ok

.gitea/workflows/build.yaml

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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 }}."
+

Dockerfile

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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"]

app.py

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+import gradio as gr
+from simplet5 import SimpleT5
+from gradio.themes.utils import sizes
+theme = gr.themes.Default(radius_size=sizes.radius_none).set(
+    block_label_text_color = '#4D63FF',
+    block_title_text_color = '#4D63FF',
+    button_primary_text_color = '#4D63FF',
+    button_primary_background_fill='#FFFFFF',
+    button_primary_border_color='#4D63FF',
+    button_primary_background_fill_hover='#EDEFFF',
+)
+
+
+
+model = SimpleT5()
+model.load_model("t5","snrspeaks/t5-one-line-summary")
+
+def sentiment_analysis(text):
+    result = model.predict(text)
+
+    return result[0]
+    
+demo = gr.Interface(fn=sentiment_analysis, 
+                    inputs='text',
+                    outputs='text',
+                    examples=[["We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production  machinelearning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in sophisticated applications, and  handling contradictory or incomplete supervision data. Overton automates the life cycle of model construction, deployment, and monitoring by providing a set of novel high-level, declarative abstractions. Overton's vision is to shift developers to these higher-level tasks instead of lower-level machine learning tasks.  In fact, using Overton, engineers can build deep-learning-based applications without writing any code in frameworks like TensorFlow. For over a year,  Overton has been used in production to support multiple applications in both near-real-time applications and back-of-house processing.  In that time, Overton-based applications have answered billions of queries in multiple languages and processed trillions of records reducing errors  1.7-2.9 times versus production systems."]],
+                    theme = theme,
+                    title = "摘要"
+                    )
+
+
+if __name__ == "__main__":
+#    demo.queue(concurrency_count=3)
+#    demo.launch(server_name = "0.0.0.0", server_port = 7028)
+    demo.launch()

requirements.txt

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+gradio
+transformers
+torch