fix: use of original boilerplate with python 3.11

Author: neagualexa

Dockerfile

@@ -1,5 +1,5 @@
 # Layer 1: Build the virtual environment
-FROM ghcr.io/lambda-feedback/evaluation-function-base/python:3.11 AS builder
+FROM ghcr.io/lambda-feedback/baseevalutionfunctionlayer:3.11 AS builder
 
 RUN pip install poetry==1.8.3
 
@@ -15,13 +15,13 @@ RUN --mount=type=cache,target=$POETRY_CACHE_DIR \
     poetry install --without dev --no-root
 
 # Layer 2: Download NLTK models
-FROM ghcr.io/lambda-feedback/evaluation-function-base/python:3.11 AS models
+FROM ghcr.io/lambda-feedback/baseevalutionfunctionlayer:3.11 AS models
 
 ENV NLTK_DATA=/usr/share/nltk_data
 
-ENV MODEL_PATH=/app/evaluation_function/models
+ENV MODEL_PATH=/app/app/models
 
-RUN apt-get update && apt-get install -y --no-install-recommends \
+RUN yum install -y \
     wget \
     unzip
 
@@ -31,17 +31,17 @@ RUN ./scripts/download_models.sh
 RUN ./scripts/download_nltk.sh
 
 # Layer 3: Final image
-FROM ghcr.io/lambda-feedback/evaluation-function-base/python:3.11
+FROM ghcr.io/lambda-feedback/baseevalutionfunctionlayer:3.11
 
 ENV VIRTUAL_ENV=/app/.venv \
     PATH="/app/.venv/bin:$PATH"
 
 ENV NLTK_DATA=/usr/share/nltk_data
 
-ENV MODEL_PATH=/app/evaluation_function/models
+ENV MODEL_PATH=/app/app/models
 
 # Copy the evaluation function to the app directory
-COPY ./evaluation_function ./evaluation_function
+COPY ./evaluation_function ./app
 
 # Precompile python files for faster startup
 RUN python -m compileall -q .
@@ -50,34 +50,9 @@ COPY --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV}
 COPY --from=models ${NLTK_DATA} ${NLTK_DATA}
 COPY --from=models ${MODEL_PATH} ${MODEL_PATH}
 
-#  ----- For Linux development instead of Layer 2 NLTK downloads
-# # Warnings: those commands sometimes download corrupted zips, so it is better to wget each package from the main site
-# RUN python -m nltk.downloader wordnet
-# RUN python -m nltk.downloader word2vec_sample
-# RUN python -m nltk.downloader brown
-# RUN python -m nltk.downloader stopwords
-# RUN python -m nltk.downloader punkt
-# RUN python -m nltk.downloader punkt_tab
+# Set permissions so files and directories can be accessed on AWS
+RUN chmod 644 $(find ./app/ -type f)
+RUN chmod 755 $(find ./app/ -type d)
 
-# Worker process start timeout to allow shimmy to start considering all downloads
-ENV FUNCTION_WORKER_SEND_TIMEOUT="600s"
-
-# Worker process stop timeout to allow shimmy to stop the container
-ENV FUNCTION_WORKER_STOP_TIMEOUT="300s"
-
-ENV EVAL_RPC_TRANSPORT="ipc"
-
-# Command to start the evaluation function with
-ENV FUNCTION_COMMAND="python"
-
-# Args to start the evaluation function with
-ENV FUNCTION_ARGS="-m,evaluation_function.main"
-
-# The transport to use for the RPC server
-ENV FUNCTION_RPC_TRANSPORT="ipc"
-
-ENV LOG_LEVEL="debug"
-
-# ------- FOR DEBIAN
-# Keep the container running
-# CMD ["tail", "-f", "/dev/null"]
+# The entrypoint for AWS is to invoke the handler function within the app package
+CMD [ "/app/app.handler" ]

poetry.lock

@@ -26,6 +26,10 @@ lf_toolkit = { git = "https://github.com/lambda-feedback/toolkit-python.git", br
     "parsing",
     "ipc",
 ] }
+jsonschema = "^4.23.0"
+evaluation-function-utils = "^0.1.3"
+black = "^24.10.0"
+autopep8 = "^2.3.1"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^8.2.2"