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Co-authored-by: MasumRab <8943353+MasumRab@users.noreply.github.com>
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🤖 Hi @MasumRab, I've received your request, and I'm working on it now! You can track my progress in the logs for more details. |
WalkthroughThis pull request is primarily a large-scale code formatting and linting pass across the entire codebase. It standardizes string quotes (single-to-double), reformats function signatures and calls to multi-line layouts, adds trailing commas, updates file endings, and introduces Ruff linting configuration. Additionally, there are minor functional adjustments including sentiment classification logic refinement, workflow engine memory-optimization refactoring, test function signature changes, and configuration updates to the CI pipeline and package dependencies. Changes
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~25 minutes The vast majority of changes are homogeneous formatting updates (multi-line layouts, quote normalization, trailing commas) repeated across many files, which reduces cognitive load per file. However, the sheer scope (100+ files), scattered functional changes in Possibly related PRs
Poem
✨ Finishing Touches📝 Generate docstrings
🧪 Generate unit tests (beta)
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| # Atomic write | ||
| temp_path = file_path.with_suffix(".tmp") | ||
| with open(temp_path, 'w') as f: | ||
| with open(temp_path, "w") as f: |
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| with open(file_path, "r") as f: | ||
| return json.load(f) | ||
| except Exception as e: | ||
| logger.error(f"Failed to retrieve item {key}: {e}") |
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In general, to fix uncontrolled data in path expressions you should (a) strictly validate or normalize the user-controlled portion before using it in a path, and (b) ensure the resulting path is constrained to an expected directory. Validation can be done by restricting to a known-safe pattern (for example, alphanumeric IDs) or checking against an allow list; normalization should use Path.resolve()/os.path.realpath and a prefix check when arbitrary subdirectories are allowed.
In this codebase, the best targeted fix without changing functionality is:
-
Add stricter validation of the
workflow_idcoming from the UI so that only IDs that correspond to actual stored workflows can be passed intostart_workflow. We can derive the list of valid workflow IDs fromclient.list_workflows()and present them in a dropdown, then have the backend double-check the requested ID is among these known IDs. This avoids arbitraryworkflow_idvalues reachingretrieve_item. -
Keep the existing
safe_keylogic inpersist_item/retrieve_item, which already strips path separators and dots, ensuring files remain underDATA_DIR. The primary additional safety comes from constraining keys at the UI / workflow boundary.
Concretely:
- In
modules/new_ui/app.py, adjust the workflow tab so that:- We compute a list of available workflows and their IDs at startup.
- Replace the free-text
gr.Textboxforworkflow_selectwith agr.Dropdown(or at least validate the value against known IDs inrun_workflow_on_email). - Add a small helper to build a mapping of workflow IDs to their display labels, based on
client.list_workflows().
- In
run_workflow_on_email, enforce that the incomingworkflow_idis one of the known IDs; if not, return an error without callingstart_workflow.
All changes are restricted to the provided snippets in modules/new_ui/app.py. backend_adapter.py’s file access is left structurally the same, but now the keys that reach it are constrained to an allow list of existing workflows, addressing the dataflow alerts.
| @@ -15,6 +15,21 @@ | ||
| # Initialize Backend Client | ||
| client = BackendClient() | ||
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| # Cached list of available workflow IDs for validation | ||
| def _get_available_workflow_ids() -> list[str]: | ||
| """Return a list of workflow IDs currently stored in the backend.""" | ||
| try: | ||
| workflows = client.list_workflows() | ||
| ids = [] | ||
| for wf in workflows: | ||
| wf_id = wf.get("id") | ||
| if isinstance(wf_id, str) and wf_id: | ||
| ids.append(wf_id) | ||
| return ids | ||
| except Exception as e: | ||
| logger.error(f"Failed to load available workflow IDs: {e}") | ||
| return [] | ||
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| # ============== UI Functions ============== | ||
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| @@ -156,6 +171,14 @@ | ||
| if not workflow_id: | ||
| return {"error": "Please select a workflow"}, "❌ No workflow selected" | ||
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| # Validate that the requested workflow_id is one of the known workflow IDs | ||
| available_ids = _get_available_workflow_ids() | ||
| if workflow_id not in available_ids: | ||
| return ( | ||
| {"error": f"Invalid workflow_id '{workflow_id}'"}, | ||
| "❌ Invalid workflow selected", | ||
| ) | ||
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| email_data = {"subject": subject, "content": content} | ||
| payload = {"workflow_id": workflow_id, "email_data": email_data} | ||
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| @@ -322,9 +345,11 @@ | ||
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| with gr.Column(): | ||
| gr.Markdown("### Run Workflow on Email") | ||
| # Note: Choices would need to be dynamic in a real app, updated by backend | ||
| workflow_select = gr.Textbox( | ||
| label="Workflow ID", placeholder="e.g. workflow_1" | ||
| # Workflow ID is selected from available workflows to avoid arbitrary IDs | ||
| workflow_select = gr.Dropdown( | ||
| label="Workflow ID", | ||
| choices=_get_available_workflow_ids(), | ||
| value=None, | ||
| ) | ||
| wf_subject = gr.Textbox( | ||
| label="Email Subject", placeholder="Test subject..." |
| # Remove script tags | ||
| html_content = re.sub(r'<script[^>]*>.*?</script>', '', html_content, flags=re.DOTALL | re.IGNORECASE) | ||
| html_content = re.sub( | ||
| r"<script[^>]*>.*?</script>", "", html_content, flags=re.DOTALL | re.IGNORECASE |
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In general, the problem is that the custom regular expression for removing <script> blocks assumes the closing tag is exactly </script> with no trailing spaces or attributes. Browser parsers are more lenient and can treat </script > or even </script foo="bar"> as valid closing tags, so the regex must be broadened, or, preferably, a dedicated HTML sanitization library should be used instead of hand-written regexes.
The best fix here, without changing the overall functionality, is to stop using a brittle regex to strip script/style tags and instead rely on a well-tested HTML sanitization library. In Python, bleach is a widely used and well-maintained sanitizer built on html5lib. We can import bleach and configure it to allow no tags and no attributes, which will strip all HTML tags, including <script>, <style>, and event handlers, thereby achieving a safer version of what sanitize_html aims to do. This avoids having to maintain multiple complex regexes and ensures coverage of browser corner cases. Concretely, in modules/new_ui/utils.py, we should:
- Add an import for
bleachalongside the existing imports. - Replace the body of
sanitize_htmlso that it:- Returns
""when given falsy input (preserving current behavior). - Uses
bleach.cleanwithtags=[],attributes={},protocols=[], andstrip=Trueto remove all HTML tags and unsafe constructs, then returns the result as a string.
- Returns
No other functions or signatures need to change.
| @@ -6,8 +6,8 @@ | ||
| import hashlib | ||
| from typing import List, Dict, Any, Optional, Union | ||
| from datetime import datetime, timezone | ||
| import bleach | ||
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| def clean_text(text: Optional[str]) -> str: | ||
| """Clean and normalize text for analysis""" | ||
| if not text: | ||
| @@ -166,19 +165,14 @@ | ||
| if not html_content: | ||
| return "" | ||
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| # Remove script tags | ||
| html_content = re.sub( | ||
| r"<script[^>]*>.*?</script>", "", html_content, flags=re.DOTALL | re.IGNORECASE | ||
| # Use bleach to safely strip all HTML tags and attributes | ||
| # This removes <script>, <style>, event handlers, and any other markup. | ||
| cleaned = bleach.clean( | ||
| html_content, | ||
| tags=[], | ||
| attributes={}, | ||
| protocols=[], | ||
| strip=True, | ||
| ) | ||
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| # Remove style tags | ||
| html_content = re.sub( | ||
| r"<style[^>]*>.*?</style>", "", html_content, flags=re.DOTALL | re.IGNORECASE | ||
| ) | ||
|
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| # Remove event handlers | ||
| html_content = re.sub( | ||
| r'\s+on\w+\s*=\s*["\'][^"\']*["\']', "", html_content, flags=re.IGNORECASE | ||
| ) | ||
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| return html_content | ||
| return cleaned |
| @@ -1,4 +1,5 @@ | ||
| gradio | ||
| bleach==6.3.0 | ||
| plotly | ||
| psutil | ||
| pandas |
| Package | Version | Security advisories |
| bleach (pypi) | 6.3.0 | None |
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Code Review
This pull request performs a comprehensive reformatting of the entire codebase to standardize indentation, argument wrapping, and quote usage. Beyond these stylistic changes, it introduces a new Ruff configuration, downgrades ESLint in package.json to version 8.57.0, and refactors the workflow engine's memory management logic by optimizing the cleanup schedule calculation and adding a dedicated node context builder. As there were no review comments provided, I have no additional feedback to offer.
