- Doodstream: S1056

nbrs = NearestNeighbors(n_neighbors=3, algorithm='brute', metric='euclidean').fit(video_features) distances, indices = nbrs.kneighbors(query_features)

from flask import Flask, jsonify from sklearn.neighbors import NearestNeighbors import numpy as np S1056 - DoodStream

# Return recommended video IDs return jsonify(indices[0].tolist()) nbrs = NearestNeighbors(n_neighbors=3

app = Flask(__name__)

if __name__ == '__main__': app.run(debug=True) This example would need significant expansion and integration with a real database and user interaction system but illustrates a basic approach to developing a feature for DoodStream like S1056. S1056 - DoodStream

@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)