S1056 - Doodstream -

app = Flask(__name__)

@app.route('/recommend', methods=['GET']) def recommend(): # Assume user provides a video ID and we fetch its features video_id = 0 # Example video ID query_features = video_features[video_id].reshape(1, -1) S1056 - DoodStream

from flask import Flask, jsonify from sklearn.neighbors import NearestNeighbors import numpy as np app = Flask(__name__) @app

nbrs = NearestNeighbors(n_neighbors=3, algorithm='brute', metric='euclidean').fit(video_features) distances, indices = nbrs.kneighbors(query_features) app = Flask(__name__) @app.route('/recommend'

# Return recommended video IDs return jsonify(indices[0].tolist())