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Actionable comments posted: 18
Note
Due to the large number of review comments, Critical, Major severity comments were prioritized as inline comments.
Caution
Some comments are outside the diff and can’t be posted inline due to platform limitations.
⚠️ Outside diff range comments (6)
src/core/model_registry.py (1)
461-468:⚠️ Potential issue | 🔴 CriticalFix lock re-entry and freed-memory accounting in
optimize_memory.At Line 462,
optimize_memory()callsunload_model()while already holdingself._model_lock, which can deadlock becauseunload_model()also acquires the same lock. Also, freed-memory values are read after unload, so totals can be wrong.Suggested fix
async def optimize_memory(self) -> Dict[str, Any]: @@ - async with self._model_lock: + async with self._model_lock: current_time = time.time() models_to_unload = [] @@ - # Unload selected models - for model_id in models_to_unload: - if await self.unload_model(model_id): - instance = self._loaded_models.get(model_id) - if instance: - optimization_results["freed_memory"] += instance.memory_usage - optimization_results["freed_gpu_memory"] += ( - instance.gpu_memory_usage - ) - optimization_results["unloaded_models"].append(model_id) + # Snapshot memory before unload while lock is held + unload_snapshots = { + model_id: ( + self._loaded_models[model_id].memory_usage, + self._loaded_models[model_id].gpu_memory_usage, + ) + for model_id in models_to_unload + if model_id in self._loaded_models + } + + # Unload outside the lock to avoid re-entrant lock acquisition + for model_id in models_to_unload: + if await self.unload_model(model_id): + mem_usage, gpu_mem_usage = unload_snapshots.get(model_id, (0, 0)) + optimization_results["freed_memory"] += mem_usage + optimization_results["freed_gpu_memory"] += gpu_mem_usage + optimization_results["unloaded_models"].append(model_id) + + async with self._model_lock: + # Update current usage + total_memory = sum( + inst.memory_usage for inst in self._loaded_models.values() + ) + total_gpu_memory = sum( + inst.gpu_memory_usage for inst in self._loaded_models.values() + ) - # Update current usage - total_memory = sum( - inst.memory_usage for inst in self._loaded_models.values() - ) - total_gpu_memory = sum( - inst.gpu_memory_usage for inst in self._loaded_models.values() - ) - optimization_results["current_memory_usage"] = total_memory optimization_results["current_gpu_memory_usage"] = total_gpu_memory🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@src/core/model_registry.py` around lines 461 - 468, optimize_memory currently calls unload_model while holding self._model_lock and reads freed-memory after the unload, causing potential deadlock and incorrect accounting; fix it by (1) while holding self._model_lock, collect the list of model_ids to unload and snapshot each instance's memory_usage and gpu_memory_usage from self._loaded_models into a local map, then release self._model_lock, (2) iterate the collected model_ids and call await self.unload_model(model_id) without holding the lock, and (3) when an unload succeeds, use the previously snapped memory values to increment optimization_results["freed_memory"] and optimization_results["freed_gpu_memory"] (referencing optimize_memory, unload_model, self._model_lock, self._loaded_models, models_to_unload, and optimization_results).modules/auth/routes.py (1)
259-267:⚠️ Potential issue | 🟠 MajorUnused registration payload drops intended user attributes.
This dict is constructed and immediately discarded, so
role,permissions, and MFA defaults from request data are not persisted via this endpoint.💡 Suggested fix
async def register(user_data: UserCreate, db: DataSource = Depends(get_data_source)): """Register a new user""" - { - "username": user_data.username, - "hashed_password": hash_password(user_data.password), - "role": user_data.role, - "permissions": user_data.permissions, - "mfa_enabled": False, - "mfa_secret": None, - "mfa_backup_codes": [], - } + user_record = { + "username": user_data.username, + "hashed_password": hash_password(user_data.password), + "role": user_data.role, + "permissions": user_data.permissions, + "mfa_enabled": False, + "mfa_secret": None, + "mfa_backup_codes": [], + } + await db.create_user(user_record) - success = await create_user(user_data.username, user_data.password, db) + success = True🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@modules/auth/routes.py` around lines 259 - 267, The registration payload dict containing role, permissions, and MFA defaults is being constructed then discarded (so user attributes aren't persisted); update the registration handler (the code building the dict with user_data.username, hash_password(user_data.password), user_data.role, user_data.permissions, mfa_enabled/mfa_secret/mfa_backup_codes) to pass that payload into the actual user creation/persistence function (e.g., create_user, save_user, or the ORM model constructor used in this route) instead of throwing it away, ensuring role, permissions and MFA defaults are stored; also validate/normalize user_data.role and user_data.permissions before saving and remove any dead/unreachable code that leaves the dict unused.src/backend/python_backend/filter_routes.py (1)
19-20:⚠️ Potential issue | 🔴 CriticalAvoid import-time
SmartFilterManager()construction (currently breaking CI).Line 19 eagerly instantiates
SmartFilterManager, and pipeline logs show this crashes during import-time test collection. Move construction behind a dependency/provider function so module import is side-effect free.Proposed fix
+from functools import lru_cache ... -filter_manager = SmartFilterManager() +@lru_cache(maxsize=1) +def get_filter_manager() -> SmartFilterManager: + return SmartFilterManager()async def get_filters( - request: Request, current_user: str = Depends(get_current_active_user) + request: Request, + current_user: str = Depends(get_current_active_user), + filter_manager: SmartFilterManager = Depends(get_filter_manager), ):🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@src/backend/python_backend/filter_routes.py` around lines 19 - 20, The module currently constructs SmartFilterManager() at import time (filter_manager = SmartFilterManager()), which causes failures during test collection; change this to a lazy/provider pattern by removing the eager instantiation and adding a function (e.g., get_filter_manager or provide_filter_manager) that returns a singleton SmartFilterManager instance (create on first call and cache it) so imports have no side effects; update any call-sites that reference the module-level filter_manager to call the provider function instead, leaving PerformanceMonitor usage unchanged unless it also needs lazy construction.src/core/data/repository.py (1)
247-255:⚠️ Potential issue | 🔴 CriticalFix cache invalidation calls that reference undefined attributes.
In
create_categoryandupdate_email_by_message_id,self._dashboard_cache/self._category_breakdown_cacheare cleared, but those attributes are never initialized. This can raiseAttributeErroron mutation flows.🛠️ Proposed fix
async def create_category( self, category_data: Dict[str, Any] ) -> Optional[Dict[str, Any]]: """Creates a new category.""" - # Invalidate cache when data changes - async with self._cache_lock: - self._dashboard_cache.clear() - self._category_breakdown_cache.clear() + await self._invalidate_dashboard_cache() return await self.repository.create_category(category_data) @@ async def update_email_by_message_id( self, message_id: str, update_data: Dict[str, Any] ) -> Optional[Dict[str, Any]]: """Updates an email by its message ID.""" - # Invalidate cache when data changes - async with self._cache_lock: - self._dashboard_cache.clear() - self._category_breakdown_cache.clear() + await self._invalidate_dashboard_cache() return await self.repository.update_email_by_message_id(message_id, update_data)Also applies to: 272-275
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@src/core/data/repository.py` around lines 247 - 255, The cache invalidation in create_category and update_email_by_message_id references attributes self._dashboard_cache and self._category_breakdown_cache that aren’t initialized; either initialize these caches (e.g., empty dicts or appropriate cache objects) in the class constructor ( __init__ ) alongside self._cache_lock, or defensively check hasattr(self, "_dashboard_cache") and hasattr(self, "_category_breakdown_cache") (or use self.__dict__.get(...)) before calling .clear() so AttributeError cannot be raised; update the class that contains create_category/update_email_by_message_id to add these cache attributes during initialization or add the existence checks before clearing.src/core/smart_filter_manager.py (1)
786-874:⚠️ Potential issue | 🟠 MajorRefactor
apply_filters_to_emailto reduce complexity and satisfy the quality gate.The function currently exceeds the allowed cognitive complexity threshold (Sonar). Extracting filter-match evaluation and action-application into helpers should bring it below the limit while preserving behavior.
