Merge pull request #95 from Aydinhamedi/main

merge

.github/workflows/python-app.yml

@@ -27,7 +27,9 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install flake8 pytest
-        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+        if [ -f requirements.txt ]; then 
+          grep -v 'gpu-control' requirements.txt | xargs pip install
+        fi
     - name: Lint with flake8
       run: |
         # stop the build if there are Python syntax errors or undefined names

README.md

@@ -3,7 +3,8 @@
 
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![CodeQL](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/codeql.yml/badge.svg?branch=main)](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/codeql.yml)
-
+[![Dependency Review](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/dependency-review.yml/badge.svg)](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/dependency-review.yml)\
+[![Python application](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/python-app.yml/badge.svg)](https://github.com/Aydinhamedi/Pneumonia-Detection-Ai/actions/workflows/python-app.yml)
 ### This project uses a deep learning model built with the TensorFlow framework to detect pneumonia in X-ray images. The model architecture is based on the EfficientNetB7 model, which has achieved an accuracy of approximately 97.12% (97.11538%) on our test data. This high accuracy rate is one of the strengths of our AI model.
 > [!IMPORTANT]
 > The code that have achived the highest acc is `backup/V6/Model_T&T.ipynb`.\