Fix logger

Makefile

@@ -1,13 +1,13 @@
 install:
-	. .venv/bin/activate; pip install -Ur requirements.txt
+	. .venv/bin/activate; pip install -Ur dev-requirements.txt
 
 activate:
 	. .venv/bin/activate
 
 install_venv:
 	python3 -m venv .venv
 	. .venv/bin/activate; python -m pip install --upgrade pip
-	. .venv/bin/activate; python -m pip install -r requirements.txt
+	. .venv/bin/activate; python -m pip install -r dev-requirements.txt
 
 formatter:
 	. .venv/bin/activate; command black --line-length 125 .
@@ -55,3 +55,6 @@ free_up_space:
 	sudo rm -rf /opt/hostedtoolcache/CodeQL
 	sudo docker image prune --all --force
 	df -h
+
+run_it:
+	. .venv/bin/activate; command python src/run_it.py

dev-requirements.txt

@@ -10,4 +10,6 @@ torchvision
 torchaudio
 tiktoken
 accelerate
-pip-upgrader==1.4.15
+pip-upgrader==1.4.15
+pytest==8.2.2
+black==24.4.2

requirements.txt

@@ -1,8 +1,6 @@
 pydantic==2.8.2
 ollama==0.2.1
 graypy==2.1.0
-pytest==8.2.2
-black==24.4.2
 sentry_sdk==1.44.0
-git+https://github.com/huridocs/ml-cloud-connector.git@e6751aa2f519b1f4344a4e81a51b9c97fbbc6c7b
+git+https://github.com/huridocs/ml-cloud-connector.git@85e0ce5aad8d7428d15343188975b29e8f1d904e
 git+https://github.com/huridocs/queue-processor@bab1f4419b0768df518d06795afd5df2ba0e331c

src/run_it.py

@@ -1,20 +1,18 @@
 from time import time
-from ml_cloud_connector.MlCloudConnector import MlCloudConnector
-from data_model.TranslationTask import TranslationTask
-from translate import get_translation
+from data_model.TranslationResponseMessage import TranslationResponseMessage
+from data_model.TranslationTaskMessage import TranslationTaskMessage
+from start_queue_processor import process
 
 if __name__ == "__main__":
     start = time()
     print("start")
 
-    text = (
-        "While there exists a rich body of work on video prediction using generative models, "
-        "the design of methods for evaluating the quality of the videos has received much less attention."
-    )
-    language_from = "English"
-    language_to = "French"
-    translation_task = TranslationTask(text=text, language_from=language_from, language_to=language_to)
-    connector = MlCloudConnector("translation")
-    translation, finished, error = connector.execute(get_translation, connector.service_logger, translation_task)
-    print(translation)
+    text = "While there exists a rich body of work on video prediction using generative models, "
+    text += "the design of methods for evaluating the quality of the videos has received much less attention."
+
+    translation_task_message = TranslationTaskMessage(key="key", text=text, language_from="English", languages_to=["French"])
+
+    results = process(translation_task_message.model_dump())
+    translation_response_message = TranslationResponseMessage(**results)
+    print(translation_response_message.model_dump())
     print("time", round(time() - start, 2), "s")

src/start_queue_processor.py

@@ -1,6 +1,7 @@
 import os
 
 from ml_cloud_connector.MlCloudConnector import MlCloudConnector
+from ml_cloud_connector.ServerType import ServerType
 from pydantic_core._pydantic_core import ValidationError
 from queue_processor.QueueProcessor import QueueProcessor
 
@@ -37,12 +38,12 @@ def get_translation_from_task(translation_task: TranslationTask):
     if not translation_task.text.strip():
         return get_empty_translation(translation_task)
 
-    connector = MlCloudConnector("translation")
-    translation, finished, error = connector.execute(get_translation, service_logger, translation_task)
+    ml_connector = MlCloudConnector(ServerType.TRANSLATION, service_logger)
+    translation, finished, error = ml_connector.execute_on_cloud_server(get_translation, service_logger, translation_task)
     return translation if finished else get_error_translation(translation_task, error)
 
 
-def process(message):
+def process(message: dict[any, any]) -> dict[any, any] | None:
     try:
         task_message = TranslationTaskMessage(**message)
         service_logger.info(f"New task {task_message.model_dump()}")

src/translate.py

@@ -1,4 +1,5 @@
 from ml_cloud_connector.MlCloudConnector import MlCloudConnector
+from ml_cloud_connector.ServerType import ServerType
 from ollama import Client
 
 from data_model.Translation import Translation
@@ -26,7 +27,7 @@ def get_content(translation_task: TranslationTask):
 
 
 def get_translation(translation_task: TranslationTask) -> Translation:
-    ip_address = MlCloudConnector("translation").get_ip()
+    ip_address = MlCloudConnector(ServerType.TRANSLATION, service_logger).get_ip()
     client = Client(host=f"http://{ip_address}:{TRANSLATIONS_PORT}")
 
     service_logger.info(f"Using translation model {MODEL} on ip {ip_address}")