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Important Review skippedDraft detected. Please check the settings in the CodeRabbit UI or the You can disable this status message by setting the WalkthroughThe changes in this pull request encompass various modifications across multiple files, including the addition of new environment variables, the introduction of a new GitHub Actions workflow, and the creation of a new Dockerfile. Key updates include adjustments to the Changes
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Actionable comments posted: 21
🧹 Outside diff range and nitpick comments (17)
Makefile (1)
1-14: Clarify the environment management strategy.The Makefile introduces conda-based targets while the project appears to be transitioning between different environment management tools (poetry, conda, Docker). Consider:
- Documenting the rationale for using multiple environment management tools
- Ensuring consistent environment setup across development and production
- Providing clear migration guidelines for developers
manga_translator/translators/keys.py (2)
17-17: Clean up TODO comment.The TODO comment about replacing OPENAI_HTTP_PROXY with --proxy should be addressed as part of maintaining clean code.
Would you like me to help create an issue to track this TODO item?
Line range hint
1-33: Consider improving configuration robustness and documentation.
- The empty string defaults for API keys could lead to silent failures. Consider raising explicit exceptions when required keys are missing.
- The file contains mixed language comments (English/Chinese). Consider standardizing to English for better maintainability.
Here's a suggested pattern for more robust key handling:
def get_required_env(key: str, default: str | None = None) -> str: value = os.getenv(key, default) if not value: raise ValueError(f"Missing required environment variable: {key}") return value # Example usage: GROQ_API_KEY = get_required_env('GROQ_API_KEY') # Will raise if not set # For optional keys: OPENAI_HTTP_PROXY = os.getenv('OPENAI_HTTP_PROXY') # Can be None.github/workflows/mit-worker-image.yml (2)
3-9: Consider adding 'develop' branch to the trigger list.The workflow currently triggers on
mainandmoeflow-*branches. Since the metadata action includes tagging for the develop branch (line 43), consider adding it to the trigger list for consistency.push: branches: - main + - develop - moeflow-*
26-28: Add existence check before cleanup.The cleanup step might fail if the directory doesn't exist. Add a check to make the workflow more robust.
- run: rm -rf /opt/hostedtoolcache + run: | + if [ -d "/opt/hostedtoolcache" ]; then + rm -rf /opt/hostedtoolcache + fimanga_translator/utils/inference.py (3)
281-289: Enhance docstring to document side effects.The implementation looks good, but the docstring could be more comprehensive by mentioning that this method also triggers permission updates via
_grant_execute_permissions.Consider updating the docstring:
def _check_downloaded_map(self, map_key: str) -> bool: """Check if model file exists Args: map_key (str): key in self._MODEL_MAPPING Returns: bool: the "file" or "archive" file exists + + Note: + This method also updates file permissions on Linux systems by calling + _grant_execute_permissions if the files exist. """
Line range hint
314-326: Add error handling and logging for permission changes.While the implementation is correct, consider adding try-catch for chmod operations and logging for better observability.
Consider updating the implementation:
def _grant_execute_permissions(self, map_key: str): mapping = self._MODEL_MAPPING[map_key] if sys.platform == 'linux': # Grant permission to executables for file in mapping.get('executables', []): p = self._get_file_path(file) if os.path.basename(p) in ('', '.'): p = os.path.join(p, file) if not os.path.isfile(p): raise InvalidModelMappingException(self._key, map_key, f'File "{file}" does not exist') if not os.access(p, os.X_OK): - os.chmod(p, os.stat(p).st_mode | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH) + try: + os.chmod(p, os.stat(p).st_mode | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH) + self.logger.debug(f"Granted execute permissions to {p}") + except OSError as e: + self.logger.warning(f"Failed to set execute permissions for {p}: {e}")
Line range hint
281-326: Consider distributed environment challenges.Since this code will run in a Celery worker environment, a few architectural considerations:
- File system operations should be atomic to handle concurrent workers
- Consider adding file locking during permission changes
- Add metrics/monitoring for file operations in distributed setup
Would you like help implementing these distributed environment safeguards?
manga_translator/utils/textblock.py (1)
Line range hint
379-382: Consider cleaning up or documenting commented codeThere are large sections of commented-out code that appear to contain potentially useful implementations. If these implementations are no longer needed, consider removing them. If they are planned for future use, add TODO comments explaining the plan.
manga_translator/ocr/model_48px.py (2)
91-91: Address the FIXME comment regarding temporary directory usage.Using a hardcoded directory for OCR results can cause issues with permissions, disk space, and concurrent access. Let's implement a proper solution using temporary directories.
