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| # Use Python 3.9 as the base image | |
| FROM python:3.9 | |
| # Set working directory in the container | |
| WORKDIR /app | |
| # Create a non-root user and set permissions | |
| RUN useradd -m myuser && chown -R myuser:myuser /app | |
| USER myuser | |
| # Set Hugging Face cache directory | |
| ENV HF_HOME=/app/.cache/huggingface | |
| # Update PATH for uvicorn | |
| ENV PATH="/home/myuser/.local/bin:${PATH}" | |
| # Upgrade pip to the latest version | |
| RUN pip install --upgrade pip | |
| # Copy requirements.txt and install dependencies | |
| COPY --chown=myuser:myuser requirements.txt . | |
| RUN pip install --no-cache-dir -r requirements.txt | |
| # Clear cache and pre-download models | |
| RUN rm -rf /app/.cache/huggingface/* && python -c "from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM; pipeline('text-classification', model='Hello-SimpleAI/chatgpt-detector-roberta'); pipeline('text-classification', model='openai-community/roberta-large-openai-detector'); pipeline('text-classification', model='sabaridsnfuji/arabic-ai-text-detector'); AutoTokenizer.from_pretrained('gpt2'); AutoModelForCausalLM.from_pretrained('gpt2'); AutoTokenizer.from_pretrained('aubmindlab/aragpt2-base'); AutoModelForCausalLM.from_pretrained('aubmindlab/aragpt2-base')" | |
| # Copy the application code | |
| COPY --chown=myuser:myuser . . | |
| # Run the FastAPI app with Uvicorn | |
| CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"] |