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Build-Deploy-Actions Details

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jianjiang 2023-04-21 13:48:37 +08:00
commit 9f9bd98743
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name: Build
run-name: ${{ github.actor }} is upgrade release 🚀
on: [push]
env:
REPOSITORY: ${{ github.repository }}
COMMIT_ID: ${{ github.sha }}
jobs:
Build-Deploy-Actions:
runs-on: ubuntu-latest
steps:
- run: echo "🎉 The job was automatically triggered by a ${{ github.event_name }} event."
- run: echo "🐧 This job is now running on a ${{ runner.os }} server hosted by Gitea!"
- run: echo "🔎 The name of your branch is ${{ github.ref }} and your repository is ${{ github.repository }}."
- name: Check out repository code
uses: actions/checkout@v3
-
name: Setup Git LFS
run: |
git lfs install
git lfs fetch
git lfs checkout
- name: List files in the repository
run: |
ls ${{ github.workspace }}
-
name: Docker Image Info
id: image-info
run: |
echo "::set-output name=image_name::$(echo $REPOSITORY | tr '[:upper:]' '[:lower:]')"
echo "::set-output name=image_tag::${COMMIT_ID:0:10}"
-
name: Login to Docker Hub
uses: docker/login-action@v2
with:
registry: artifacts.iflytek.com
username: ${{ secrets.DOCKERHUB_USERNAME }}
password: ${{ secrets.DOCKERHUB_TOKEN }}
- name: Set up Docker Buildx
uses: docker/setup-buildx-action@v2
-
name: Build and push
run: |
docker version
docker buildx build -t artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }} . --file ${{ github.workspace }}/Dockerfile --load
docker push artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }}
docker rmi artifacts.iflytek.com/docker-private/atp/${{ steps.image-info.outputs.image_name }}:${{ steps.image-info.outputs.image_tag }}
- run: echo "🍏 This job's status is ${{ job.status }}."

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FROM python:3.8.13
WORKDIR /app
COPY . /app
RUN pip config set global.index-url https://pypi.mirrors.ustc.edu.cn/simple
RUN pip install -r requirements.txt
CMD ["python", "app.py"]

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from PIL import Image
import gradio as gr
from transformers import ViTFeatureExtractor, ViTForImageClassification
import torch
# Init model, transforms
model = ViTForImageClassification.from_pretrained('nateraw/vit-age-classifier')
transforms = ViTFeatureExtractor.from_pretrained('nateraw/vit-age-classifier')
def predict(im):
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"}
# Transform our image and pass it through the model
inputs = transforms(im, return_tensors='pt')
output = model(**inputs)
# Predicted Class probabilities
proba = output.logits.softmax(1)
# Predicted Classes
preds = proba.argmax(1)
values, indices = torch.topk(proba, k=5)
return {labels[i.item()]: v.item() for i, v in zip(indices.numpy()[0], values.detach().numpy()[0])}
inputs = [
gr.inputs.Image(type="pil", label="Input Image")
]
title = "ViT-Age-Classification"
description = "ViT-Age-Classification is used to categorize an individual's age using images"
article = " <a href='https://huggingface.co/nateraw/vit-age-classifier'>ViT Age Classification Model Repo on Hugging Face Model Hub</a>"
examples = ["stock_baby.webp","stock_teen.webp","stock_guy.jpg","stock_old_woman.jpg"]
gr.Interface(
predict,
inputs,
outputs = 'label',
title=title,
description=description,
article=article,
examples=examples,
theme="huggingface",
).launch(debug=True, enable_queue=True)

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transformers
gradio
Pillow
torch

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