Would you like me to help implement a solution using
tempfile.mkdtemp()that ensures proper cleanup of temporary files? This would:
- Create a unique temporary directory for each inference
- Automatically clean up files after use
- Handle concurrent access safely
Line range hint
1-1024: Consider restructuring for Celery task worker integration.Since this PR aims to run models as Celery task workers, consider the following architectural improvements:
- The OCR inference operations are CPU-intensive and should be offloaded to Celery workers
- The async methods (
_load,_unload,_infer) perform blocking operations that could impact performanceConsider:
- Wrapping the inference logic in a Celery task
- Using proper async IO operations for file operations
- Implementing a result backend for storing OCR results instead of using the filesystem
Example Celery task structure:
from celery import shared_task @shared_task def ocr_inference_task(image_data: bytes, textlines: List[dict]) -> List[dict]: model = Model48pxOCR() # Perform inference and return results return resultsmanga_translator/translators/groq.py (2)
40-40: Fix typo in_CHAT_SYSTEM_TEMPLATEThere's a typo in the
_CHAT_SYSTEM_TEMPLATE. The word "currenly" should be "currently".Apply this diff to correct the typo:
- 'You will try to understand the context of the story by reading previous and currenly provided sentences' + 'You will try to understand the context of the story by reading previous and currently provided sentences'
41-41: Improve grammar for clarity in_CHAT_SYSTEM_TEMPLATEThe sentence structure can be improved for better readability.
Apply this diff to enhance clarity:
- 'Understand that this is being used as a manga translator, so the translation should retain some words from the original text. Like Senpai should not be translated to "senior" in this context. but kept as Senpai' + 'Understand that this is being used as a manga translator, so the translation should retain some words from the original text. For example, "Senpai" should not be translated to "senior" in this context but kept as "Senpai".'moeflow_worker.py (2)
12-13: Address the FIXME comments to improve the translator implementationThere are
FIXMEcomments suggesting the implementation of a better translator, possibly with Langchain, and the creation of a different translators package. Resolving these will enhance the translation functionality and maintainability of the code.Would you like assistance in implementing the improved translator or setting up a separate translators package? I can help with code suggestions or open a new GitHub issue to track this task.
119-119: Implementmit_inpaintfunction or handle NotImplementedError appropriatelyThe
mit_inpaintfunction currently raisesNotImplementedError. Ensure that this is intentional and consider providing a fallback mechanism or implementing the function to handle inpainting tasks.Would you like assistance in implementing the
mit_inpaintfunction or creating a placeholder that handles this case more gracefully? I can help develop the implementation or open a GitHub issue to track this task.manga_translator/manga_translator.py (2)
Line range hint
398-404: Refine exception handling during language detectionCatching the general
Exceptionmay mask unexpected errors. It's better to catch the specificLangDetectExceptionfrom thelangdetectlibrary to handle known issues while allowing unforeseen exceptions to propagate appropriately.Replace the generic exception with the specific one:
try: source_language = LANGDETECT_MAP.get(langdetect.detect(txtln.text), 'UNKNOWN') -except Exception: +except langdetect.lang_detect_exception.LangDetectException: source_language = 'UNKNOWN'
Line range hint
704-708: Add retry limit or delay to prevent potential infinite loopIn
_send_state, if an exception occurs andfinishedisTrue, thecontinuestatement will cause the loop to retry indefinitely without a delay. This could lead to an infinite loop and overload the server.Consider adding a retry limit or a delay between retries:
retry_count = 0 while retry_count < MAX_RETRIES: try: # existing code break except Exception: if finished: retry_count += 1 time.sleep(DELAY_INTERVAL) continue else: break
📜 Review details
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Review profile: CHILL
📒 Files selected for processing (21)
- .dockerignore (1 hunks)
- .env.sample (1 hunks)
- .github/workflows/mit-worker-image.yml (1 hunks)
- .gitignore (1 hunks)
- Dockerfile.moeflow_worker (1 hunks)
- Makefile (1 hunks)
- README_moeflow_worker.md (1 hunks)
- conda.yaml (1 hunks)
- manga_translator/init.py (1 hunks)
