Merge pull request #98 from NeotomaDB/93-docker-compose-for-article-r…

…elevance

Docker compose for article relevance

docker-compose.yml

@@ -1,5 +1,28 @@
 version: "3.9"
 services:
+  article-relevance-prediction:
+    image: metaextractor-article-relevance:v0.0.1
+    build:
+      dockerfile: ./docker/article-relevance/Dockerfile
+      context: .
+    environment:
+      - N_RECENT=10
+      - MIN_DATE=
+      - MAX_DATE=
+      - TERM=
+      - AUTO_MIN_DATE=False
+      - AUTO_CHECK_DUP=False
+      - OUTPUT_PATH=/outputs/
+      - SEND_XDD=False
+      - DOI_FILE_PATH=/raw/gdd_api_return.json
+      - MODEL_PATH=/models/logistic_regression_model.joblib
+
+    volumes:
+      - ./data/article-relevance/outputs:/output
+      - ./data/article-relevance/processed/prediction_parquet:/parquet
+      - ./data/article-relevance/raw:/raw
+      - ./models/article-relevance:/models
+
   data-review-tool:
     image: metaextractor-data-review-tool:v0.0.1
     build:

docker/article-relevance/Dockerfile

@@ -1,6 +1,8 @@
 # Use the official Python 3.10 image as the base image
 FROM python:3.10
 
+ENV DOI_PATH="/raw"
+ENV PARQUET_PATH="/parquet"
 # Set the working directory inside the container
 WORKDIR /app/
 
@@ -13,9 +15,6 @@ RUN pip install --no-cache-dir -r requirements.txt
 # Copy the entire repository folder into the container
 COPY src ./src
 
-# Copy the model folder into the container
-COPY models/article-relevance ./models/article-relevance
-
 # Copy the shell script to the container
 COPY docker/article-relevance/run-prediction.sh .
 
@@ -24,7 +23,10 @@ RUN chmod +x run-prediction.sh
 
 # Mount volumes
 RUN mkdir -p /output
-VOLUME ["/output"]
+RUN mkdir -p /raw
+RUN mkdir -p /parquet
+RUN mkdir -p /models
+VOLUME ["/output", "/parquet", "/raw", "/models"]
 
 # Set the entry point for the Docker container
 ENTRYPOINT ["./run-prediction.sh"]

src/article_relevance/relevance_prediction_parquet.py

@@ -422,9 +422,19 @@ def main():
     opt = docopt(__doc__)
 
     doi_list_file_path = opt["--doi_file_path"]
-    model_path = opt['--model_path']
     output_path = opt['--output_path']
     send_xdd = opt['--send_xdd']
+
+    # # /models directory is a mounted volume, containing the model object
+    # models = os.listdir("/models")
+    # models = [f for f in models if f.endswith(".joblib")]
+
+    # if models:
+    #     model_path = os.path.join("/models", models[0])
+    # else:
+    #     model_path = ""
+
+    model_path = opt['--model_path']
 
     metadata_df = crossref_extract(doi_list_file_path)