refactor to pythonic package

.dockerignore

-test/
+tests/
 .editorconfig
 .gitignore
 .env
\ No newline at end of file

Dockerfile

@@ -4,10 +4,10 @@ WORKDIR /app
 
 COPY . ./
 
-RUN apt install ffmpeg -y
+RUN apt update && apt install ffmpeg -y
 
 RUN poetry install --no-cache --only main
 
 VOLUME [ "/data" ]
 
-CMD ["poetry", "run", "python", "src/server.py"]
+CMD ["poetry", "run", "python", "src/audio_analyzer/server.py"]
\ No newline at end of file

Makefile

@@ -44,9 +44,6 @@ install:
 test:
 	$(POETRY) pytest
 
-test-coverage:
-	$(POETRY) pytest --cov-config .coveragerc --cov-report term-missing --cov-report html --cov=src
-
 format:
 	$(POETRY) ruff check --fix ./src ./tests
 

pyproject.toml

@@ -5,6 +5,13 @@ description = "MPAI CAE-ARP Audio Analyser"
 authors = ["Matteo Spanio <dev2@audioinnova.com>"]
 license = "GPLv3"
 readme = "README.md"
+packages = [
+    { include = "audio_analyzer", from = "src" },
+    { include = "ml", from = "src" }
+]
+
+[tool.poetry.scripts]
+audio-analyser = 'audio_analyzer.cli:main'
 
 [tool.poetry.dependencies]
 python = "^3.10"

src/classifier.py → src/audio_analyzer/classifier.py

@@ -2,10 +2,10 @@ from pandas import DataFrame
 
 from mpai_cae_arp.audio import AudioWave
 from mpai_cae_arp.types.irregularity import Irregularity, IrregularityProperties
-from ml.classification import load_model, ClassificationResult
+from ml import classification as mlc
 
 
-def classification_to_irreg_properties(classification: ClassificationResult) -> IrregularityProperties:
+def classification_to_irreg_properties(classification: mlc.ClassificationResult) -> IrregularityProperties:
     return IrregularityProperties(
         reading_equalisation=classification.reading_equalization,
         reading_speed=classification.reading_speed,
@@ -19,7 +19,7 @@ def classify(audio_blocks: DataFrame) -> list[IrregularityProperties]:
     audio_blocks_classification = []
 
     # classify the audioBlocks
-    eq_classifier = load_model("pretto_and_berio_nono_classifier")
+    eq_classifier = mlc.load_model("pretto_and_berio_nono_classifier")
     prediction = eq_classifier.predict(audio_blocks)
 
     for i in range(len(prediction)):

src/cli.py → src/audio_analyzer/cli.py

@@ -2,13 +2,14 @@ import argparse
 import os
 import sys
 from rich.console import Console
-import segment_finder as sf
-import classifier as cl
 
 from mpai_cae_arp.types.irregularity import IrregularityFile, Source
 from mpai_cae_arp.files import File, FileType
 from mpai_cae_arp.io import prettify, Style
 
+from . import segment_finder as sf
+from . import classifier as cl
+
 
 def get_args() -> tuple[str | None, str | None]:
     if len(sys.argv) > 1:
@@ -27,7 +28,6 @@ def get_args() -> tuple[str | None, str | None]:
         return os.getenv("WORKING_DIRECTORY"), os.getenv("FILES_NAME")
 
 
-
 def exit_with_error(error_message: str, console) -> None:
     console.print(f"[red bold]Error: {error_message}")
     quit(os.EX_USAGE)

src/client.py → src/audio_analyzer/client.py

 from rich.console import Console
-from rich.markdown import Markdown
 import os
 
 import grpc
@@ -9,7 +8,7 @@ from mpai_cae_arp.network import arp_pb2_grpc
 channels = {
     "AudioAnalyser": grpc.insecure_channel("[::]:50051"),
     "VideoAnalyser": grpc.insecure_channel("[::]:50052"),
-    "TapeIrregularityClassifier": grpc.insecure_channel("[::]:50051/tape-irregularity-classifier"),
+    "TapeIrregularityClassifier": grpc.insecure_channel("[::]:50053"),
     "TapeAudioRestoration": grpc.insecure_channel("[::]:50051/tape-audio-restoration"),
     "Packager": grpc.insecure_channel("[::]:50051/packager"),
 }
@@ -18,15 +17,15 @@ def run(console: Console):
 
     audio_analyser = arp_pb2_grpc.AIMStub(channels["AudioAnalyser"])
     video_analyser = arp_pb2_grpc.AIMStub(channels["VideoAnalyser"])
+    tape_irreg_classifier = arp_pb2_grpc.AIMStub(channels["TapeIrregularityClassifier"])
 
     request = arp_pb2.InfoRequest()
-    for analyser in [audio_analyser, video_analyser]:
+    for analyser in [audio_analyser, video_analyser, tape_irreg_classifier]:
         response = analyser.getInfo(request)
         console.print("[bold]{}[/], v{}".format(response.title, response.version))
-        console.print(Markdown(response.description))
 
     request = arp_pb2.JobRequest(
-        working_dir="../data",
+        working_dir="/data",
         files_name="BERIO100",
         index=1,
     )
@@ -64,8 +63,19 @@ def run(console: Console):
                 exit(os.EX_SOFTWARE)
             console.print(result.message)
 
+    with console.status("[bold]Computing TapeIrregularityClassifier...", spinner="bouncingBall"):
+        for result in tape_irreg_classifier.work(request):
+            if result.status == "error":
+                console.print("[bold red]Error![/] :boom:")
+                console.print(f"[italic red]{result.message}")
+                for channel in channels.values():
+                    channel.close()
+                exit(os.EX_SOFTWARE)
+            console.print(result.message)
+
     channels["AudioAnalyser"].close()
     channels["VideoAnalyser"].close()
+    channels["TapeIrregularityClassifier"].close()
 
     console.print("[bold green]Success![/] :tada:")
 

src/server.py → src/audio_analyzer/server.py

@@ -16,8 +16,8 @@ from mpai_cae_arp.network.arp_pb2 import (
     License,
 )
 
-import segment_finder as sf
-import classifier as cl
+from . import segment_finder as sf
+from . import classifier as cl
 
 info = File('config.yml', FileType.YAML).get_content()