- manga_translator/detection/init.py (1 hunks)
- manga_translator/manga_translator.py (11 hunks)
- manga_translator/ocr/model_48px.py (1 hunks)
- manga_translator/ocr/model_manga_ocr.py (3 hunks)
- manga_translator/translators/init.py (4 hunks)
- manga_translator/translators/groq.py (1 hunks)
- manga_translator/translators/keys.py (1 hunks)
- manga_translator/utils/generic.py (2 hunks)
- manga_translator/utils/inference.py (1 hunks)
- manga_translator/utils/textblock.py (1 hunks)
- moeflow_worker.py (1 hunks)
- requirements.txt (1 hunks)
✅ Files skipped from review due to trivial changes (6)
- .dockerignore
- .env.sample
- README_moeflow_worker.md
- manga_translator/init.py
- manga_translator/detection/init.py
- requirements.txt
🧰 Additional context used
🪛 Ruff
manga_translator/translators/__init__.py
20-20:
.qwen2.Qwen2Translatorimported but unused; consider removing, adding to__all__, or using a redundant alias(F401)
20-20:
.qwen2.Qwen2BigTranslatorimported but unused; consider removing, adding to__all__, or using a redundant alias(F401)
manga_translator/translators/groq.py
2-2:
asyncioimported but unusedRemove unused import:
asyncio(F401)
3-3:
timeimported but unusedRemove unused import:
time(F401)
4-4:
jsonimported but unusedRemove unused import:
json(F401)
5-5:
reimported but unusedRemove unused import:
re(F401)
moeflow_worker.py
53-53: Multiple
isinstancecalls foro, merge into a single callMerge
isinstancecalls foro(SIM101)
55-58: Combine
ifbranches using logicaloroperatorCombine
ifbranches(SIM114)
🔇 Additional comments (17)
Dockerfile.moeflow_worker (2)
8-9:⚠️ Potential issueEnhance conda environment setup reliability
The conda environment setup lacks error handling and version pinning:
- Add environment activation verification
- Add error handling for the preparation script
- Consider pinning conda version
-RUN --mount=type=cache,target=/root/.conda conda env update --prune --file conda.yaml -RUN conda run -n mit-py311 --no-capture-output python3 docker_prepare.py +RUN --mount=type=cache,target=/root/.conda \ + conda env update --prune --file conda.yaml && \ + conda run -n mit-py311 python3 -c "import sys; sys.exit(0)" || exit 1 + +RUN conda run -n mit-py311 --no-capture-output python3 docker_prepare.py || \ + (echo "Model preparation failed" && exit 1)Additionally, there's a potential conflict between the PR objectives mentioning poetry.yaml and the usage of conda.yaml here.
#!/bin/bash # Check for poetry related files echo "Checking for poetry files..." fd -t f "poetry\.(lock|yaml|toml)$"
1-2: Verify CUDA support requirementsThe switch from
pytorch/pytorch:2.2.2-cuda12.1-cudnn8-runtimetocontinuumio/miniconda3removes built-in CUDA support. Ensure that the model inference doesn't require GPU acceleration, or if it does, verify that CUDA dependencies are properly installed through conda.yaml.✅ Verification successful
CUDA support is properly configured in conda environment
The switch from
pytorch/pytorchtocontinuumio/miniconda3base image is safe. The conda.yaml explicitly includes all necessary CUDA dependencies:
- PyTorch 2.2.2 with CUDA 12.1 support (
pytorch=2.2.2=py3.11_cuda12.1_cudnn8.9.2_0)- CUDA runtime components (
cuda-runtime=12.1.0)- CUDA libraries and CUDNN
- Proper channels configuration including
pytorchandnvidia🏁 Scripts executed
The following scripts were executed for the analysis:
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#!/bin/bash # Check if CUDA dependencies are specified in conda.yaml rg -i "cuda|cudnn|pytorch.*cuda" conda.yamlLength of output: 104
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#!/bin/bash # First, let's check if conda.yaml exists and see its contents fd -t f "conda.yaml" echo "---" fd -t f "conda.yaml" -x cat {} # Also check for any other environment files that might contain PyTorch/CUDA deps fd -t f "(environment|requirements)\.(yaml|yml)$" echo "---" fd -t f "(environment|requirements)\.(yaml|yml)$" -x cat {}Length of output: 3797
Makefile (2)
12-13: Clarify model preparation process and add error handling.The target runs
docker_prepare.pybut provides no feedback or error handling. Consider:
- Adding a description of what models are being prepared
- Implementing status output and error handling
- Clarifying why this runs in conda despite the Docker-related name
#!/bin/bash # Check the docker preparation script if [ -f "docker_prepare.py" ]; then echo "Content of docker_prepare.py:" cat docker_prepare.py else echo "Warning: docker_prepare.py not found" fi
6-7: Verify conda environment setup requirements.The target assumes conda and conda.yaml are available. Consider adding checks and better error handling.
