added updated version of serve.py

src/serve.py

@@ -0,0 +1,48 @@
+import logging
+
+from waitress import serve
+
+from image_prediction.config import CONFIG
+from image_prediction.default_objects import load_pipeline
+from image_prediction.flask import make_prediction_server
+from image_prediction.utils import get_logger
+from image_prediction.utils.banner import show_banner
+
+logger = get_logger()
+
+
+def main():
+    def predict(pdf):
+        # Keras service_estimator.predict stalls when service_estimator was loaded in different process;
+        # therefore, we re-load the model (part of the pipeline) every time we process a new document.
+        # https://stackoverflow.com/questions/42504669/keras-tensorflow-and-multiprocessing-in-python
+        logger.debug("Loading pipeline...")
+        pipeline = load_pipeline(verbose=CONFIG.service.verbose)
+        logger.debug("Running pipeline...")
+        return pipeline(pdf)
+
+    prediction_server = make_prediction_server(predict)
+
+    run_prediction_server(prediction_server, mode=CONFIG.webserver.mode)
+
+
+def run_prediction_server(app, mode="development"):
+    if mode == "development":
+        app.run(host=CONFIG.webserver.host, port=CONFIG.webserver.port, debug=True)
+    elif mode == "production":
+        serve(app, host=CONFIG.webserver.host, port=CONFIG.webserver.port, _quiet=False)
+
+
+if __name__ == "__main__":
+    logging_level = CONFIG.service.logging_level
+    logging.basicConfig(level=logging_level)
+    logging.getLogger("flask").setLevel(logging.ERROR)
+    logging.getLogger("urllib3").setLevel(logging.ERROR)
+    logging.getLogger("werkzeug").setLevel(logging.ERROR)
+    logging.getLogger("waitress").setLevel(logging.ERROR)
+    logging.getLogger("PIL").setLevel(logging.ERROR)
+    logging.getLogger("h5py").setLevel(logging.ERROR)
+
+    show_banner()
+
+    main()