add build.yaml Dockerfile app.py income.png invoice.png requirements.txt

.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
+RUN pip install protobuf==3.20.*
+
+CMD ["python", "app.py"]

app.py

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+from transformers import DonutProcessor, VisionEncoderDecoderModel
+import gradio as gr
+from PIL import Image
+import torch
+import re
+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',
+)
+
+
+processor = DonutProcessor.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa")
+model = VisionEncoderDecoderModel.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa")
+
+
+def vqa(image, question):
+    inp = Image.fromarray(image.astype('uint8'), 'RGB')
+    pixel_values = processor(inp, return_tensors="pt").pixel_values
+    task_prompt = "{user_input}"
+    prompt = task_prompt.replace("{user_input}", question)
+    decoder_input_ids = processor.tokenizer(prompt, add_special_tokens=False, return_tensors="pt")["input_ids"]
+    
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model.to(device)
+
+    outputs = model.generate(pixel_values.to(device),
+                               decoder_input_ids=decoder_input_ids.to(device),
+                               max_length=model.decoder.config.max_position_embeddings,
+                               early_stopping=True,
+                               pad_token_id=processor.tokenizer.pad_token_id,
+                               eos_token_id=processor.tokenizer.eos_token_id,
+                               use_cache=True,
+                               num_beams=1,
+                               bad_words_ids=[[processor.tokenizer.unk_token_id]],
+                               return_dict_in_generate=True,
+                               output_scores=True)
+
+
+    seq = processor.batch_decode(outputs.sequences)[0]
+    seq = seq.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "")
+    seq = re.sub(r"<.*?>", "", seq, count=1).strip()
+
+    return processor.token2json(seq)
+
+with gr.Blocks(theme=theme, css="footer {visibility: hidden}") as demo:
+    gr.Markdown("""
+    <div align='center' ><font size='60'>图片问答</font></div>
+    """)
+    with gr.Row():
+        with gr.Column():
+            image = gr.Image(label="图片")
+            question = gr.Textbox(label="问题")
+            with gr.Row():
+                button = gr.Button("提交", variant="primary")
+        box2 = gr.Textbox(label="文本")
+
+    button.click(fn=vqa, inputs=[image, question], outputs=box2)
+    examples = gr.Examples(examples=[['income.png', 'What are the 2020 net sales?'], ['invoice.png','What is the invoice number?']],  inputs=[image, question], label="例子")
+
+
+if __name__ == "__main__":
+    demo.queue(concurrency_count=3).launch(server_name = "0.0.0.0", server_port = 7026)

requirements.txt

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+gradio == 3.27.0
+transformers
+torch
+sentencepiece