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Build-Deploy-Actions
Details
Build-Deploy-Actions
Details
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name: Build
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run-name: ${{ github.actor }} is upgrade release 🚀
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on: [push]
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env:
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REPOSITORY: ${{ github.repository }}
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COMMIT_ID: ${{ github.sha }}
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jobs:
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Build-Deploy-Actions:
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runs-on: ubuntu-latest
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steps:
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- run: echo "🎉 The job was automatically triggered by a ${{ github.event_name }} event."
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- run: echo "🐧 This job is now running on a ${{ runner.os }} server hosted by Gitea!"
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- run: echo "🔎 The name of your branch is ${{ github.ref }} and your repository is ${{ github.repository }}."
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- name: Check out repository code
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uses: actions/checkout@v3
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-
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name: Setup Git LFS
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run: |
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git lfs install
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git lfs fetch
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git lfs checkout
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- name: List files in the repository
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run: |
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ls ${{ github.workspace }}
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-
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name: Docker Image Info
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id: image-info
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run: |
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echo "::set-output name=image_name::$(echo $REPOSITORY | tr '[:upper:]' '[:lower:]')"
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echo "::set-output name=image_tag::${COMMIT_ID:0:10}"
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-
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name: Login to Docker Hub
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uses: docker/login-action@v2
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with:
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registry: artifacts.iflytek.com
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username: ${{ secrets.DOCKERHUB_USERNAME }}
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password: ${{ secrets.DOCKERHUB_TOKEN }}
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v2
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-
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name: Build and push
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run: |
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docker version
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docker buildx build -t artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }} . --file ${{ github.workspace }}/Dockerfile --load
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docker push artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }}
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docker rmi artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }}
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- run: echo "🍏 This job's status is ${{ job.status }}."
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FROM python:3.8.13
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WORKDIR /app
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COPY . /app
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RUN pip config set global.index-url https://pypi.mirrors.ustc.edu.cn/simple
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RUN pip install -r requirements.txt
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CMD ["python", "app.py"]
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from PIL import Image
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import gradio as gr
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from transformers import ViTFeatureExtractor, ViTForImageClassification
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import torch
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# Init model, transforms
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model = ViTForImageClassification.from_pretrained('nateraw/vit-age-classifier')
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transforms = ViTFeatureExtractor.from_pretrained('nateraw/vit-age-classifier')
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def predict(im):
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labels = {0:"0-2", 1: "3-9" , 2: "10-19", 3: "20-29", 4: "30-39", 5: "40-49", 6: "50-59", 7:"60-69",8:"more than 70"}
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# Transform our image and pass it through the model
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inputs = transforms(im, return_tensors='pt')
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output = model(**inputs)
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# Predicted Class probabilities
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proba = output.logits.softmax(1)
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# Predicted Classes
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preds = proba.argmax(1)
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values, indices = torch.topk(proba, k=5)
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return {labels[i.item()]: v.item() for i, v in zip(indices.numpy()[0], values.detach().numpy()[0])}
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inputs = [
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gr.inputs.Image(type="pil", label="Input Image")
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]
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title = "ViT-Age-Classification"
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description = "ViT-Age-Classification is used to categorize an individual's age using images"
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article = " <a href='https://huggingface.co/nateraw/vit-age-classifier'>ViT Age Classification Model Repo on Hugging Face Model Hub</a>"
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examples = ["stock_baby.webp","stock_teen.webp","stock_guy.jpg","stock_old_woman.jpg"]
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gr.Interface(
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predict,
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inputs,
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outputs = 'label',
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title=title,
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description=description,
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article=article,
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examples=examples,
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theme="huggingface",
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).launch(debug=True, enable_queue=True)
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transformers
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gradio
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Pillow
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torch
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