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        "author": "ConvexHull Technology Private Limited",
        "author_email": "connect@accessai.co",
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        "classifiers": [
            "Intended Audience :: Developers",
            "License :: OSI Approved :: Apache Software License",
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            "Programming Language :: Python",
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        "description": "[![Build Status](https://travis-ci.org/accessai/access-niu.svg?branch=master)](https://travis-ci.org/accessai/access-niu)\n[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)\n[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)\n# access-niu\nThis repository contains application to train models for Image classification and Regression tasks.\n\n## Tasks\n- [x] Create a basic app for training and inference\n- [ ] Support for training regression models\n- [ ] Support of training multi input/output models\n- [ ] Incorporate Bayesian Inference for finding uncertainty in the predictions.\n- [ ] Create Docker Image\n- [ ] Support for serving the application with gunicorn\n- [ ] Anything else? \n\n## Installation\n```bash\npip install access-niu\n```\n\n## Training\n```bash\npython -m access_niu.train --template access_niu/sample/colors/sample_template.yml\n```\n\n## Inference\n```bash\npython -m access_niu.wsgi --project ./colors\n```\nNow use this curl command to parse\n```bash\ncurl -X POST \\\n  http://localhost:8000/parse \\\n  -F data=@image_leisure_0.jpg\n```\n\n\n## References\n- This project is inspired from [RASA-NLU](https://github.com/RasaHQ/rasa) project.",
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