save changes

app.py

@@ -59,6 +59,40 @@ base_model = nn.Sequential(
 def main():
     st.title("American Sign Language Recognition")
     
+    about = """
+# American Sign Language Recognition
+
+This project demonstrates a Convolutional Neural Network (CNN) model for recognizing American Sign Language (ASL) using the Hugging Face Hub and Streamlit for the user interface. The model is trained on a dataset of ASL images and includes data augmentation techniques to improve generalization.
+
+## Table of Contents
+- Introduction
+- Installation
+- Usage
+- Model Architecture
+- Data Augmentation
+- Training
+- Results
+- Acknowledgements
+
+## Introduction
+This project aims to build a robust ASL recognition system using deep learning techniques. The model is implemented in PyTorch and utilizes the Hugging Face Hub for dataset management. The Streamlit app provides an interactive interface for training and visualizing the model's performance.
+
+## Installation
+To run this project, you need to have Python installed along with the following libraries:
+- `streamlit`
+- `torch`
+- `torchvision`
+- `datasets`
+- `huggingface_hub`
+- `matplotlib`
+
+You can install the required libraries using:
+```bash
+pip install streamlit torch torchvision datasets huggingface_hub matplotlib
+"""
+    with st.expander("About", expanded=True):
+        st.markdown(about)
+
     # Move slider and button to sidebar
     num_epochs = st.sidebar.slider("Number of Epochs", 1, 20, 5)
     train_button = st.sidebar.button("Train Model")

model_repo

@@ -0,0 +1 @@
+Subproject commit 3b1e03d8d415269d86b88c9df83295a9ef454bb5