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README.md

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-## Motivation
+---
+title: {{title}}
+emoji: {{emoji}}
+colorFrom: {{colorFrom}}
+colorTo: {{colorTo}}
+sdk: {{sdk}}
+app_file: app.py
+pinned: false
+---
 
-Pneumonia is a lung infection (🫁) that inflames the air sacs in one or both lungs. This infection arises when the air sacs get filled with fluid or pus (purulent material). It can be a bacterial or viral infection. The main symptoms are - cough with phlegm or pus, fever, chills, and breathing difficulty. 
+# Configuration
 
-This disease is responsible for over 15% of all deaths of children under five years old worldwide. This proves the severity of this disease and the need for accurate detection. 
+`title`: Pneumonia Detection
 
-The most commonly used method to diagnose pneumonia is through chest radiograph or chest X-ray, which depicts the infection as an increased opacity in the lungs' specific area(s).
+`sdk`: gradio
 
-To increase the diagnosis procedure's efficacy and reach, we can leverage machine learning algorithms to identify abnormalities in the chest X-ray images. In this model, many chest X-ray images (both normal and pneumonia) are fed to build `Convolutional Neural Network (CNN)` model for fulfilling the purpose. 
+`app_file`: app.py
 
-
-## Requirements
-
-- Python 3.7.x
-- Tensorflow 2.4.1+
-- Keras 2.4.3+
-- scikit-learn 0.24.1+
-- matplotlib 3.3.3+
-- texttable 1.6.3+
-- gradio 1.5.3+
-
-## Dataset
-
-You can download the dataset from [kaggle](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia/). Use the underlying download link to download the dataset.
-
-### Instructions to follow
-
-* Extract the archive
-* You will find several directories in it
-* Copy the `chest-xray` directory contents (`train`, `test` and `val` subdirectories) to the `data` folder
-
-The number of images belonging to both classes (`Normal` and `Pneumonia`) in the `train`, `test` and `val` datasets are -
-
-<img width="326" alt="Screenshot 2021-02-07 at 16 40 00" src="https://user-images.githubusercontent.com/76659596/107151515-4083f280-6963-11eb-84c7-f2a23cc24134.png">
-
-
-## Installation
-
-* Clone the repository 
-
-`git clone https://github.com/baishalidutta/Pneumonia-Detection.git`
-
-* Install the required libraries
-
-`pip3 install -r requirements.txt`
-
-## Usage
-
-Enter into the `source` directory to execute the following source codes.
-
-* To generate the model on your own, run
-
-`python3 cnn_training_model.py` 
-
-* To evaluate any dataset using the pre-trained model (in the `model` directory), run
-
-`python3 cnn_model_evaluation.py`
-
-Note that, for evaluation, `cnn_model_evaluation.py` will use all the images contained inside both `test` and `val` subdirectories (inside `data` directory).
-
-Alternatively, you can find the whole analysis in the notebook inside the `notebook` directory. To open the notebook, use either `jupyter notebook` or `google colab` or any other IDE that supports notebook feature such as `PyCharm Professional`.
-
-## Evaluation 
-
-Our model is trained with 96% accuracy on the training dataset. The model's accuracy on the `test` and `val` datasets are 91% and 88% respectively. In both cases, the `f1-score` and `ROC_AUC Score` are relatively high, as shown below. 
-
-### On Test Dataset (624 images, 234 `Normal` and 390 `Pneumonia`)
-
-<p align="center">
-<img width="960" alt="Screenshot 2021-02-07 at 17 07 23" src="https://user-images.githubusercontent.com/76659596/107152321-93f83f80-6967-11eb-95b4-0bfb3ccae6d7.png">
-</p>
-
-### On Validation Dataset (16 images, 8 `Normal` and 8 `Pneumonia`)
-
-<p align="center">
-<img width="960" alt="Screenshot 2021-02-07 at 17 10 07" src="https://user-images.githubusercontent.com/76659596/107152360-ba1ddf80-6967-11eb-90cb-dfaeca31f275.png">
-</p>
-
-## Web Application
-
-To run the web application locally, go to the `webapp` directory and execute:
-
-`python3 web_app.py`
-
-This will start a local server that you can access in your browser. You can either upload/drag a new X-ray image or select any test X-ray images from the examples below.
-
-You can, alternatively, try out the hosted web application [here](https://gradio.app/g/baishalidutta/Pneumonia-Detection).
-
-## Developer
-
-Baishali Dutta (<a href='mailto:[email protected]'>[email protected]</a>)
-
-## Contribution [![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/baishalidutta/Pneumonia-Detection/issues)
-
-If you would like to contribute and improve the model further, check out the [Contribution Guide](https://github.com/baishalidutta/Pneumonia-Detection/blob/main/CONTRIBUTING.md)
-
-## License [![License](http://img.shields.io/badge/license-Apache-blue.svg)](https://www.apache.org/licenses/LICENSE-2.0)
-
-This project is licensed under Apache License Version 2.0
+`pinned`: true