add support for an API to analyze and anonymize files #9
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l-r-sowmya
approved these changes
Jun 5, 2024
| try: | ||
| file = request.files['file'] | ||
| language = request.form['language'] | ||
| if file.filename == '': |
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- Need to include validation for checking only the uploaded file is csv or not?
- Also need to limit size for upload
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| analyzer_results_list = {} | ||
| for a in analyzer_results: | ||
| recognizer_results = [] | ||
| for r in a.recognizer_results: | ||
| recognizer_results.append([o.to_dict() for o in r]) | ||
| analyzer_results_list[a.key] = { | ||
| "value": a.value, | ||
| "recognizer_results": recognizer_results | ||
| } | ||
|
|
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Need to be moved inside service kind of file.
| f"A fatal error occurred during execution of " | ||
| f"AnalyzerEngine.analyze(). {e}" | ||
| ) | ||
| return jsonify(error=e.args[0]), 500 |
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will status code be always 500
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| for key, value in analyzer_results.items(): | ||
| recognizer_results = [] | ||
| for results_for_each_entry in value["recognizer_results"]: | ||
| each_entry_recognizer_results = [] | ||
| for r in results_for_each_entry: | ||
| each_entry_recognizer_results.append( | ||
| RecognizerResult(r["entity_type"], | ||
| r["start"], | ||
| r["end"], | ||
| r["score"])) | ||
| recognizer_results.append(each_entry_recognizer_results) |
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logic can be moved into respective domain file
| self.logger.setLevel(os.environ.get("LOG_LEVEL", self.logger.level)) | ||
| self.app = Flask(__name__) | ||
| self.logger.info("Starting analyzer engine") | ||
| nlp_engine = FlairNLPEngine(NLP_ENGINE) |
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We shld also think about how model loading can be done efficiently. For every request it shld not download it.
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