.github/workflows/mit-worker-image.yml (2)
16-24: LGTM! Well-configured job permissions.The job configuration follows security best practices by explicitly defining minimal required permissions.
46-57: 🛠️ Refactor suggestionVerify Docker build configuration.
Several improvements could enhance the build step:
- The Dockerfile path needs to be verified
- Consider adding build arguments for version/commit information
- Add error handling for the build step
Consider adding build arguments for better traceability:
with: context: . file: ./Dockerfile.moeflow_worker platforms: linux/amd64 push: true provenance: false sbom: false tags: ${{ steps.docker_meta.outputs.tags }} cache-from: type=gha,scope=mit-worker-x64 cache-to: type=gha,mode=max,scope=mit-worker-x64 + build-args: | + COMMIT_SHA=${{ github.sha }} + VERSION=${{ steps.docker_meta.outputs.version }}✅ Verification successful
Docker build configuration is valid but can be enhanced
The verification shows:
- Dockerfile.moeflow_worker exists and uses conda for environment management
- No poetry files are present, confirming conda is the package manager
- No existing build-args in the workflow
While the basic configuration is correct, the suggested improvements for build arguments would indeed enhance traceability. The original review's concerns about Dockerfile existence and poetry conflicts are resolved, but the build-args suggestion remains valuable.
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#!/bin/bash # Description: Verify the existence of Dockerfile and related configurations # Test 1: Check if Dockerfile exists if [ ! -f "./Dockerfile.moeflow_worker" ]; then echo "Error: Dockerfile.moeflow_worker not found" exit 1 fi # Test 2: Check for poetry.yaml mentioned in PR objectives if [ -f "poetry.yaml" ]; then echo "Warning: poetry.yaml exists and might conflict with packages" fiLength of output: 69
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conda.yaml (2)
1-5: Environment configuration looks good but verify CUDA compatibility.The environment is properly named and includes necessary channels for PyTorch and NVIDIA support.
Let's verify CUDA compatibility across dependencies:
✅ Verification successful
CUDA versions are consistent across the environment configuration
The verification shows that:
- The conda environment uses CUDA 12.1 consistently across core CUDA packages (cuda-runtime, cuda-libraries, cuda-cudart, etc.)
- PyTorch is properly configured with CUDA 12.1 (pytorch-cuda=12.1)
- The only exception is cuda-opencl=12.4.127 which is not a concern as it's a separate OpenCL implementation
- The codebase properly handles CUDA device availability checks and is compatible with the specified CUDA version
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1-132: Clarify package manager strategy.The PR objectives mention creating a "clean and working
poetry.yaml", but this PR introduces aconda.yamlinstead. This raises some concerns:
- Are we switching from Poetry to Conda intentionally?
- Will this cause conflicts with existing Poetry dependencies?
- How will this affect the development workflow?
Let's check for existing Poetry configuration:
Consider:
- Document the rationale for using Conda instead of Poetry
- Ensure this aligns with the team's package management strategy
- Update the PR description to reflect this change in approach
manga_translator/ocr/model_manga_ocr.py (2)
150-152: LGTM! Improved error handling for args dictionary.The use of
args.get('use_mocr_merge', False)instead of direct dictionary access prevents KeyError exceptions and provides a sensible default.
29-29: Verify callers of merge_bboxes function.The function signature change to include return type annotation improves type safety. However, this is a breaking change that requires verification of all callers.
