app = Flask(__name__)
# Example in-memory video features video_features = np.array([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ]) S1056 - DoodStream
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. app = Flask(__name__) # Example in-memory video features
nbrs = NearestNeighbors(n_neighbors=3, algorithm='brute', metric='euclidean').fit(video_features) distances, indices = nbrs.kneighbors(query_features) S1056 - DoodStream
from flask import Flask, jsonify from sklearn.neighbors import NearestNeighbors import numpy as np
# Return recommended video IDs return jsonify(indices[0].tolist())