Merge pull request 'add parts for odcker build and gitea action build' (#1) from docker-build into main
Reviewed-on: https://source.michaeldileo.org/michael_dileo/automatic-linkedin-answer-ai/pulls/1
This commit was merged in pull request #1.
This commit is contained in:
37
.dockerignore
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.dockerignore
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# Git
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.git
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.gitignore
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# Python
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__pycache__
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*.pyc
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*.pyo
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*.pyd
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.Python
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*.so
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*.egg
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*.egg-info
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dist
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build
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.venv
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venv
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env
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# IDE
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.vscode
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.idea
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*.swp
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*.swo
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# Documentation
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README.md
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*.md
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# Config (user should provide their own)
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config.yaml
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# Docs (user should mount their own)
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docs/*.txt
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# CI/CD
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.gitea
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.gitea/workflows/README.md
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.gitea/workflows/README.md
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# Gitea Actions Workflows
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This directory contains Gitea Actions workflows for automated builds and releases.
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## Build and Release Workflow
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The `build-release.yml` workflow automatically builds Docker images and attaches them to Gitea releases.
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### Setup
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1. **Enable Gitea Actions**: Ensure Actions are enabled on your Gitea instance (Settings → Actions)
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2. **Configure Token**: The workflow uses `secrets.GITHUB_TOKEN` which Gitea provides automatically. If your Gitea version uses a different token name, you may need to:
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- Update the workflow to use `secrets.GITEA_TOKEN` instead
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- Or create a custom token secret in your repository settings
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3. **API URL**: The workflow uses `github.api_url` which Gitea Actions should provide. If you encounter issues, you may need to manually set the Gitea API URL in the workflow.
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### Usage
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**Automatic Release on Tag Push:**
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```bash
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git tag v1.0.0
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git push origin v1.0.0
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```
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The workflow will:
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1. Build a Docker image tagged with the version
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2. Save it as a compressed tar file
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3. Create or update a Gitea release
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4. Attach the tar file to the release
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**Manual Trigger:**
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You can also trigger the workflow manually from the Gitea Actions UI. The image will be saved as an artifact (not attached to a release).
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### Loading the Image
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After downloading the `.tar.gz` file from a release:
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```bash
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# Load the image
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docker load < automatic-linkedin-answer-ai-v1.0.0.tar.gz
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# Verify it's loaded
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docker images | grep automatic-linkedin-answer-ai
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# Run the container
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docker run -it \
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-v $(pwd)/config.yaml:/app/config.yaml \
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-v $(pwd)/docs:/app/docs \
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automatic-linkedin-answer-ai:v1.0.0
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```
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### Troubleshooting
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**Workflow fails with authentication errors:**
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- Check that Actions are enabled on your Gitea instance
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- Verify the token secret is available (should be automatic with Gitea Actions)
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- If using a self-hosted Gitea, ensure the API URL is accessible from the runner
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**Release not created:**
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- Ensure you're pushing a tag that matches the pattern `v*` (e.g., `v1.0.0`)
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- Check the workflow logs for specific error messages
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- Verify you have write permissions to the repository
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**Image too large:**
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- The workflow compresses the image with gzip
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- Consider using multi-stage builds in the Dockerfile to reduce image size
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- You may need to adjust Gitea's upload size limits if the image is very large
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80
.gitea/workflows/build-release.yml
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name: Build Docker Image and Release
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on:
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push:
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tags:
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- 'v*' # Triggers on version tags like v1.0.0
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workflow_dispatch: # Allows manual triggering
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jobs:
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build-and-release:
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runs-on: ubuntu-latest
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permissions:
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contents: write # Required to create/update releases
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steps:
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- name: Checkout code
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uses: actions/checkout@v4
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v3
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- name: Extract version from tag
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id: tag_version
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run: |
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if [[ "${{ github.ref }}" == refs/tags/* ]]; then
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VERSION=${GITHUB_REF#refs/tags/}
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echo "version=$VERSION" >> $GITHUB_OUTPUT
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else
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VERSION="latest-$(date +%Y%m%d-%H%M%S)"
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echo "version=$VERSION" >> $GITHUB_OUTPUT
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fi
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- name: Build Docker image
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run: |
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docker build -t automatic-linkedin-answer-ai:${{ steps.tag_version.outputs.version }} \
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-t automatic-linkedin-answer-ai:latest .
