Try Dockerfile based on requirements_simple.txt

.github/workflows/python-app.yml

@@ -16,7 +16,7 @@ jobs:
   build:
     runs-on: ubuntu-latest
     container: 
-      image: jbreffle/monkeytype-analysis:latest  # Use the Docker image from Docker Hub
+      image: jbreffle/monkeytype-analysis:slim  # Use the Docker image from Docker Hub
 
     steps:
     - uses: actions/checkout@v4

Dockerfile

@@ -1,16 +1,11 @@
-FROM python:3.10
-
-# Install dependencies in a single layer and clean up in the same RUN to reduce image size
-RUN python -m pip install --upgrade pip && \
-    pip install flake8 pytest && \
-    rm -rf /var/lib/apt/lists/*
+FROM python:3.10-slim
 
 # Set the working directory to /app
 WORKDIR /app
 
 # First, copy only requirements.txt and install Python dependencies to leverage Docker cache
-COPY requirements.txt .
-RUN if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
+COPY requirements_simple.txt requirements.txt
+RUN if [ -f requirements.txt ]; then pip install --no-cache-dir -r requirements.txt; fi
 
 # Then copy the rest of the application
 COPY . .

requirements_simple.txt

@@ -0,0 +1,22 @@
+#jupyter # to run notebooks
+#torch
+#hyperopt
+#pytest
+#mpld3
+#statsmodels
+pandas
+numpy
+matplotlib
+#scikit-learn # import sklearn
+scipy
+pyprojroot
+PyYAML # import yaml
+streamlit
+# pyarrow # import pyarrow.parquet as pq
+boto3 # For AWS services, like S3 and EC2
+python-dotenv # from dotenv import load_dotenv; load_dotenv()
+awswrangler # import awswrangler as wr
+s3fs # Necessary for pandas to read/write from/to S3
+# For GitHub actions
+flake8
+pytest

streamlit/pages/1_Trial_difficulty.py

@@ -1,7 +1,5 @@
 import streamlit as st
 import matplotlib.pyplot as plt
-import numpy as np
-from scipy.optimize import curve_fit
 import pyprojroot
 
 from src import plot