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anibalrojosananibalrojosan
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Created requirements.txt using uv and reused former main.yml and dockefiles
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‎.github/workflows/main.yml‎

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name: CI/CD Pipeline
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on:
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push:
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branches:
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- main
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- feature/uv-ci-test
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jobs:
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build-and-push:
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with:
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python-version: '3.12'
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- name: Install uv
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run: pip install uv
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- name: Generate requirements.txt
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run: uv pip compile pyproject.toml -o requirements.txt
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- name: Create uv virtual environment
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run: uv venv
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- name: Install Python dependencies for model training
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run: |
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uv pip install -r requirements.txt
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pip install --upgrade pip
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pip install -r requirements.txt
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- name: Create models directory
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run: mkdir -p models
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- name: Train the model
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run: uv run python -m src.model.model_training
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run: python -m src.model.model_training
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v3

‎config/Dockerfile.api‎

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# 1. Use a "builder" stage to install dependencies
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FROM python:3.12-slim AS builder
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# Copy the uv binary from the official distroless image
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COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/
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WORKDIR /app
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# Create a virtual environment
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RUN uv venv
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# Copy dependency definitions
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COPY pyproject.toml uv.lock ./
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# Install dependencies into the virtual environment, excluding the project itself.
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RUN . .venv/bin/activate && uv sync --locked --no-install-project
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# Copy the application source code
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COPY src/ ./src/
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# Install the project itself into the venv.
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# Since dependencies are already installed, this step is very fast.
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RUN . .venv/bin/activate && uv pip install .
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# 2. Use a clean "final" stage for the runtime environment
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FROM python:3.12-slim
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WORKDIR /app
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# Copy the virtual environment with all dependencies from the builder stage
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COPY --from=builder /app/.venv ./.venv
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COPY requirements.txt .
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RUN pip install --no-cache-dir -r requirements.txt
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# Copy other necessary assets
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COPY src/ ./src/
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COPY models/model.joblib ./models/
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COPY data/data.csv ./data/
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# Activate the virtual environment by adding it to the PATH
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ENV PATH="/app/.venv/bin:$PATH"
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EXPOSE 5000
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CMD ["python", "-m", "gunicorn", "--bind", "0.0.0.0:5000", "src.app:app"]
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CMD ["gunicorn", "--bind", "0.0.0.0:5000", "src.app:app"]

‎config/Dockerfile.streamlit‎

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# 1. Use a "builder" stage to install dependencies
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FROM python:3.12-slim AS builder
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# Copy the uv binary from the official distroless image
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COPY --from=ghcr.io/astral-sh/uv:latest /uv /uvx /bin/
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FROM python:3.12-slim
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WORKDIR /app
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# Create a virtual environment
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RUN uv venv
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# Copy dependency definitions
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COPY pyproject.toml uv.lock ./
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COPY requirements.txt .
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# Install dependencies into the virtual environment, excluding the project itself.
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RUN . .venv/bin/activate && uv sync --locked --no-install-project
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RUN pip install --no-cache-dir -r requirements.txt
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# Copy the application source code
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COPY src/ ./src/
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# Install the project itself into the venv.
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# Since dependencies are already installed, this step is very fast.
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RUN . .venv/bin/activate && uv pip install .
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# 2. Use a clean "final" stage for the runtime environment
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FROM python:3.12-slim
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WORKDIR /app
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# Copy the virtual environment with all dependencies from the builder stage
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COPY --from=builder /app/.venv ./.venv
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# Activate the virtual environment by adding it to the PATH
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ENV PATH="/app/.venv/bin:$PATH"
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EXPOSE 8501
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CMD ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]

‎requirements.txt‎

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# This file was autogenerated by uv via the following command:
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# uv pip compile pyproject.toml -o requirements.txt
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altair==5.5.0
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# via streamlit
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attrs==25.4.0
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# via
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# jsonschema
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# referencing
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blinker==1.9.0
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# via
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# flask
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# streamlit
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cachetools==6.2.1
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# via streamlit
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certifi==2025.10.5
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# via requests
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charset-normalizer==3.4.4
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# via requests
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click==8.3.0
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# via
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# flask
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# streamlit
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colorama==0.4.6
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# via
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# click
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# pytest
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flask==3.1.2
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# via breast-cancer-ops (pyproject.toml)
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gitdb==4.0.12
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# via gitpython
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gitpython==3.1.45
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# via streamlit
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gunicorn==23.0.0
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# via breast-cancer-ops (pyproject.toml)
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idna==3.11
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# via requests
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iniconfig==2.1.0
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# via pytest
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itsdangerous==2.2.0
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# via flask
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jinja2==3.1.6
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# via
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# altair
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# flask
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# pydeck
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joblib==1.5.2
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# via
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# breast-cancer-ops (pyproject.toml)
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# scikit-learn
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jsonschema==4.25.1
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# via altair
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jsonschema-specifications==2025.9.1
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# via jsonschema
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markupsafe==3.0.3
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# via
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# flask
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# jinja2
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# werkzeug
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narwhals==2.8.0
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# via altair
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numpy==2.3.4
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# via
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# pandas
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# pydeck
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# scikit-learn
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# scipy
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# streamlit
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packaging==25.0
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# via
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# altair
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# gunicorn
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# pytest
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# streamlit
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pandas==2.3.3
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# via
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# breast-cancer-ops (pyproject.toml)
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# streamlit
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pillow==11.3.0
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# via streamlit
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pluggy==1.6.0
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# via pytest
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protobuf==6.33.0
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# via streamlit
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pyarrow==21.0.0
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# via streamlit
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pydeck==0.9.1
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# via streamlit
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pygments==2.19.2
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# via pytest
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pytest==8.4.2
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# via
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# breast-cancer-ops (pyproject.toml)
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# pytest-mock
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pytest-mock==3.15.1
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# via breast-cancer-ops (pyproject.toml)
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python-dateutil==2.9.0.post0
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# via pandas
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pytz==2025.2
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# via pandas
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referencing==0.37.0
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# via
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# jsonschema
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# jsonschema-specifications
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requests==2.32.5
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# via
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# breast-cancer-ops (pyproject.toml)
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# streamlit
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rpds-py==0.27.1
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# via
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# jsonschema
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# referencing
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scikit-learn==1.7.2
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# via breast-cancer-ops (pyproject.toml)
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scipy==1.16.2
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# via scikit-learn
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six==1.17.0
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# via python-dateutil
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smmap==5.0.2
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# via gitdb
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streamlit==1.50.0
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# via breast-cancer-ops (pyproject.toml)
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tenacity==9.1.2
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# via streamlit
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threadpoolctl==3.6.0
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# via scikit-learn
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toml==0.10.2
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# via streamlit
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tornado==6.5.2
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# via streamlit
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typing-extensions==4.15.0
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# via
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# altair
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# referencing
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# streamlit
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tzdata==2025.2
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# via pandas
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urllib3==2.5.0
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# via requests
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watchdog==6.0.0
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# via streamlit
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werkzeug==3.1.3
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# via flask

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