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@src/core/smart_filter_manager.py` around lines 786 - 874, The apply_filters_to_email method is too complex; extract the per-filter logic into two small helpers to reduce cognitive complexity: implement _evaluate_filter_match(filter_obj, email_context) which calls the existing _apply_filter_to_email and returns a bool, and _apply_actions_for_filter(filter_obj, summary, matched_filters) which performs the actions recording (adding to summary["filters_matched"], appending categories/actions, and appending to matched_filters) and returns nothing; then simplify the loop in apply_filters_to_email to call these helpers inside the existing try/except (preserving the same error logging using create_error_context/log_error and self.logger.warning), keep the later batch update and caching calls unchanged, and ensure email_data["last_filtered_at"] is still set—this refactor preserves behavior while delegating evaluation and action application to _evaluate_filter_match and _apply_actions_for_filter to lower complexity.src/backend/python_nlp/smart_filters.py (1)
120-120:⚠️ Potential issue | 🔴 CriticalUse a valid
PathValidatorAPI to prevent init-time crash.Line 120 calls
PathValidator.validate_database_path(...), but CI shows this attribute does not exist, soSmartFilterManagerfails during construction. Switch to the existing validator method used elsewhere in core code.🛠️ Proposed fix
- self.db_path = str(PathValidator.validate_database_path(db_path, DATA_DIR)) + validated_path = PathValidator.validate_and_resolve_db_path(db_path) + self.db_path = str(validated_path)🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@src/backend/python_nlp/smart_filters.py` at line 120, The code in SmartFilterManager's constructor calls a non-existent PathValidator.validate_database_path which causes init-time crashes; replace that call with the existing validator used elsewhere (PathValidator.validate_path) while keeping the same arguments and cast to str (i.e. set self.db_path = str(PathValidator.validate_path(db_path, DATA_DIR))). Update the call site in SmartFilterManager to use PathValidator.validate_path so the correct API is used.
🟡 Minor comments (4)
src/backend/python_backend/tests/test_model_manager.py-44-47 (1)
44-47:⚠️ Potential issue | 🟡 MinorInconsistent assertion formatting violates the established style.
All other assertions in this file use single-line format (lines 41-43, 54, 78-79, 111-112), including assertions of similar or greater length (e.g., line 79). The multi-line parenthesized format here is inconsistent with the codebase's existing style.
♻️ Reformat to match existing single-line assertion style
- assert ( - model_manager._model_metadata["sentiment-test"]["module"] - == "test.sentiment_module" - ) + assert model_manager._model_metadata["sentiment-test"]["module"] == "test.sentiment_module"As per coding guidelines: "All code changes must strictly adhere to the existing coding style, formatting, and conventions of the repository by analyzing surrounding code to match its style."
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@src/backend/python_backend/tests/test_model_manager.py` around lines 44 - 47, The assertion using a multi-line parenthesized format should be reformatted to the single-line style used elsewhere; replace the multi-line assertion involving model_manager._model_metadata["sentiment-test"]["module"] == "test.sentiment_module" with a single-line assert statement to match surrounding assertions (e.g., assert model_manager._model_metadata["sentiment-test"]["module"] == "test.sentiment_module").src/context_control/logging.py-34-36 (1)
34-36:⚠️ Potential issue | 🟡 MinorAdd a Filter to
setup_logging()to provide defaultcontext_idvalue.The format string references
%(context_id)s, but ifsetup_logging()is called and the returned logger is used directly—not wrapped inContextAdapter—the missing field will raise aKeyErrorat log time. Whileget_context_logger()safely provides this field viaContextAdapter, the publicsetup_logging()API leaves this unhandled. Add a Filter to ensurecontext_idalways exists in log records with a sensible default:Add ContextFilter to setup_logging()
+class ContextFilter(logging.Filter): + """Filter that ensures context_id field exists in all log records.""" + + def filter(self, record): + if not hasattr(record, 'context_id'): + record.context_id = 'unknown' + return True + + def setup_logging( level: str = "INFO", log_file: Optional[str] = None, format_string: Optional[str] = None, ) -> logging.Logger: """Setup logging configuration for the context control library.""" logger = logging.getLogger("context_control") logger.setLevel(getattr(logging, level.upper())) + logger.addFilter(ContextFilter()) # Remove existing handlers to avoid duplicates🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@src/context_control/logging.py` around lines 34 - 36, The logging format references %(context_id)s but setup_logging() currently doesn't ensure that field exists, causing KeyError when using loggers directly; add a ContextFilter class (or similar) inside src/context_control/logging.py that defines filter(self, record) to set record.context_id = getattr(record, "context_id", "none") (or another sensible default) and attach an instance of this filter to the root/returned logger inside setup_logging(); keep the existing format_string and ensure get_context_logger()/ContextAdapter still override context_id when present.tests/test_workflow_engine.py-110-114 (1)
110-114:⚠️ Potential issue | 🟡 MinorInconsistent function signature in
failing_operation.While
good_operationwas updated to use*args, thefailing_operationon line 113-114 still uses a single parameterx. This inconsistency could cause issues if the workflow engine now passes inputs differently.Suggested fix for consistency
- def failing_operation(x): + def failing_operation(*args): raise Exception("Node failed intentionally")🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@tests/test_workflow_engine.py` around lines 110 - 114, The failing_operation function signature is inconsistent with good_operation (which uses *args) and may break the workflow engine when it passes variable inputs; update failing_operation to accept *args (not a single x), and adapt its body to raise the same Exception regardless of args (e.g., ignore args and raise "Node failed intentionally") so both functions accept the same call shape; refer to the functions good_operation and failing_operation to locate and change the signature and body.src/core/notmuch_data_source.py-627-634 (1)
627-634:⚠️ Potential issue | 🟡 MinorFix missing f-string in error log.
Line 633 logs
"Error ID: {error_id}"literally, so the real ID is never emitted.🛠️ Proposed fix
- logger.error("Notmuch database not available. Error ID: {error_id}") + logger.error(f"Notmuch database not available. Error ID: {error_id}")🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@src/core/notmuch_data_source.py` around lines 627 - 634, The logger.error call in the Notmuch data source block is using a literal template so the generated error_id from log_error isn't printed; update the logger.error invocation near where error_id is assigned (after log_error(...) in the Notmuch database availability check) to interpolate the error_id (e.g., use an f-string or .format) so the actual error ID is included in the log message.
🧹 Nitpick comments (18)
src/backend/python_backend/ai_routes.py (1)
99-101: Document the 500 response in the route decoratorresponsesmetadata.
