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RECOMMENDER SYSTEM

This is a movie recommender system that uses your current list of watched movies to recommend other titles you may be interested in. It is currently using content based recommender system.

Virtual Environment Setup

conda create --no-default-packages -n <env_name>
conda activate <env_name>
conda install python=3.9

Install Packages

pip install -r requirements.txt

Create config.py in main directory

Follow the instructions here to generate your own api key:
https://developers.themoviedb.org/3/getting-started/introduction

tmdb_api_key = '<api_key>'

Additional Note: Import category.py in main.py and adjust accordingly if you were to run this locally because this code uses github secrets to add env variable to docker image

Run fastAPI using uvicorn

uvicorn app.api:app --host localhost --port 8000 --reload --reload-dir .

Run Movie Recommendation using python

The list of movies for a user needs to be the tmdb_id for a specific movie. It is made this way to prevent any problems caused by the case of a string (Ex. The Dark Knight != the dark knight).

import requests
import json
payload = {
            "user_id": 0,
            "movie_list": [76600, 267805, 315162, 436270, 505642, 536554, 587092, 631842, 640146, 646389, 653851, 758009, 785084, 823999, 842544, 842942, 843794, 1058949]
}
response = requests.post(url='http://localhost:8000/predict',json=payload)
output_ = json.loads(response.text)

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