✅ Verification successful
Let me analyze the results from the
rgcommand to verify the impact of themerge_bboxesfunction signature change.The search results show that
merge_bboxesis:
- Defined in
manga_translator/ocr/model_manga_ocr.py- Called only once within the same file, where both return values are properly unpacked
Let me verify if this function is exported or used in other ways:
Let me check if the function is exposed through
__all__or if the module itself is imported:
Function signature change is safe to proceed
The
merge_bboxesfunction is:
- Only called once within its own file
- Not exposed through
__all__- Not imported directly in any other files
- The only module import is for the class
ModelMangaOCR, not the functionThe change in return type annotation is localized and doesn't impact any other parts of the codebase.
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#!/bin/bash # Search for all callers of merge_bboxes rg "merge_bboxes\(" --type pyLength of output: 330
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#!/bin/bash # Check if merge_bboxes is in __all__ and if the module is imported echo "=== Checking __all__ in model_manga_ocr.py ===" rg "__all__.*=.*" manga_translator/ocr/model_manga_ocr.py echo -e "\n=== Checking imports of model_manga_ocr ===" rg "from.*model_manga_ocr import|import.*model_manga_ocr" --type pyLength of output: 411
manga_translator/utils/textblock.py (1)
45-45: LGTM: Improved type safety with specific type annotationThe updated type annotation
List[Tuple[int, int, int, int]]clearly defines the expected structure of thelinesparameter, making it easier to catch type-related errors during development.manga_translator/utils/generic.py (1)
68-68: LGTM: Type hints accurately reflect the function's behavior.The updated signature
atoi(text: str) -> int | strcorrectly specifies that the function accepts a string and returns either an integer or string.moeflow_worker.py (3)
88-88: Clarify the comment regardingasync_ocrreturn valueThe comment mentions an unknown reason for
async_ocrreturning[[Quad]]instead of[result]. Investigate the cause to ensure the function behaves as expected, and update the comment to reflect any findings.I recommend checking the return type of
async_ocrand ensuring it aligns with the expected data structure. This will help prevent unexpected behaviors in downstream processing.
185-197: Ensure proper handling of asynchronous translationThe
async_translatefunction usesawaitwith the translator's methods and returns the result. Confirm that the translator supports asynchronous operations and that theAwaitablereturn type is correctly handled when using@async_to_sync.Please verify that the translator's methods are compatible with the asynchronous context and that the result is correctly returned after synchronization.
93-99:⚠️ Potential issueVerify the order of image dimensions when unpacking
shapeIn NumPy, the shape of an image array is typically
(height, width, channels). Ensure thatimg_handimg_ware assigned correctly to prevent dimension mismatches in subsequent processing.Consider swapping
img_wandimg_h:- img_w, img_h, *_rest = load_rgb_image(path_or_url).shape + img_h, img_w, *_rest = load_rgb_image(path_or_url).shapeThis adjustment ensures that
widthandheightcorrespond to the correct dimensions when passed toasync_textline_merge.manga_translator/manga_translator.py (2)
121-121: Enhanced type annotations intranslate_pathmethodUpdating the
paramsparameter type fromdicttodict[str, Union[int, str]]improves type safety and clarifies the expected structure of the arguments.
225-225: Added return type annotation to_translate_fileSpecifying the return type as
-> boolenhances code readability and aids in static type checking, ensuring that the method's purpose is clear.
.gitignore
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| @@ -38,5 +38,5 @@ share/python-wheels/ | |||
| .installed.cfg | |||
| *.egg | |||
| MANIFEST | |||
| venv | |||
There was a problem hiding this comment.
💡 Codebase verification
Virtual environment patterns need consolidation in both .gitignore and .dockerignore
The verification confirms scattered virtual environment patterns that should be consolidated:
.gitignorehas related patterns spread across lines 16-20, 36, and 41.dockerignorehas a singlevenventry at line 10
For better maintainability, update both files:
- In
.gitignore, consolidate under a single "Virtual Environment" section:
# Virtual Environment
venv/
.venv/
ENV/
env/
pyvenv.cfg
Scripts/
Lib/
include/
share/
- In
.dockerignore, align with the same patterns:
# Virtual Environment
venv/
.venv/
ENV/
env/
🔗 Analysis chain
LGTM! Consider consolidating virtual environment patterns.
