Dockerfile

# M3GNet FastMCP HuggingFace Space image
FROM python:3.10-slim

ENV PIP_NO_CACHE_DIR=1 \
    HF_HUB_DISABLE_TELEMETRY=1 \
    TF_CPP_MIN_LOG_LEVEL=2 \
    CUDA_VISIBLE_DEVICES=-1

WORKDIR /app

# System build/runtime dependencies for pymatgen, ASE, and TensorFlow
RUN apt-get update \
    && apt-get install -y --no-install-recommends \
        build-essential \
        git \
        curl \
        libopenblas-dev \
        liblapack-dev \
        libffi-dev \
        libssl-dev \
        ca-certificates \
    && rm -rf /var/lib/apt/lists/*

# Copy everything from the build context into the image
COPY . /app

# Staged installation to avoid dependency conflicts:
# 1) Install TensorFlow 2.13 (requires numpy<=1.24.3)
# 2) Install m3gnet with --no-deps, then its deps manually with compatible versions
# 3) Install MCP service deps (needs pydantic v2)
# 4) Force upgrade pydantic to v2 (pymatgen works fine with it at runtime)

RUN pip install --upgrade pip

# Stage 1: TensorFlow with compatible numpy
RUN pip install --no-cache-dir tensorflow==2.13.0 numpy==1.24.3

# Stage 2: M3GNet and materials science stack
RUN pip install --no-cache-dir \
    ase==3.22.1 \
    monty==2024.2.2 \
    sympy \
    spglib \
    scipy \
    matplotlib \
    pandas \
    networkx \
    requests \
    ruamel.yaml \
    tabulate \
    uncertainties

# Install pymatgen with relaxed deps, then m3gnet
RUN pip install --no-cache-dir --no-deps pymatgen==2024.2.20 \
    && pip install --no-cache-dir --no-deps m3gnet==0.2.4

# Stage 3: MCP service dependencies
RUN if [ -f "mcp_output/requirements.txt" ]; then \
        pip install --no-cache-dir -r mcp_output/requirements.txt; \
    elif [ -f "requirements.txt" ]; then \
        pip install --no-cache-dir -r requirements.txt; \
    fi

# Stage 4: Ensure pydantic v2 for MCP (runtime compatible with pymatgen)
RUN pip install --no-cache-dir --upgrade "pydantic>=2.0"

EXPOSE 7860

CMD ["python", "/app/entrypoint.py"]