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- Updated the content_extraction.ipynb notebook to use the new Azure AI Content Understanding SDK. - Replaced deprecated methods and adjusted the code for asynchronous operations. - Improved the structure of the notebook for better readability and organization. - Added a new sample_helper.py file containing utility functions for handling analysis results, saving images, and extracting operation IDs. - Enhanced error handling and logging throughout the notebook.
- Updated `.gitignore` to exclude `test_output/` directory. - Added new face images for enrollment and testing. - Refactored `build_person_directory.ipynb` to use async methods and improved logging. - Updated person and face management logic to handle Azure SDK changes. - Improved error handling and logging for face and person operations. - Enhanced `content_extraction.ipynb` with audio analysis capabilities and cleanup logic.
- Updated `analyzer_training.ipynb` to enhance client initialization and error handling. - Modified training data path handling and SAS URL generation for better clarity. - Improved analyzer creation process with unique ID generation and logging. - Enhanced document analysis with operation ID extraction and result retrieval. - Updated `build_person_directory.ipynb` to streamline face addition and identification processes. - Refined face association and disassociation logic for better clarity and functionality. - Improved person directory updates with clearer resource handling. - Updated `classifier.ipynb` to enhance classifier ID generation and result handling. - Improved operation ID extraction and result retrieval for classification tasks. - Refactored `content_extraction.ipynb` to standardize analyzer ID usage and improve file handling. - Enhanced audio and video analysis processes with clearer logging and data handling. - Updated kernel specifications across notebooks for consistency.
- Updated imports in management.ipynb to include new Azure SDK components. - Replaced token-based authentication with AzureKeyCredential in management.ipynb. - Enhanced analyzer creation process with detailed logging and error handling. - Improved analyzer listing and retrieval with async support in management.ipynb. - Refactored DocumentProcessor to remove hardcoded analyzer ID and improve file handling. - Added detailed docstring to _get_analyze_list method for better clarity. - Streamlined file processing logic to handle unsupported document types more gracefully.
…ling comments in field_extraction notebook
tools/test_notebooks/README.md
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| ```env | ||
| TRAINING_DATA_SAS_URL=<Blob container SAS URL> | ||
| TRAINING_DATA_PATH=<Designated folder path under the blob container> | ||
| training_data_sas_url=<Blob container SAS URL> |
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Sorry for the confusion of the comment in the previous PR: #100 (comment).
Using constant TRAINING_DATA_SAS_URL and TRAINING_DATA_PATH is right for setting up the variables in .env.
That comment was referring to that specific line. I mean we're using lower-case training_data_sas_url and training_data_path as variables within the analyzer_training notebook after getting the env variables.
…tebook for consistency
…ttps://github.com/Azure-Samples/azure-ai-content-understanding-python into changjian-wang/update-additional-samples-from-sdk
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