The change to use venv is more flexible than /venv and aligns well with Poetry usage. However, there are multiple virtual environment-related patterns scattered in the file (pyvenv.cfg, Scripts, Lib, etc.).
Consider consolidating all virtual environment patterns under a single section for better maintainability:
-pyvenv.cfg
-Scripts
-Lib
-include
-share
-venv
+# Virtual Environment
+venv/
+.venv/
+ENV/
+env/
+pyvenv.cfgLet's verify there are no untracked virtual environment directories:
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Dockerfile.moeflow_worker
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| RUN apt update \ | ||
| && DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get -y install tzdata build-essential python3-opencv |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Optimize package installation layer
The package installation layer can be optimized to reduce image size:
-RUN apt update \
- && DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get -y install tzdata build-essential python3-opencv
+RUN apt-get update && \
+ DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get install -y --no-install-recommends \
+ tzdata \
+ build-essential \
+ python3-opencv && \
+ apt-get clean && \
+ rm -rf /var/lib/apt/lists/*📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| RUN apt update \ | |
| && DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get -y install tzdata build-essential python3-opencv | |
| RUN apt-get update && \ | |
| DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt-get install -y --no-install-recommends \ | |
| tzdata \ | |
| build-essential \ | |
| python3-opencv && \ | |
| apt-get clean && \ | |
| rm -rf /var/lib/apt/lists/* |
Dockerfile.moeflow_worker
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| WORKDIR /app | ||
| COPY . /app |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Optimize file copying strategy
The current COPY command copies all files including potentially unnecessary ones.
- Add appropriate entries to .dockerignore to exclude unnecessary files
- Consider a multi-stage build pattern
- Copy only required files:
-COPY . /app
+COPY conda.yaml docker_prepare.py ./
+COPY src/ ./src/Committable suggestion was skipped due to low confidence.
Makefile
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| .PHONY: default | ||
|
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||
| default: | ||
| @echo Please use other targets | ||
|
|
There was a problem hiding this comment.
🛠️ Refactor suggestion
Enhance the default target's help message.
While having a default target is good practice, consider making it more helpful by listing available targets and their descriptions.
.PHONY: default
default:
- @echo Please use other targets
+ @echo "Available targets:"
+ @echo " conda-env - Update conda environment from conda.yaml"
+ @echo " run-worker - Run Celery worker for MIT tasks"
+ @echo " prepare-models - Prepare models for docker environment"
+ @echo " build-image - Build the MIT docker image"
+ @echo " run-web-server - Run the web server in docker"Committable suggestion was skipped due to low confidence.
| run-worker: | ||
| conda run -n mit-py311 --no-capture-output celery --app moeflow_worker worker --queues mit --loglevel=debug --concurrency=1 |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Consider making worker configuration more flexible.
The worker configuration has several hard-coded values that could benefit from being configurable:
- Concurrency level is fixed at 1
- Queue name is hard-coded
- Log level is fixed at debug
run-worker:
- conda run -n mit-py311 --no-capture-output celery --app moeflow_worker worker --queues mit --loglevel=debug --concurrency=1
+ conda run -n mit-py311 --no-capture-output celery \
+ --app moeflow_worker \
+ worker \
+ --queues $${CELERY_QUEUE:-mit} \
+ --loglevel=$${LOG_LEVEL:-info} \
+ --concurrency=$${WORKER_CONCURRENCY:-1}📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| run-worker: | |
| conda run -n mit-py311 --no-capture-output celery --app moeflow_worker worker --queues mit --loglevel=debug --concurrency=1 | |
| run-worker: | |
| conda run -n mit-py311 --no-capture-output celery \ | |
| --app moeflow_worker \ | |
| worker \ | |
| --queues $${CELERY_QUEUE:-mit} \ | |
| --loglevel=$${LOG_LEVEL:-info} \ | |
| --concurrency=$${WORKER_CONCURRENCY:-1} |
|
|
||
| def to_json(value: object) -> Any: | ||
| """ | ||
| :return: a json-serizable deep clone of `value` |
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Correct typo in docstring: 'json-serizable' → 'json-serializable'
There's a typo in the docstring of the to_json function. Fixing it will improve code documentation clarity.
