Dockerfile

# Use a lightweight Python base
FROM python:3.10-slim

# Set working directory
WORKDIR /app

# Install system packages if needed
RUN apt-get update && apt-get install -y \
    git \
    && rm -rf /var/lib/apt/lists/*

# Install PyTorch CPU version
RUN pip install --no-cache-dir torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cpu

# Copy dependency list
COPY requirements.txt .

# Install Python dependencies
RUN pip install --no-cache-dir -r requirements.txt

# Create cache directory and set permissions
RUN mkdir -p /app/cache && chmod -R 777 /app/cache

# Set environment variable for Hugging Face cache

# Set cache env vars
ENV HF_HOME=/app/cache
ENV TORCH_HOME=/app/cache

# ✅ Pre-download BERT
RUN python -c "\
import os; \
from transformers import BertTokenizerFast, BertModel; \
import torchvision.models as models; \
from torchvision.models import Inception_V3_Weights; \
cache_dir = '/app/cache'; \
BertTokenizerFast.from_pretrained('bert-base-multilingual-cased', cache_dir=cache_dir); \
BertModel.from_pretrained('bert-base-multilingual-cased', cache_dir=cache_dir); \
models.inception_v3(weights=Inception_V3_Weights.IMAGENET1K_V1); \
print('Files in cache:', os.listdir(cache_dir))"

# Copy the app and model files
COPY . .

# Expose port
EXPOSE 7860

# Run FastAPI app with Uvicorn
CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]