S1056 - Doodstream Apr 2026

# Example in-memory video features video_features = np.array([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ])

# Return recommended video IDs return jsonify(indices[0].tolist()) S1056 - DoodStream

nbrs = NearestNeighbors(n_neighbors=3, algorithm='brute', metric='euclidean').fit(video_features) distances, indices = nbrs.kneighbors(query_features) # Example in-memory video features video_features = np

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

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