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+# RespireCheck-Classification-of-X-Ray-Using-Knowledge-Distillation-and-Semi-Supervised-Segmentation
+This web application leverages CLIP-KDViT and Grad-CAM to provide explainable chest X-ray diagnoses for COVID-19, pneumonia, and tuberculosis with high accuracy.
+Features
+
+AI-powered Diagnosis: Automatically classifies chest X-ray images to detect COVID-19, pneumonia, and tuberculosis.
+Explainable AI: Uses Grad-CAM for visualizing and understanding model predictions.
+High Accuracy: Achieves high diagnostic accuracy through advanced deep learning techniques.
+
+Requirements
+
+Before running the project locally, ensure you have the following:
+
+    Python 3.x (preferably Python 3.7 or higher)
+    Virtual Environment (venv)
+
+Steps to Deploy the Web Application
+1. Create a Virtual Environment:
+
+Create a new virtual environment in your local environment to isolate the dependencies required for this project.
+
+python -m venv venv
+
+2. Activate the Virtual Environment:
+
+    On Windows:
+
+        .\venv\Scripts\activate
+
+    On MacOS/Linux:
+
+        source venv/bin/activate
+
+3. Install Required Libraries:
+
+After activating the virtual environment, install all the necessary Python frameworks and dependencies from requirements.txt.
+
+pip install -r requirements.txt
+
+The requirements.txt file should include all the necessary dependencies for running the app, such as:
+
+    Flask
+    torch
+    torchvision
+    PIL
+    numpy
+    opencv-python
+    grad-cam
+    And any other specific libraries used in the project.
+
+4. Upload Model Files:
+
+Due to the size of the model.pth files, you need to upload them on your own. The pre-trained model files are required for making predictions but are too large to upload directly to the repository.
+
+Once you have the model.pth files, place them in the appropriate directory of the project (e.g., ./models/).
+
+5. Run the Web Application:
+
+Once the virtual environment is set up and the necessary model files are in place, you can run the web application.
+
+    python app.py
+
+This will start the web server, and you can access the application via:
+
+    http://127.0.0.1:5000
+
+Contributing
+
+Feel free to fork the repository and submit issues or pull requests. Contributions are welcome!