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)
from flask import Flask, jsonify from sklearn.neighbors import NearestNeighbors import numpy as np
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.array([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ])
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.
# Return recommended video IDs return jsonify(indices[0].tolist())
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'
# Example in-memory video features video_features = np.array([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ])
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.
# Return recommended video IDs return jsonify(indices[0].tolist())