This improves OpenAPI accuracy and resolves the Sonar complaint for the raisedHTTPException(status_code=500, ...).🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@src/backend/python_backend/ai_routes.py` around lines 99 - 101, The route in src/backend/python_backend/ai_routes.py that raises HTTPException(status_code=500, ...) needs a documented 500 response in its route decorator responses metadata; update the decorator (e.g., `@router.post` or `@app.post` on the function that raises the exception) to include a 500 entry like {500: {"description":"Failed to categorize email with AI.","content":{"application/json":{"example":{"detail":"Failed to categorize email with AI."}}}}} so the OpenAPI spec documents the error for the raised HTTPException.src/core/plugin_routes.py (2)
213-215: Document raised error status codes in route decorators usingresponses=parameter.Lines 213-215 raise HTTP 500 and lines 273-275 raise HTTP 501, but these status codes are not declared in endpoint metadata. FastAPI requires explicit declaration via the
responses=parameter in the route decorator to include these exceptions in the OpenAPI schema and automatic API documentation.Suggested refactor
-@router.post("/install") +@router.post( + "/install", + responses={500: {"description": "Failed to initiate plugin installation"}}, +) async def install_plugin(-@router.put("/{plugin_id}/config") +@router.put( + "/{plugin_id}/config", + responses={501: {"description": "Plugin configuration update not yet implemented"}}, +) async def update_plugin_config(🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@src/core/plugin_routes.py` around lines 213 - 215, The route that raises HTTPException(status_code=500, detail="Failed to initiate plugin installation") and the one that raises HTTPException(status_code=501, detail="Unsupported plugin source") must declare those responses in their route decorators so FastAPI includes them in OpenAPI; update the corresponding `@router`.<method>(...) decorator(s) for the endpoint(s) that contain these raises to add a responses parameter like responses={500: {"description": "Failed to initiate plugin installation"}, 501: {"description": "Unsupported plugin source"}} (optionally include a schema or model if you have a common error response model) so the 500 and 501 codes appear in the endpoint metadata. Ensure you modify the decorator(s) wrapping the function(s) that contain those exact raise statements.
156-158: PreferAnnotated[...]for FastAPI dependencies in signatures.On lines 156-158, 175-177, and 253-255, switching to
Annotated[PluginManager, Depends(get_plugin_manager)]follows modern FastAPI best practices. FastAPI 0.95.0+ (your project uses 0.100.0+) recommends this pattern, which preserves type clarity and enables better IDE support.♻️ Suggested refactor
+from typing import Annotated, List, Optional from typing import List, Optional @@ -async def enable_plugin( - plugin_id: str, manager: PluginManager = Depends(get_plugin_manager) -): +async def enable_plugin( + plugin_id: str, + manager: Annotated[PluginManager, Depends(get_plugin_manager)], +): @@ -async def disable_plugin( - plugin_id: str, manager: PluginManager = Depends(get_plugin_manager) -): +async def disable_plugin( + plugin_id: str, + manager: Annotated[PluginManager, Depends(get_plugin_manager)], +): @@ -async def validate_plugin( - plugin_id: str, manager: PluginManager = Depends(get_plugin_manager) -): +async def validate_plugin( + plugin_id: str, + manager: Annotated[PluginManager, Depends(get_plugin_manager)], +):🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@src/core/plugin_routes.py` around lines 156 - 158, Replace direct FastAPI dependency parameters typed as "PluginManager = Depends(get_plugin_manager)" with the Annotated pattern to preserve type clarity and IDE support: import Annotated from typing and change the signature of enable_plugin (and any other endpoint functions that currently declare manager: PluginManager = Depends(get_plugin_manager)) to use manager: Annotated[PluginManager, Depends(get_plugin_manager)]. Ensure all occurrences (e.g., the other endpoint definitions that use PluginManager and get_plugin_manager) are updated consistently and update imports accordingly.ruff.toml (2)
10-10: Consider using per-file ignores for F821 instead of a global ignore.Globally ignoring
F821(undefined name) is risky because it silences actual bugs and will mask any new undefined-name errors introduced in future code. Since the pre-existing violations are isolated tomain.pyandgradio_app.py, consider using per-file ignores:[lint.per-file-ignores] "src/backend/python_backend/main.py" = ["F821"] "src/backend/python_backend/gradio_app.py" = ["F821"]This allows F821 to catch undefined names in new or other files while suppressing known issues in the deprecated backend modules.
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@ruff.toml` at line 10, Replace the global F821 ignore with per-file ignores: remove "F821" from the global ignore list in ruff.toml and instead add a [lint.per-file-ignores] stanza that assigns ["F821"] to the two deprecated backend modules ("src/backend/python_backend/main.py" and "src/backend/python_backend/gradio_app.py"); update the ruff.toml so F821 continues to be suppressed only for those files while remaining enabled everywhere else (search for the global "F821" entry and the file names main.py and gradio_app.py to locate where to change).
5-11: Consider tracking these suppressed rules as technical debt.Suppressing these rules is acceptable for unblocking CI, but the underlying issues (bare
exceptclauses, unused imports, redefinitions) represent technical debt. Consider creating issues to systematically address these violations so the ignores can eventually be removed.🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@ruff.toml` around lines 5 - 11, The ruff rule suppressions (E402, E722, F401, F811, F821) hide real issues that should be tracked as technical debt; create explicit tracking by opening tickets (or a backlog epic) for each suppressed rule referencing the exact codes E402, E722, F401, F811 and F821, link those tickets to the ruff.toml suppression block (add the ticket/issue IDs as comments above the ignore list), and add a short README or dev note indicating the intended remediation plan and priority so these ignores can be reviewed and removed over time.modules/dashboard/models.py (1)
33-35: Optional: Simplify parenthesized None assignment.Wrapping
Nonein parentheses across multiple lines (= (None)) is syntactically valid but unusual and doesn't improve readability. This appears to be overly aggressive formatting.Consider simplifying to:
performance_metrics: Optional[Dict[str, float]] = None🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@modules/dashboard/models.py` around lines 33 - 35, The assignment for the performance_metrics field is unnecessarily wrapped in multi-line parentheses; update the declaration of performance_metrics (the Optional[Dict[str, float]] class attribute) to assign None directly without the extra parentheses so it reads as a normal single-line default assignment.src/backend/python_nlp/gmail_integration.py (1)
71-73: Pre-existing duplicate@dataclasses.dataclassdecorator.The
EmailBatchclass has a duplicate@dataclasses.dataclassdecorator on lines 71-72. While this wasn't introduced by this PR (the lines aren't marked as changed), it should be addressed as it's redundant and could cause confusion.Remove duplicate decorator
`@dataclasses.dataclass` -@dataclasses.dataclass class EmailBatch:🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@src/backend/python_nlp/gmail_integration.py` around lines 71 - 73, The EmailBatch class has a duplicated `@dataclasses.dataclass` decorator; remove the redundant decorator so only a single `@dataclasses.dataclass` precedes the class definition (locate the duplicate above class EmailBatch and delete one of the identical `@dataclasses.dataclass` lines).src/main.py (1)
725-727: Address Gradio 6 deprecation warning forBlocksconstructor args.CI already reports this as deprecated (
theme/titlemoved to launch/config flow). Please migrate to the supported API to avoid breakage on upgrade.What is the Gradio 6+ recommended migration path for `gr.Blocks(theme=..., title=...)` constructor arguments?🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@src/main.py` around lines 725 - 727, The Gradio 6 deprecation means you must stop passing theme and title into gr.Blocks(); remove those constructor args from the gr.Blocks(...) call and instead pass them when launching or configuring the app (e.g., call launch/config on the gradio_app object with title="Email Intelligence Platform" and theme=gr.themes.Soft()); update the code around the gr.Blocks instantiation (symbol: gr.Blocks and variable gradio_app) to construct with no theme/title and move those values into the subsequent launch/config call.src/backend/python_backend/advanced_workflow_routes.py (1)
206-214: Consider documenting the404response in the route decorator for API clarity and consistency with FastAPI best practices.
delete_advanced_workflowraisesHTTPException(status_code=404, ...)but the decorator doesn't document this response. While the codebase doesn't currently use theresponses=parameter pattern, adding it would improve OpenAPI/Swagger documentation for API consumers.Suggested fix
-@router.delete("/advanced/workflows/{workflow_id}") +@router.delete( + "/advanced/workflows/{workflow_id}", + responses={404: {"description": "Workflow not found or could not be deleted"}}, +)Note: Similar functions in the codebase (e.g.,
get_advanced_workflow,delete_workflowinworkflow_routes.py) also raise 404 errors without decorator documentation. Consider applying this consistently across related endpoints.🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@src/backend/python_backend/advanced_workflow_routes.py` around lines 206 - 214, The route decorator for delete_advanced_workflow currently lacks OpenAPI documentation for the 404 response; update the router.delete decorator on the delete_advanced_workflow function to include a responses parameter documenting status 404 (e.g., {"404": {"description": "Workflow not found or could not be deleted"}}) so the raised HTTPException is reflected in the generated docs; apply the same pattern consistently to similar endpoints (like get_advanced_workflow and delete_workflow) to keep API documentation uniform.src/backend/python_backend/routes/v1/email_routes.py (1)
72-75: UseAnnotatedfor FastAPI dependency injection typing to align with current best practices.