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- name: Save Docker image as tar
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run: |
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docker save automatic-linkedin-answer-ai:${{ steps.tag_version.outputs.version }} \
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| gzip > automatic-linkedin-answer-ai-${{ steps.tag_version.outputs.version }}.tar.gz
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ls -lh automatic-linkedin-answer-ai-*.tar.gz
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- name: Create or update release (Gitea API)
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if: startsWith(github.ref, 'refs/tags/')
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run: |
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TAG_NAME=${GITHUB_REF#refs/tags/}
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RELEASE_NAME="Release $TAG_NAME"
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IMAGE_FILE="automatic-linkedin-answer-ai-$TAG_NAME.tar.gz"
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# Check if release exists
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RELEASE_ID=$(curl -s -H "Authorization: token ${{ secrets.GITHUB_TOKEN }}" \
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"${{ github.api_url }}/repos/${{ github.repository }}/releases/tags/$TAG_NAME" \
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| grep -o '"id":[0-9]*' | head -1 | cut -d':' -f2)
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if [ -z "$RELEASE_ID" ]; then
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# Create new release
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curl -X POST \
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-H "Authorization: token ${{ secrets.GITHUB_TOKEN }}" \
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-H "Content-Type: application/json" \
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-d "{\"tag_name\":\"$TAG_NAME\",\"name\":\"$RELEASE_NAME\",\"draft\":false,\"prerelease\":false}" \
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"${{ github.api_url }}/repos/${{ github.repository }}/releases" > /tmp/release.json
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RELEASE_ID=$(grep -o '"id":[0-9]*' /tmp/release.json | head -1 | cut -d':' -f2)
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fi
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# Upload asset
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curl -X POST \
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-H "Authorization: token ${{ secrets.GITHUB_TOKEN }}" \
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-H "Content-Type: application/gzip" \
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--data-binary "@$IMAGE_FILE" \
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"${{ github.api_url }}/repos/${{ github.repository }}/releases/$RELEASE_ID/assets?name=$IMAGE_FILE"
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- name: Upload image artifact (for manual builds)
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if: github.event_name == 'workflow_dispatch'
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uses: actions/upload-artifact@v4
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with:
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name: docker-image
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path: automatic-linkedin-answer-ai-*.tar.gz
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retention-days: 30
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58
Dockerfile
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58
Dockerfile
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# Use Python 3.11 as base image
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FROM python:3.11-slim
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# Install system dependencies required for browser automation and other tools
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RUN apt-get update && apt-get install -y \
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wget \
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curl \
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git \
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build-essential \
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chromium \
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chromium-driver \
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fonts-liberation \
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libappindicator3-1 \
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libasound2 \
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libatk-bridge2.0-0 \
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libatk1.0-0 \
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libcups2 \
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libdbus-1-3 \
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libdrm2 \
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libgbm1 \
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libgtk-3-0 \
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libnspr4 \
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libnss3 \
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libx11-xcb1 \
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libxcomposite1 \
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libxdamage1 \
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libxrandr2 \
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xdg-utils \
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&& rm -rf /var/lib/apt/lists/*
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# Install uv for fast Python package management
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RUN pip install --no-cache-dir uv
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# Set working directory
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WORKDIR /app
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# Copy dependency files
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COPY pyproject.toml uv.lock ./
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# Install dependencies using uv
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RUN uv sync --frozen
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# Copy application code
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COPY app.py ./
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# Create docs directory (user should mount their own config.yaml and docs)
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RUN mkdir -p docs
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# Set Python path to use uv's virtual environment
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ENV PATH="/app/.venv/bin:$PATH"
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ENV PYTHONUNBUFFERED=1
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# Set Chrome/Chromium path for browser automation
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ENV CHROMIUM_PATH=/usr/bin/chromium
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ENV CHROME_PATH=/usr/bin/chromium
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# Default command (user should override with their own config.yaml)
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CMD ["python", "app.py"]
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1
app.py
1
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import asyncio
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import asyncio
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import logging
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import logging
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import os
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import os
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import time
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import yaml
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import yaml
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from browser_use import Agent
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from browser_use import Agent
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description = "Add your description here"
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description = "Add your description here"
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readme = "README.md"
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readme = "README.md"
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requires-python = ">=3.8"
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requires-python = ">=3.8"
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dependencies = []
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dependencies = [
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"browser-use",
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"langchain",
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"langchain-community",
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"langchain-openai",
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"langchain-text-splitters",
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"python-dotenv",
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"pyyaml",
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]
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