62 lines
1.8 KiB
Python
62 lines
1.8 KiB
Python
"""
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!pip install scikit-learn
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!pip install pandas
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!pip install fastapi
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!pip install "uvicorn[standard]"
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!uvicorn main:app --reload
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"""
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import numpy as np
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import pandas as pd
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from fastapi import FastAPI
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from scipy.spatial.distance import cosine
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from sklearn.preprocessing import MultiLabelBinarizer
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from engine import FS
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app = FastAPI()
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data = pd.DataFrame()
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@app.on_event('startup')
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async def startup_event():
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global data
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data = pd.read_csv('processed_data.csv', index_col='id', converters={'genres': pd.eval})
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all_genres = data.genres.explode().unique()
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mlb = MultiLabelBinarizer()
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mlb.fit([all_genres])
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data['genres'] = data['genres'].apply(lambda x: mlb.transform([x])[0])
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data['emotions'] = data[['Happy', 'Angry', 'Surprise', 'Sad', 'Fear']].values.tolist()
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@app.get('/score/{first_id}/{second_id}')
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def rec_score(first_id: str, second_id: str):
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try:
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first = data.loc[first_id]
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except KeyError:
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return {'error': f'{first_id} is not a valid id'}
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try:
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second = data.loc[second_id]
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except KeyError:
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return {'error': f'{second_id} is not a valid id'}
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year_diff = int(first['release_year'] - second['release_year'])
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FS.set_variable('RELEASE_YEAR', year_diff)
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runtime_diff = int(first['runtime'] - second['runtime'])
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FS.set_variable('RUNTIME', runtime_diff)
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if not (np.isnan(first['seasons']) or np.isnan(second['seasons'])):
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season_diff = int(first['seasons'] - second['seasons'])
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FS.set_variable('SEASONS', season_diff)
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else:
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FS.set_variable('SEASONS', 0)
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genre_diff = 1 - cosine(first['genres'], second['genres'])
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FS.set_variable('GENRES', genre_diff)
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emotion_diff = 1 - cosine(first['emotions'], second['emotions'])
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FS.set_variable('EMOTIONS', emotion_diff)
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return FS.inference(['RECOMMENDATION'])
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