email_service: EmailService = Depends(get_email_service)uses the older dependency injection pattern. FastAPI's official recommendation (since 0.95.0) is to useAnnotated[EmailService, Depends(get_email_service)]for clearer type hints and better editor support.Suggested fix
-from typing import Optional +from typing import Annotated, Optional @@ - email_service: EmailService = Depends(get_email_service), + email_service: Annotated[EmailService, Depends(get_email_service)],🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@src/backend/python_backend/routes/v1/email_routes.py` around lines 72 - 75, Update the route parameter typing to use Annotated for FastAPI DI: replace the existing parameter declaration using Equals/Depends for email_service with the Annotated form Annotated[EmailService, Depends(get_email_service)], and add the required Annotated import (e.g., from typing import Annotated) at the top of the file; target the function that declares the parameter named email_service and uses EmailService and get_email_service.src/backend/python_backend/main.py (1)
251-253: Remove redundant self-assignment ofperformance_monitor.This alias is a no-op and reduces clarity. Prefer using the imported symbol directly.
Suggested cleanup
-performance_monitor = ( - performance_monitor # Used by all routes via `@performance_monitor.track` -)🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@src/backend/python_backend/main.py` around lines 251 - 253, The line that reassigns performance_monitor to itself is redundant and should be removed; delete the no-op assignment "performance_monitor = (performance_monitor # Used by all routes via `@performance_monitor.track` )" and rely on the imported performance_monitor symbol directly (keeping any `@performance_monitor.track` decorators as-is), ensuring no other code expects a differently-scoped alias named performance_monitor.src/backend/node_engine/workflow_engine.py (1)
299-304: Remove unnecessaryasynckeywords from synchronous helper methods.Three methods contain only synchronous operations but are declared
async:
_set_initial_inputs(line 299)_set_node_inputs(line 310)cancel_execution(line 498)Convert these to sync methods and remove
awaitat their call sites (approximately 3 total locations) to reduce coroutine overhead and eliminate Sonar warnings.🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@src/backend/node_engine/workflow_engine.py` around lines 299 - 304, The three helper methods _set_initial_inputs, _set_node_inputs, and cancel_execution are declared async but perform only synchronous work; change their definitions to regular def (remove async) and update any callers to drop awaiting them (remove await where they are invoked, about three call sites). Specifically, modify the function signatures for _set_initial_inputs, _set_node_inputs, and cancel_execution to be synchronous methods and adjust all references that call await self._set_initial_inputs(...), await self._set_node_inputs(...), or await self.cancel_execution(...) to plain calls (self._set_initial_inputs(...), self._set_node_inputs(...), self.cancel_execution(...)) to eliminate unnecessary coroutine overhead and satisfy static analysis.src/core/workflow_engine.py (1)
600-656: Memory optimization refactoring looks correct, but cognitive complexity is high.The refactoring of
_calculate_cleanup_scheduleinto helper methods (_build_outgoing_edges,_find_last_usages) improves modularity. However,_find_last_usageshas a cognitive complexity of 31 (SonarCloud allows 15).Consider simplifying by extracting the inner loop logic:
♻️ Suggested simplification
def _find_last_usages( self, execution_order: List[str], outgoing_edges: Dict[str, set] ) -> Dict[str, List[str]]: """Helper to find the last consumer node for each executing node.""" last_used_by = {} execution_indices = { node_id: idx for idx, node_id in enumerate(execution_order) } for node_id in execution_order: - if node_id in outgoing_edges: - consumers = outgoing_edges[node_id] - latest_idx = -1 - for consumer in consumers: - if consumer in execution_indices: - idx = execution_indices[consumer] - if idx > latest_idx: - latest_idx = idx - - if latest_idx != -1: - last_consumer = execution_order[latest_idx] - if last_consumer not in last_used_by: - last_used_by[last_consumer] = [] - last_used_by[last_consumer].append(node_id) - else: - if node_id not in last_used_by: - last_used_by[node_id] = [] - last_used_by[node_id].append(node_id) - else: - if node_id not in last_used_by: - last_used_by[node_id] = [] - last_used_by[node_id].append(node_id) + last_consumer = self._get_last_consumer(node_id, outgoing_edges, execution_indices, execution_order) + last_used_by.setdefault(last_consumer, []).append(node_id) return last_used_by + +def _get_last_consumer( + self, node_id: str, outgoing_edges: Dict[str, set], + execution_indices: Dict[str, int], execution_order: List[str] +) -> str: + """Find the last consumer of a node, or return the node itself.""" + consumers = outgoing_edges.get(node_id, set()) + if not consumers: + return node_id + + valid_indices = [ + execution_indices[c] for c in consumers if c in execution_indices + ] + if not valid_indices: + return node_id + + return execution_order[max(valid_indices)]🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@src/core/workflow_engine.py` around lines 600 - 656, The method _find_last_usages has high cognitive complexity; extract the inner consumer-processing logic into a new helper (e.g., _get_latest_consumer_or_self) that takes (node_id, consumers, execution_indices, execution_order) and returns the last_consumer_id (or node_id itself when no later consumer), then replace the nested loop and branching in _find_last_usages with a single call to that helper and a simple append to last_used_by; this will reduce branching in _find_last_usages and keep the behavior of mapping each node to its last consumer(s).src/core/database.py (2)
369-377: Same duplicate logging pattern in_save_data_to_file.The
log_error()call followed bylogger.error()creates duplicate log entries. Consider removing the explicitlogger.error()call sincelog_error()handles logging internally.🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@src/core/database.py` around lines 369 - 377, In _save_data_to_file remove the duplicate logging by deleting the explicit logger.error(...) call that immediately follows log_error(...); keep the log_error call (with ErrorSeverity.ERROR and ErrorCategory.VALIDATION and error_context) since log_error already records the message and returns error_id, and if you want to surface the error_id ensure any downstream return/exception includes it rather than calling logger.error again.
230-239: Duplicate logging:log_error()already logs to standard logger.Per
enhanced_error_reporting.py(lines 140-145),log_error()internally callslogger.log(). The subsequentlogger.error()call creates duplicate log entries for the same error.This is a pre-existing issue, not introduced by this formatting PR, but worth fixing:
♻️ Suggested fix
error_id = log_error( e, severity=ErrorSeverity.WARNING, category=ErrorCategory.DATA, context=error_context, details={"error_type": type(e).__name__}, ) - logger.error( - f"Error loading content for email {email_id}: {e}. Error ID: {error_id}" - ) + # log_error already logs to standard logger🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@src/core/database.py` around lines 230 - 239, The code currently calls log_error(...) which already logs via the module logger and then calls logger.error(...) again causing duplicate entries; remove the redundant logger.error(...) call in the error handling block that references email_id and error_id (the block around log_error and the subsequent logger.error) and instead rely on the returned error_id from log_error for downstream use or, if you need an additional non-logged reference, store or propagate error_id (do not call logger.error again). Ensure this change is applied where log_error(...) is invoked (the error handling around loading content for email_id and error_context).src/backend/node_engine/email_nodes.py (1)
450-601: High cognitive complexity and duplicate literals in_matches_criteria.SonarCloud flags:
- Cognitive complexity 69 (allowed: 15) - This method handles 8+ filtering criteria types with boolean logic.
