update readme

README.md

@@ -10,4 +10,61 @@ pinned: false
 license: mit
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-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Neural image style transfer
+
+Neural style transfer is an optimization technique used to take two images—a content image and a style reference image (such as an artwork by a famous painter)—and blend them together so the output image looks like the content image, but “painted” in the style of the style reference image.
+
+This is implemented by optimizing the output image to match the content statistics of the content image and the style statistics of the style reference image. These statistics are extracted from the images using a convolutional network.
+
+## Example
+
+## Goal
+
+In this project we are buidling **streamlit** demo for Fast arbitrary image style transfer using a **pretrained** Image Stylization model from **TensorFlow Hub**. To use it, simply upload a content image and style image.
+
+## Deployed app
+
+The app is deployed on Huggingface **Spaces**: [Click here for live demo](https://huggingface.co/spaces/SudhanshuBlaze/neural-style-transfer-streamlit)
+
+### Project Structure
+
+```bash
+Neural Style Transfer Project
+
+├── app.py
+├── requirements.txt
+└── examples
+```
+
+### Project Requirements
+
+- Python3
+- git
+
+### Project Steps
+
+- `Step 1`: Cloning the repo
+
+```bash
+git clone https://github.com/DigitalProductschool/AI-Makerspace.git
+```
+
+- `Step 2`: Changing working directory to TextAutocomplete-Streamlit
+
+```bash
+cd AI-Makerspace/HuggingFace/StyleTransfer
+```
+
+- `Step 3`: Installing dependencies using pip3
+
+```bash
+pip3 install -r requirements.txt
+```
+
+- `Step 4`: Running the streamlit web app
+
+```bash
+streamlit run app.py
+```
+
+#### Now go to http://localhost:8501/ to test out this streamlit web-app