt
file as well. Ask poetry to output arequirements.txt
file for you with:poetry export --without-hashes --format=requirements.txt > requirements.txt
or, to specifically only export the ones required for the demo app, run:
poetry export --without-hashes --only app --format=requirements.txt > requirements.txt
-
I have added all scraper dependencies with:
poetry add --group scraper [package-name]
This will let us keep the dependencies separate and organized, as the scraper may only be needed temporarily.
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You can use Jupyter Notebooks with Poetry by creating a kernel like so:
poetry run python -m ipykernel install --user --name [project-name]
Start your notebooks with
poetry run jupyter lab
and select your newly created kernel.