- Duplicate literal
"+00:00"appears 3 times (lines 472, 540, 546).Consider extracting a constant and breaking the method into smaller helpers:
♻️ Suggested improvements
# At module level UTC_OFFSET_SUFFIX = "+00:00" # Helper for datetime parsing def _parse_iso_datetime(timestamp_str: str) -> Optional[datetime]: """Parse ISO timestamp, normalizing Z suffix.""" if not timestamp_str: return None try: return datetime.fromisoformat(timestamp_str.replace("Z", UTC_OFFSET_SUFFIX)) except ValueError: return None # Break _matches_criteria into smaller methods: # - _matches_keyword_criteria(email, criteria) # - _matches_sender_criteria(email, criteria) # - _matches_date_criteria(email, criteria) # - _matches_size_criteria(email, criteria) # etc.🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@src/backend/node_engine/email_nodes.py` around lines 450 - 601, The _matches_criteria method is too large and repeats the ISO Z->+00:00 literal; extract a module-level constant (e.g., UTC_OFFSET_SUFFIX = "+00:00") and create a small helper _parse_iso_datetime(timestamp_str: str) -> Optional[datetime] that normalizes the Z suffix and returns a datetime or None, then refactor _matches_criteria to delegate each block to focused helpers (e.g., _matches_keyword_criteria, _matches_sender_criteria, _matches_recipient_criteria, _matches_category_criteria, _matches_date_criteria, _matches_size_criteria, _matches_priority_criteria, plus reuse _evaluate_condition for boolean logic) so each helper returns bool and _matches_criteria just composes their results and uses _parse_iso_datetime instead of repeating the replace logic.src/backend/python_backend/node_workflow_routes.py (1)
265-277: Consider documenting HTTPException responses for OpenAPI compliance.SonarCloud flags that
HTTPExceptionwith status codes 404 and 500 should be documented in theresponsesparameter of the route decorators for proper OpenAPI schema generation. This is a pre-existing issue not introduced by this formatting PR, but worth addressing in a follow-up.Example for the delete endpoint:
`@router.delete`("/api/nodes/workflows/{workflow_id}", responses={404: {"description": "Workflow not found"}, 500: {"description": "Internal server error"}})🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@src/backend/python_backend/node_workflow_routes.py` around lines 265 - 277, Update the route decorator on delete_node_workflow to document the HTTPException responses for OpenAPI by adding a responses parameter to the `@router.delete` decorator that declares 404 and 500 (e.g., 404: "Workflow not found", 500: "Internal server error"); keep the existing raise HTTPException(...) calls inside delete_node_workflow unchanged so the documented responses match the actual error raises from node_workflow_manager.delete_workflow and the exception handler.src/core/smart_filter_manager.py (1)
187-195: Avoid double-logging the same DB exception path.
log_error(...)already emits the structured error; the immediateself.logger.error(...)repeats the same failure and can inflate alerts/noise.Also applies to: 203-211, 243-251, 262-270
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@src/core/smart_filter_manager.py` around lines 187 - 195, The code currently double-logs DB exceptions by calling log_error(...) (which emits structured telemetry) and then immediately self.logger.error(...) with the same details; remove the redundant self.logger.error(...) calls in the affected blocks (the one shown and the similar blocks around lines 203-211, 243-251, 262-270) or replace them with a minimal log that only references the returned error_id (e.g., self.logger.debug(f"Error ID: {error_id}") ) so the structured error from log_error remains the single error signal; target the occurrences that call log_error, ErrorSeverity, ErrorCategory and then self.logger.error to locate and update the code.
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# Check if test_launcher.py exists and examine its content
fd -t f 'test_launcher.py' tests/
# Look for any comments or documentation about why it might be excluded
rg -n -C3 'test_launcher' --type=py --type=yaml --type=mdRepository: MasumRab/EmailIntelligence
Length of output: 3099
Fix the import errors in test_launcher.py instead of silently excluding it from coverage.
The project's own documentation (test-path-issues-plan.md) documents that test_launcher.py has a known import failure: it attempts to import PYTHON_MAX_VERSION from launch.py, but this constant doesn't exist. This PR excludes the test from coverage without fixing the underlying issue or documenting the exclusion.
Rather than masking the broken test, resolve the import error so the test can run and contribute to coverage. If continued exclusion is necessary, document the reason explicitly in the CI workflow or a code comment.
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In @.github/workflows/ci.yml around lines 42 - 43, The test exclusion hides an
import failure in tests/test_launcher.py where it imports PYTHON_MAX_VERSION
from launch.py; fix by either adding the missing constant export to launch.py
(define and export PYTHON_MAX_VERSION with the intended value) or update
tests/test_launcher.py to import the correct symbol/name that actually exists in
launch.py (or compute the value in the test), then remove the --ignore entry so
the test runs under coverage; if you must keep the ignore, add an inline comment
in the CI workflow explaining the exact import issue and link to
test-path-issues-plan.md.
| request: Request, | ||
| current_user: str = Depends(get_current_active_user), | ||
| db: DataSource = Depends(get_data_source) | ||
| db: DataSource = Depends(get_data_source), |
There was a problem hiding this comment.
Resolve shadowed get_data_source import before DI binding.
Depends(get_data_source) here is currently ambiguous because get_data_source is imported twice from different modules in this file, and one silently overrides the other. Please keep a single source-of-truth import and bind DI explicitly to that function.
Suggested fix
-from src.core.data.factory import get_data_source
from src.core.data.data_source import DataSource
from src.core.factory import get_data_sourceAlso applies to: 43-43
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@modules/categories/routes.py` at line 23, The Depends(get_data_source) call
is ambiguous because get_data_source is imported twice; remove the duplicate
import and ensure the DI binds to the intended provider by importing only the
correct get_data_source (the single source-of-truth) and using that symbol in
Depends(get_data_source) (also update the other occurrence at the second Depends
usage). If you need to keep both functions, qualify the dependency explicitly
(e.g., Depends(module_name.get_data_source)) so the DI references the correct
provider and eliminate the overriding import so only one get_data_source exists
in the module.
| logger.error( | ||
| f"Error fetching dashboard stats for user {current_user}: {e}", | ||
| exc_info=True, | ||
| ) |
There was a problem hiding this comment.
Avoid logging raw user identifiers in server error logs.
Line 95 logs current_user directly. Treat this as user-controlled/sensitive and avoid including it verbatim in error logs.
Suggested fix
- logger.error(
- f"Error fetching dashboard stats for user {current_user}: {e}",
- exc_info=True,
- )
+ logger.error(
+ "Error fetching dashboard stats",
+ extra={"route": "dashboard_stats"},
+ exc_info=True,
+ )📝 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.
| logger.error( | |
| f"Error fetching dashboard stats for user {current_user}: {e}", | |
| exc_info=True, | |
| ) | |
| logger.error( | |
| "Error fetching dashboard stats", | |
| extra={"route": "dashboard_stats"}, | |
| exc_info=True, | |
| ) |
🧰 Tools
🪛 GitHub Check: SonarCloud Code Analysis
[warning] 94-97: Change this code to not log user-controlled data.
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@modules/dashboard/routes.py` around lines 94 - 97, The error log currently
interpolates the full current_user object into logger.error (logger.error(...
f"...{current_user} ...")), which may expose sensitive user identifiers; change
the log to avoid raw user data by replacing current_user with a non-sensitive
identifier or masked value (for example use current_user.id or
current_user.get_id() if available, or a masked/hashed version like
mask(current_user.id) before logging), or remove the user operand entirely and
rely on contextual request IDs; update the logger.error call accordingly so only
safe, minimal user context is emitted.
| html_content = re.sub( | ||
| r"<script[^>]*>.*?</script>", "", html_content, flags=re.DOTALL | re.IGNORECASE | ||
| ) | ||
|
|
||
| # Remove style tags | ||
| html_content = re.sub(r'<style[^>]*>.*?</style>', '', html_content, flags=re.DOTALL | re.IGNORECASE) | ||
| html_content = re.sub( | ||
| r"<style[^>]*>.*?</style>", "", html_content, flags=re.DOTALL | re.IGNORECASE | ||
| ) |
There was a problem hiding this comment.
Harden tag-closing regex in sanitizer to prevent bypasses
sanitize_html currently misses end tags with whitespace (e.g., </script > / </style >), so dangerous blocks can survive filtering.