Apply this diff to correct the typo:
- :return: a json-serizable deep clone of `value`
+ :return: a json-serializable deep clone of `value`📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| :return: a json-serizable deep clone of `value` | |
| :return: a json-serializable deep clone of `value` |
moeflow_worker.py
Outdated
| elif isinstance(o, list): | ||
| return o | ||
| elif isinstance(o, str): | ||
| return o |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Combine similar isinstance checks to streamline the code
The isinstance checks for list and str can be combined to simplify the conditional statements.
Apply this diff to merge the checks:
- elif isinstance(o, list):
- return o
- elif isinstance(o, str):
- return o
+ elif isinstance(o, (list, str)):
+ return o📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| elif isinstance(o, list): | |
| return o | |
| elif isinstance(o, str): | |
| return o | |
| elif isinstance(o, (list, str)): | |
| return o |
🧰 Tools
🪛 Ruff
55-58: Combine
ifbranches using logicaloroperatorCombine
ifbranches(SIM114)
moeflow_worker.py
Outdated
| lambda r: len(r.text) > min_text_length and utils_generic.is_valuable_text(r.text), | ||
| text_blocks_all) | ||
|
|
||
| return to_json(text_blocks) |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Convert filter object to list before serialization
The filter object in text_blocks should be converted to a list before passing it to to_json for serialization. This ensures compatibility and prevents potential issues during JSON encoding.
Apply this diff to convert the filter object:
- return to_json(text_blocks)
+ return to_json(list(text_blocks))📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| lambda r: len(r.text) > min_text_length and utils_generic.is_valuable_text(r.text), | |
| text_blocks_all) | |
| return to_json(text_blocks) | |
| lambda r: len(r.text) > min_text_length and utils_generic.is_valuable_text(r.text), | |
| text_blocks_all) | |
| return to_json(list(text_blocks)) |
moeflow_worker.py
Outdated
| 'prob': o.prob, | ||
| 'textlines': self.default(o.textlines), | ||
| } | ||
| elif isinstance(o, filter) or isinstance(o, tuple): |
There was a problem hiding this comment.
🛠️ Refactor suggestion
Simplify multiple isinstance checks into a single call
To improve code readability and efficiency, you can merge the multiple isinstance checks for o.
Apply this diff to simplify the check:
- elif isinstance(o, filter) or isinstance(o, tuple):
+ elif isinstance(o, (filter, tuple)):📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| elif isinstance(o, filter) or isinstance(o, tuple): | |
| elif isinstance(o, (filter, tuple)): |
🧰 Tools
🪛 Ruff
53-53: Multiple
isinstancecalls foro, merge into a single callMerge
isinstancecalls foro(SIM101)
manga_translator/manga_translator.py
Outdated
| @@ -488,7 +493,7 @@ async def _run_detection(self, ctx: Context): | |||
| self.device, self.verbose) | |||
|
|
|||
| async def _run_ocr(self, ctx: Context): | |||
| textlines = await dispatch_ocr(ctx.ocr, ctx.img_rgb, ctx.textlines, ctx, self.device, self.verbose) | |||
| textlines: list[Quadrilateral] = await dispatch_ocr(ctx.ocr, ctx.img_rgb, ctx.textlines, ctx, self.device, self.verbose) | |||
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Ensure Python version compatibility with type hints
The use of type hinting syntax list[Quadrilateral] is only supported in Python 3.9 and above. If the codebase needs to maintain compatibility with earlier Python versions (3.8 or below), consider importing List from the typing module and using List[Quadrilateral] instead.
Modify the type hint to maintain compatibility:
-from .utils.generic import Quadrilateral
+from typing import List
from .utils.generic import Quadrilateral
-textlines: list[Quadrilateral] = await dispatch_ocr(...)
+textlines: List[Quadrilateral] = await dispatch_ocr(...)Committable suggestion was skipped due to low confidence.
Switch docker base image
…ines defining an arg in the constructor might be easier than catching the error
Fix Dockerfile
gradio UI: export to moeflow
moeflow-com/moeflow#5
Summary by CodeRabbit
Release Notes
New Features
GroqTranslatorclass for translations using the GROQ API..env.sample.Improvements
Bug Fixes
Chores
.dockerignoreand.gitignoreto streamline ignored files.groqas a new dependency inrequirements.txt.