Setup workshop example

.github/workflows/deploy.yml

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+name: Deploy
+on:
+  push:
+    branches: [ main ]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: git push https://andreped:$HF_TOKEN@huggingface.co/spaces/andreped/ViT-ImageClassifier main

.gitignore

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+venv/
+*.jpg
+*.jpeg
+*.png
+flagged/

README.md

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-# INF1600-ai-workshop
+---
+title: 'ViT: Image Classifier'
+colorFrom: indigo
+colorTo: indigo
+sdk: gradio
+app_port: 7860
+emoji: 🫁
+pinned: false
+license: mit
+app_file: app.py
+---
+
+
+# INF-1600 AI Deployment workshop
+
+This workshop was developed for the _"Intro to Artificial Intelligence"_ course with
+code INF-1600 at UiT: The Arctic University of Norway.
+
+The workshop was a collaboration with UiT and Sopra Steria.
+
+In this workshop, you will get a prior on:
+* Cloning and pushing code from/to GitHub
+* Load and run a pretrained image classification model from [Transformers]().
+* Develop a simple web application to enable users to test the model using [Gradio]().
+* Making a web app accessible on the local network
+* Making a public web app anyone can access using [Hugging Face Spaces]().
+
+## Getting Started
+
+1. Make your first GitHub account by going [here](https://github.com/) and signing up.
+
+2. After logging in to GitHub, go to the code repository [here](https://github.com/andreped/INF1600-ai-workshop).
+
+3. Make a fork of the repository and make your own copy of the code by making a fork.
+
+4. Now you are ready to clone your own fork to your own laptop by opening a terminal and running (remember to replace `<username>` with your own GitHub user name):
+```
+git clone git+https://github.com/<username>/INF1600-ai-workshop.git
+```
+
+5. After cloning, go inside the repository, and from the terminal run these lines to create a virtual environment and activate it:
+```
+virtualenv -ppython3 venv --clear
+source venv/bin/activate
+```
+
+6. **TASK:** Analyse the `app.py` script which python packages are required, and
+add the missing dependencies to the `requirements.txt` file.
+
+7. Install dependencies to the virtual environment by:
+```
+pip install -r requirements.txt
+```
+
+8. To test if everything is working, try to run the `app.py` script to launch the web server.
+
+9. You can then access the web app by going to [http://127.0.0.1:7860](http://127.0.0.1:7860) in your favourite web browser.
+
+10. From the prompted website, try clicking one of the image examples and clicking the orange `Submit` button. The model results should show on the right after a few seconds.
+
+10. Try accessing this address from your mobile phone.
+
+11. This should not work, to access the app from a different device, you need to serve it.
+Try setting `share=True` in the `interface.launch()` call in the `app.py` script.
+When running `app.py` now, you should be given a different web address. Try using that one instead.
+
+But of course, hosting the app yourself from your laptop is not ideal. What if there was some alternative way to do this **completely for free**...
+
+12. Click [here](https://huggingface.co/join) to go to the Hugging Face sign up page and make an account.
+
+13. After making an account and logging in, click the `+ New` button on the left of the website and choose `Space` from the dropdown.
+
+14. In the `Create a new Space` tab, choose a `Space name` for the app, choose a License (preferably `MIT`), among the `Space SDKs` choose `Gradio`, and finally, click `Create Space`.
+
+15. At the bottom of the page, click the `Create` hyperlink at the `(Hunt: Create the app.py file (...))`
+
+16. Name the file `app.py`, copy-paste the `app.py` code
+
+## Workshop Organizers
+
+* [André Pedersen](https://github.com/andreped), Apps, Sopra Steria
+* [Tor-Arne Schmidt Nordmo](https://uit.no/ansatte/person?p_document_id=581687), IFI, UiT: The Arctic University of Norway
+
+## License
+
+The code in this repository is released under [MIT license](https://github.com/andreped/INF1600-ai-workshop).

app.py

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+import re
+import requests
+
+import gradio as gr
+from torch import topk
+from torch.nn.functional import softmax
+from transformers import ViTImageProcessor, ViTForImageClassification
+
+
+def load_label_data():
+    file_url = "https://gist.githubusercontent.com/yrevar/942d3a0ac09ec9e5eb3a/raw/238f720ff059c1f82f368259d1ca4ffa5dd8f9f5/imagenet1000_clsidx_to_labels.txt"
+    response = requests.get(file_url)
+    labels = []
+    pattern = '["\'](.*?)["\']'
+    for line in response.text.split('\n'):
+        try:
+            tmp = re.findall(pattern, line)[0]
+            labels.append(tmp)
+        except IndexError:
+            pass
+    return labels
+
+
+def run_model(image, nb_classes):
+    processor = ViTImageProcessor.from_pretrained('google/vit-base-patch16-224')
+    model = ViTForImageClassification.from_pretrained('google/vit-base-patch16-224')
+
+    inputs = processor(images=image, return_tensors="pt")
+    outputs = model(**inputs)
+    outputs = softmax(outputs.logits, dim=1)
+    outputs = topk(outputs, k=nb_classes)
+    return outputs
+
+
+def classify_image(image, labels, nb_classes):
+    top10 = run_model(image, nb_classes=nb_classes)
+    return {labels[top10[1][0][i]]: float(top10[0][0][i]) for i in range(nb_classes)}
+
+
+def main():
+    nb_classes = 10
+    labels = load_label_data()
+    examples=[
+        ['cat.jpg'],
+        ['dog.jpeg']
+    ]
+
+    # define UI
+    image = gr.Image(height=512)
+    label = gr.Label(num_top_classes=nb_classes)
+    interface = gr.Interface(
+        fn=lambda x: classify_image(x, labels, nb_classes), inputs=image, outputs=label, title='Vision Transformer Image Classifier', examples=examples,
+    )
+    interface.launch(debug=True, share=False, height=600, width=1200)  # by setting share=True you can serve the website for others to access
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+gradio
+torch
+transformers