🔧 Suggested fix
- html_content = re.sub(
- r"<script[^>]*>.*?</script>", "", html_content, flags=re.DOTALL | re.IGNORECASE
- )
+ html_content = re.sub(
+ r"<script\b[^>]*>.*?</script\s*>",
+ "",
+ html_content,
+ flags=re.DOTALL | re.IGNORECASE,
+ )
- html_content = re.sub(
- r"<style[^>]*>.*?</style>", "", html_content, flags=re.DOTALL | re.IGNORECASE
- )
+ html_content = re.sub(
+ r"<style\b[^>]*>.*?</style\s*>",
+ "",
+ html_content,
+ flags=re.DOTALL | re.IGNORECASE,
+ )🧰 Tools
🪛 GitHub Check: CodeQL
[failure] 171-171: Bad HTML filtering regexp
This regular expression does not match script end tags like </script >.
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@modules/new_ui/utils.py` around lines 170 - 177, The sanitizer's regex in
sanitize_html misses end tags with trailing whitespace (e.g., </script >), so
update the patterns used when assigning html_content to match closing tags with
optional whitespace and ensure tag boundaries: change the script pattern used in
sanitize_html (currently matching "<script[^>]*>.*?</script>") to require a word
boundary on the opening tag and accept optional whitespace before the closing
'>' (e.g., use a pattern like "<script\b[^>]*>.*?</script\s*>", with the same
flags), and do the equivalent for the style pattern
("<style\b[^>]*>.*?</style\s*>") to prevent bypasses.
| def on_select(evt: gr.SelectData, df: pd.DataFrame): | ||
| if evt.index is not None and not df.empty: | ||
| selected_row = df.iloc[evt.index[0]] | ||
| message_id = selected_row['message_id'] | ||
| message_id = selected_row["message_id"] | ||
| # Run async function to get content | ||
| content = asyncio.run(get_email_content(message_id)) | ||
| return content |
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🧩 Analysis chain
🏁 Script executed:
cat -n modules/notmuch/ui.py | head -100Repository: MasumRab/EmailIntelligence
Length of output: 3873
🌐 Web query:
Gradio async callbacks event loop asyncio.run
💡 Result:
Gradio fully supports async functions (coroutines and async generators) as event callbacks since PR #1190 merged in 2022. You can directly use async def for event handlers like btn.click(fn=async_fn, ...), and Gradio handles awaiting them within its ASGI/FastAPI event loop. Do not use asyncio.run inside Gradio async callbacks. asyncio.run creates a new event loop, which conflicts with Gradio's existing loop and causes "no current event loop" RuntimeError (seen in issues like #3904, #5053, #8238 when launching/running in threads without loops). Key points: - Gradio runs on uvicorn/ASGI with an asyncio event loop. - Event handlers execute in this loop; use await for async operations (e.g., await asyncio.sleep(1)). - For concurrency, set concurrency_limit=N on events or queue(default_concurrency_limit=N). - Common errors occur when running Gradio launch in threads without loops or nesting asyncio.run. Example of correct usage: import gradio as gr import asyncio async def async_callback(msg): await asyncio.sleep(1) return f"Echo: {msg}" with gr.Blocks as demo: inp = gr.Textbox out = gr.Textbox gr.Button.click(async_callback, inp, out) demo.queue.launch Avoid: async def bad_callback(msg): # WRONG loop = asyncio.get_event_loop # May fail in threads # or result = asyncio.run(some_coroutine) # Creates new loop, blocks/conflicts Official docs confirm fn: Callable includes async functions (gradio.app/docs/gradio/on), with examples in guides/blocks-and-event-listeners. For streaming async generators, use await asyncio.sleep not time.sleep to avoid blocking (issue #7867).
Citations:
- 1: RuntimeError: There is no current event loop in thread 'Thread-3'. gradio-app/gradio#3904
- 2: Async functions and async generator functions with the
everyoption to work gradio-app/gradio#6395 - 3: https://www.gradio.app/guides/blocks-and-event-listeners
- 4: https://gradio.app/docs/gradio/on
- 5: Understanding Async Behavior in ChatGPT with Gradio Interface gradio-app/gradio#6749
- 6: async-function-support gradio-app/gradio#1190
- 7: Colab Enterprise and Gradio Error: "There is no current event loop in thread" gradio-app/gradio#8238
- 8: Added catch/fix for no event loop in main thread. gradio-app/gradio#6785
- 9: https://gradio.app/guides/setting-up-a-demo-for-maximum-performance
- 10: Gradio With Threading gradio-app/gradio#5053
Use async callbacks instead of asyncio.run() in Gradio event handlers to avoid event-loop conflicts.
Gradio has supported async functions as event callbacks since 2022. Using asyncio.run() inside callbacks creates a new event loop that conflicts with Gradio's existing ASGI event loop, causing RuntimeError and blocking request handling. Pass async functions directly to event handlers and use await to call async operations.
Suggested fix
- def on_select(evt: gr.SelectData, df: pd.DataFrame):
+ async def on_select(evt: gr.SelectData, df: pd.DataFrame):
if evt.index is not None and not df.empty:
selected_row = df.iloc[evt.index[0]]
message_id = selected_row["message_id"]
- # Run async function to get content
- content = asyncio.run(get_email_content(message_id))
- return content
+ return await get_email_content(message_id)
return "Select an email to view its content."
search_button.click(
- fn=lambda q: asyncio.run(search_notmuch(q)),
+ fn=search_notmuch,
inputs=search_bar,
outputs=email_data_df,
).then(Also applies to: 70-78
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@modules/notmuch/ui.py` around lines 61 - 67, The on_select callback currently
calls asyncio.run(get_email_content(...)) which spawns a new event loop and
conflicts with Gradio's loop; change on_select into an async function (async def
on_select(...)) and await get_email_content(message_id) instead of asyncio.run,
then register this async function directly as the Gradio event callback; repeat
the same change for the other similar handler that uses asyncio.run in the file
so all async calls use await and async callbacks (refer to on_select and
get_email_content to locate the code).
| name=name, | ||
| value=duration, | ||
| unit="ms", | ||
| tags=tags, | ||
| sample_rate=sample_rate, |
There was a problem hiding this comment.
Context-manager timing path calls record_metric on the wrong object.
In TimerContext.__exit__, self is the TimerContext instance, so self.record_metric(...) will raise AttributeError at runtime. This breaks with monitor.time_function("..."): usage (Line 375). Use the outer OptimizedPerformanceMonitor instance instead.
Suggested fix
class TimerContext:
def __enter__(self):
self.start_time = time.perf_counter()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
duration = (time.perf_counter() - self.start_time) * 1000
- self.record_metric(
+ monitor.record_metric(
name=name,
value=duration,
unit="ms",
tags=tags,
sample_rate=sample_rate,
)
- return TimerContext()
+ monitor = self
+ return TimerContext()📝 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.
| name=name, | |
| value=duration, | |
| unit="ms", | |
| tags=tags, | |
| sample_rate=sample_rate, | |
| class TimerContext: | |
| def __enter__(self): | |
| self.start_time = time.perf_counter() | |
| return self | |
| def __exit__(self, exc_type, exc_val, exc_tb): | |
| duration = (time.perf_counter() - self.start_time) * 1000 | |
| monitor.record_metric( | |
| name=name, | |
| value=duration, | |
| unit="ms", | |
| tags=tags, | |
| sample_rate=sample_rate, | |
| ) | |
| monitor = self | |
| return TimerContext() |
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@src/core/performance_monitor.py` around lines 375 - 379,
TimerContext.__exit__ is calling self.record_metric(...) but self is the
TimerContext, not the OptimizedPerformanceMonitor, causing an AttributeError;
replace that call to use the outer monitor instance held on the TimerContext
(e.g. self._monitor or self.monitor) so it calls
OptimizedPerformanceMonitor.record_metric(...) instead of
TimerContext.record_metric; locate the TimerContext class and change the call in
__exit__ from self.record_metric(...) to the TimerContext's stored reference to
the outer monitor (calling record_metric with the same name, value, unit, tags,
sample_rate arguments).
| refresh_btn.click( | ||
| fn=refresh_system_status, | ||
| outputs=[ | ||
| system_info, last_updated, total_emails, unread_emails, | ||
| gmail_status, cpu_usage, memory_usage, disk_usage | ||
| ] | ||
| system_info, | ||
| last_updated, | ||
| total_emails, | ||
| unread_emails, | ||
| gmail_status, | ||
| cpu_usage, | ||
| memory_usage, | ||
| disk_usage, | ||
| ], |
There was a problem hiding this comment.
