API Implementation Enhancement Suggestions

from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score # Assuming I have a DataFrame with document types and features df = pd.read_csv('documents.csv') # Split data into training and testing sets X_…

report = RiskReport(report_data=report_data) db.session.add(report) db.session.commit() return jsonify({"message": "Report created successfully"}), 201 if __name__ == "__main__": app.run(debug=True) ``` …

return jsonify({'error': 'Payload exceeds 5KB limit'}), 400 # Perform the search query # TODO: Implement the actual search logic here search_result = {} return jsonify(search_result) if __name__ == '__main…

# Extract synonyms for each token synonyms = [] for token in tokens: # Use WordNet to get synonyms synsets = nltk.corpus.wordnet.synsets(token) for synset in synsets: for lemma in synset.lemma…

synonym_expansion >> Edge(label="Synonyms") >> rewriting # Add a Kafka queue for message passing kafka_queue = Kafka("Kafka Queue") tokenization >> Edge(label="Tokens") >> kafka_queue kafka_queue >> Edge(label="Toke…

return rewritten_query except Exception as e: # Log the error logging.error(f"Error parsing query: {query}") raise ``` Can someone review my logging code and make sure I'm doing it correctly? ->-> 1,1 [T…