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+# Facial Emotion Recognition
+
+## Introduction
+
+This project is a facial emotion detection system that uses a convolutional neural network to detect facial emotions. 
+The model is trained on the [FER-2013 dataset](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data) which consists of 48x48 pixel grayscale images of faces with 7 different emotions:
+- Angry
+- Disgust
+- Fear
+- Happy
+- Sad
+- Surprise
+- Neutral
+
+The model is trained using PyTorch and the dataset is preprocessed using torchvision image transforms.
+
+## Web Application
+
+The web application backend is built using Flask and the front-end is created using SvelteJS. 
+The application allows the user to either capture an image of themselves or upload an image and the model will predict the emotion of the person in the image.
+
+The application is live and can be accessed at [https://fer.rohand.in](https://fer.rohand.in/).
+
+## Usage
+
+To run the models, clone the repository and run the following command in the terminal:
+
+```shell
+python -m pip install -r requirements.txt
+```
+
+Then, run the following command to make a prediction on an image:
+
+```shell
+python predict.py --image-file <path-to-image> --model-path model/SimpleCNNMdodel
+```
+
+## Models
+
+### SimpleCNNModel
+
+This model is a simple convolutional neural network with 3 convolutional layers with ReLU activation, MaxPooling and BatchNormalization and 4 fully connected layers. 
+The model is trained for 3 epochs with a batch size of 32 and a learning rate of 0.001 for the Adam optimizer. 
+The model achieves a validation accuracy of 56%.
+
+## References
+- Deploy multiple Flask applications in the same server: [here](https://towardsdatascience.com/deploy-multiple-flask-applications-using-nginx-and-gunicorn-16f8f7865497)
+- SvelteJS + Flask: [here](https://cabreraalex.medium.com/svelte-js-flask-combining-svelte-with-a-simple-backend-server-d1bc46190ab9)