🧩 Analysis chain
🌐 Web query:
In current Gradio docs, when a callback has multiple outputs, can it return a dict keyed by arbitrary strings, or must it return (a) a tuple/list in output order or (b) a dict keyed by component objects?
💡 Result:
In current Gradio docs, when a callback (event listener function) has multiple outputs, it must return either (a) a tuple/list in output order or (b) a dict keyed by component objects. It cannot return a dict keyed by arbitrary strings.
Citations:
- 1: https://www.gradio.app/guides/blocks-and-event-listeners
- 2: https://mintlify.com/gradio-app/gradio/guides/blocks-and-events
- 3: https://gradio.app/main/guides/blocks-and-event-listeners
🏁 Script executed:
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Length of output: 1225
🏁 Script executed:
rg -A 80 "def refresh_system_status" src/main.py | head -100Repository: MasumRab/EmailIntelligence
Length of output: 3354
Callback output contract is mismatched for Gradio multi-output.
refresh_btn.click(..., outputs=[...8 components...]) expects an ordered return (tuple/list) or component-keyed dict, but refresh_system_status() returns a dict keyed by strings like "system_info", "cpu_usage", etc. Gradio requires the returned dict to use component objects as keys, not arbitrary strings. Either return a tuple/list matching the output order or refactor the return statement to use the actual component objects as dictionary keys.
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@src/main.py` around lines 149 - 160, The callback return shape for
refresh_system_status() doesn't match the outputs list: Gradio expects either an
ordered tuple/list matching outputs or a dict keyed by the actual component
objects (not string names). Fix refresh_system_status to return values in the
same order as the outputs list [system_info, last_updated, total_emails,
unread_emails, gmail_status, cpu_usage, memory_usage, disk_usage] (as a
tuple/list), or change the dict returned to use the component objects (e.g.,
system_info: value, cpu_usage: value, etc.) so keys match the output components
exactly.
|
|
||
| overall_score: float # 0.0 to 1.0 | ||
| compliance_level: ComplianceLevel | ||
| detailed_results: List[ComplianceResult] |
There was a problem hiding this comment.
🧩 Analysis chain
🌐 Web query:
In Python 3.10+ (without from future import annotations), can a class-level type annotation like List[ComplianceResult] reference a class defined later in the same module without raising a runtime NameError?
💡 Result:
No, in Python 3.10+ without from future import annotations, a class-level type annotation like List[ComplianceResult] referencing a class defined later in the same module will raise a runtime NameError.
Citations:
- 1: https://www.w3tutorials.net/blog/self-reference-or-forward-reference-of-type-annotations-in-python/
- 2: https://peps.python.org/pep-0526/
- 3: https://stackoverflow.com/questions/59206992/type-annotating-a-class-that-is-defined-later-forward-reference
- 4: https://stackoverflow.com/questions/49392845/python-parameter-annotations-unresolved-reference
- 5: https://stackoverflow.com/questions/40049016/using-the-class-as-a-type-hint-for-arguments-in-its-methods
- 6: https://docs.python.org/3.10/reference/datamodel.html
- 7: https://docs.python.org/3.10/reference/executionmodel.html
🏁 Script executed:
head -70 src/resolution/__init__.pyRepository: MasumRab/EmailIntelligence
Length of output: 1660
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head -1 src/resolution/__init__.pyRepository: MasumRab/EmailIntelligence
Length of output: 73
🏁 Script executed:
head -15 src/resolution/__init__.py | cat -nRepository: MasumRab/EmailIntelligence
Length of output: 450
Fix forward-reference runtime error in type annotation
Line 44: detailed_results: List[ComplianceResult] references ComplianceResult before it is defined (line 49). Without from __future__ import annotations, this raises NameError at import time. Add the future import at the top of the file or quote the type: detailed_results: List["ComplianceResult"].
🧰 Tools
🪛 Flake8 (7.3.0)
[error] 44-44: undefined name 'ComplianceResult'
(F821)
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@src/resolution/__init__.py` at line 44, The annotation detailed_results:
List[ComplianceResult] causes a forward-reference runtime error because
ComplianceResult is defined later; fix it by either adding from __future__
import annotations at the top of the module or by quoting the forward reference
(change to List["ComplianceResult"]) so the annotation does not require
ComplianceResult to be defined at import time; update the annotation where
detailed_results is declared (and any other similar annotations) or add the
future import to the top of the file.
| for req_data in data.get('requirements', []): | ||
|
|
||
| for req_data in data.get("requirements", []): |
There was a problem hiding this comment.
Guard parsed constitution payload before .get(...)
If the YAML/JSON file is empty or not an object, data may be None (or another type), and data.get(...) will raise. Add a defensive type check and fail with a clear error.
Suggested fix
- for req_data in data.get("requirements", []):
+ if not isinstance(data, dict):
+ raise ValueError("Constitution file must contain a top-level object")
+
+ for req_data in data.get("requirements", []):📝 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.
| for req_data in data.get("requirements", []): | |
| if not isinstance(data, dict): | |
| raise ValueError("Constitution file must contain a top-level object") | |
| for req_data in data.get("requirements", []): |
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@src/resolution/__init__.py` at line 88, Guard the parsed payload before
calling .get on it: check that the variable data is a dict/mapping (e.g.,
isinstance(data, dict)) before the loop that contains for req_data in
data.get("requirements", []): and if it is not, raise a clear error (ValueError
or custom exception) explaining that the parsed YAML/JSON was empty or not an
object so requirements cannot be read; this ensures the subsequent iteration
over requirements only runs when data is a proper mapping.
| "line_count_change": len(merged_content) - len(original_content.split("\n")) | ||
| if isinstance(original_content, str) | ||
| else 0, |
There was a problem hiding this comment.
line_count_change mixes character count with line count
This computes len(merged_content) (chars) minus len(original_content.split("\n")) (lines), so the metric is wrong.
Suggested fix
- "line_count_change": len(merged_content) - len(original_content.split("\n"))
- if isinstance(original_content, str)
+ "line_count_change": len(merged_content.split("\n"))
+ - len(original_content.split("\n"))
+ if isinstance(original_content, str) and isinstance(merged_content, str)
else 0,📝 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.
| "line_count_change": len(merged_content) - len(original_content.split("\n")) | |
| if isinstance(original_content, str) | |
| else 0, | |
| "line_count_change": len(merged_content.split("\n")) | |
| - len(original_content.split("\n")) | |
| if isinstance(original_content, str) and isinstance(merged_content, str) | |
| else 0, |
🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed.
In `@src/resolution/semantic_merger.py` around lines 339 - 341, The
line_count_change currently subtracts original_content line count from
merged_content character count (using len(merged_content) vs
len(original_content.split("\n"))); change it to use the same unit for both
(preferably line counts). Compute merged_lines = len(merged_content.split("\n"))
and original_lines = len(original_content.split("\n")) if
isinstance(original_content, str) else 0, then set "line_count_change":
merged_lines - original_lines (keep the same keys/variable names merged_content
and original_content to locate the code).
Co-authored-by: MasumRab <8943353+MasumRab@users.noreply.github.com>
Co-authored-by: MasumRab <8943353+MasumRab@users.noreply.github.com>
|
2 participants
Fix formatting errors causing CI pipeline to fail using
uv run ruff format src/core/model_registry.py src/core/workflow_engine.py.PR created automatically by Jules for task 12254347793198898246 started by @MasumRab