# Pull the base image with python 3.8 as a runtime for your Lambda
FROM public.ecr.aws/lambda/python:3.8

# Copy the requirements for pip + huggingface transformers
COPY requirements.txt ./

# Install the python requirements from requirements.txt
RUN python3.8 -m pip install -r requirements.txt

# Copy the earlier created app.py file to the container
COPY app.py ./

# Load the BERT model from Huggingface and store it in the model directory, when Lambda function is invoked it will be copied to EFS.
RUN mkdir model1
RUN curl -L https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/pytorch_model.bin -o ./model1/pytorch_model.bin
RUN curl https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json -o ./model1/config.json
RUN curl https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json -o ./model1/tokenizer.json
RUN curl https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer_config.json -o ./model1/tokenizer_config.json

# Load a second model
RUN mkdir model2
RUN curl -L https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/pytorch_model.bin -o ./model2/pytorch_model.bin
RUN curl https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json -o ./model2/config.json
RUN curl https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json -o ./model2/tokenizer.json
RUN curl https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer_config.json -o ./model2/tokenizer_config.json

# Set the CMD to your handler
CMD ["app.lambda_handler"]