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Movie recommender app8/7/2023 MOVIE_FIELDS = ["movieId", "id", "imdb_id", "original_title", "title", "overview", The movies_df and candidates variables store the movie database and the embeddings, respectively. The function setup_movie_database defines the fields, cleans the data by removing null values, and then fills in empty fields, adds a movie ID field, and returns the movie database and the embeddings representing movie descriptions. The code below builds the movie database. You’ll see how to embed a movie description later on. This file also includes pre-calculated embeddings of each movie's description. You can get this data from a variety of sources, such as the Internet Movie Database (IMDB), but this tutorial will use a premade JSON file (the_movies_with_embeddings.json), which already contains information from nearly 45,000 movies. To begin, you’ll build a movie database containing fields for the title, description, original language, and more. Keep this information private! Step 1: Gather Movie Data To get the API key, log in to the Cohere dashboard and navigate to the API Keys section. env file at the root of your directory to hold your Cohere API key. You can use the Makefile to create a virtual environment with the required libraries and dependencies using the make command or just install the necessary dependencies by running the pip install -r requirements.txt command either in a notebook or in your local environment. The Makefile builds a virtual environment for the project.The data/the_movies_with_embeddings.json file contains a movie dataset and precomputed embeddings of each movie’s description.The requirements.txt file lists the project dependencies.The utils.py file provides some utilities.The movies.py file contains the code of the movie recommender.The project code on GitHub contains the following files: To complete this project, register for an account with Cohere and generate your API key to access the Cohere API’s resources. You’ll also be able to filter movies by language and choose how many to display in an interactive and user-friendly interface. You’ll build an app that takes movie descriptions in any language and recommends similar movies via Cohere’s multilingual models. Step 7: Setup and run Building a Movie Recommendation App Step 2: Build a user interface to get movie descriptions This is made possible by leveraging Cohere's multilingual models, which enable the embedding of movie descriptions into language-invariant representations.įollow the steps below to build your movie recommendation app: What's more, the app is capable of performing multilingual searches, allowing you to describe your ideal movie in various languages. Once completed, you'll be able to simply describe the type of movie you'd like to watch, and the app will provide suggestions tailored to your preferences. In this article, we'll walk you through the process of building your own movie recommendation app, built by Cohere's Machine Learning Engineer, Amr Kayid. Have you ever found yourself spending way too much time browsing through a streaming platform to find the perfect movie? We've all been there, scrolling through countless titles and descriptions, trying to pinpoint something that truly matches our interests.
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