Minor Changes

README.md

@@ -1,6 +1,6 @@
 ---
 title: Water Body Segmentation
-emoji: 🏃
+emoji: 🤗
 colorFrom: blue
 colorTo: gray
 sdk: gradio
@@ -9,4 +9,19 @@ app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# UNET Water Body Segmentation - PyTorch
+
+This project contains the code for training and deploying a UNET model for water body segmentation from satellite images. The model is trained on the [Satellite Images of Water Bodies](https://www.kaggle.com/datasets/franciscoescobar/satellite-images-of-water-bodies) from Kaggle. The model is trained using PyTorch and deployed using [Gradio](https://gradio.app/) on [Hugging Face Spaces](https://huggingface.co/spaces).
+
+## 🚀 Getting Started
+
+All the code for training the model and exporting to ONNX format is present in the [notebook](notebooks) folder or you can use this [Kaggle Notebook](https://www.kaggle.com/code/gauthamkrishnan119/water-body-segmentation-pytorch) for training the model. The [app.py](app.py) file contains the code for deploying the model using Gradio. 
+
+## 🤗 Demo
+
+You can try out the model on [Hugging Face Spaces](https://huggingface.co/spaces/gauthamk/water-body-segmentation)
+
+## 🖥️ Sample Inference
+
+![Sample Inference](samples/sample1.png)
+

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 from functions import *
 
 title = "Water Body Segmentation - Image Segmentation PyTorch"
-examples = ['samples/image1.png', 'samples/image2.png', 'samples/image3.png', 'samples/image4.png', 'samples/image5.png']
+examples = ['examples/image1.png', 'examples/image2.png', 'examples/image3.png', 'examples/image4.png', 'examples/image5.png']
 
 interface = gr.Interface(fn=predict, inputs=gr.Image(type= 'numpy').style(height= 256),
             outputs= gr.Image(type = "numpy").style(height= 256),