Pneumania Detection Web application build with Flask. The model's prediction accuracy is 93%. Model was trained on public available dataset: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia?
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You should clone this repo to your local machine by
git clone https://github.com/jenapss/Tensorflow-2.3-Pneumania-Detection-System -
After you cloned everything from this repo, please install all needed Python Packages by
pip3 install -r requirements.txt -
After you installed everything, go to root directory of this repo and run following command
python3 app.py
NOTE: After you execute command python3 app.py, you should see something similar to the following output:
`* Serving Flask app "app" (lazy loading)
- Environment: production WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.
- Debug mode: on
- Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)
- Restarting with stat 2021-02-14 20:14:02.999071: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations: AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2021-02-14 20:14:03.025437: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x7fcd9f03f9a0 initialized for platform Host (this does not guarantee that XLA will be used). Devices: 2021-02-14 20:14:03.025468: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version
- Debugger is active!
- Debugger PIN: 260-914-463`