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src/api_rate_limiter.py (1)

376-376: Keep typing.Tuple for 3.8 compatibility; silence Ruff UP006 locally

The switch to Tuple is correct for Python 3.8. To keep linters green on this branch, consider line-level suppression for UP006.

Apply this minimal tweak:

-    ) -> Tuple[bool, str, dict]:
+    ) -> Tuple[bool, str, dict]:  # noqa: UP006 - keep typing generics for Python 3.8

src/security/jwt_manager.py (2)

16-16: Remove unused Union import

Union isn’t used anymore; drop it to satisfy linters.

Apply:

-from typing import Dict, List, Optional, Union, Any
+from typing import Dict, List, Optional, Any

---

57-67: Keep typing.Dict for 3.8; optionally suppress Ruff UP006

The shift to Dict[str, Any] is correct for Python 3.8. If Ruff flags UP006, either configure the rule off for this branch or add inline suppressions.

Option A (inline, minimal):

-    def create_access_token(self, user_data: Dict[str, Any]) -> str:
+    def create_access_token(self, user_data: Dict[str, Any]) -> str:  # noqa: UP006
...
-    def create_refresh_token(self, user_data: Dict[str, Any]) -> str:
+    def create_refresh_token(self, user_data: Dict[str, Any]) -> str:  # noqa: UP006
...
-    def create_token_pair(self, user_data: Dict[str, Any]) -> TokenResponse:
+    def create_token_pair(self, user_data: Dict[str, Any]) -> TokenResponse:  # noqa: UP006

Option B (preferable if many occurrences across the file): add a file-level directive # ruff: noqa: UP006 at the top of the file.

Also applies to: 69-81, 82-91

PR_DESCRIPTION.md (1)

68-79: Wording nit: “Testing Improvements Made” vs scope of “no testing improvements”

To avoid mixed signals, consider renaming this section to emphasize test enablement without infra changes.

-## 🧪 **Testing Improvements Made**
+## 🧪 **Test Enablement (No infra changes)**

src/unified_ai_api.py (2)

532-543: Ruff ANN401 on Any: either keep intentionally or narrow type to avoid the rule

If your ruff config forbids Any (ANN401), consider narrowing the parameter type. Otherwise, keeping Any here is reasonable given this adapter handles arbitrary dataclass/dict-like results.

Optional change (3.8-safe) to avoid ANN401:

• Import Mapping, Union: from typing import Mapping, Union
• Update signature:

-def _tx_to_dict(result: Any) -> Dict[str, Any]:
+def _tx_to_dict(result: Union[Mapping[str, Any], object]) -> Dict[str, Any]:

Please confirm whether ANN401 is enforced in CI for this repo. If yes, I can sweep similar cases.

---

606-624: Standardize type annotations to typing generics for Python 3.8 compatibility

Several PEP 585-style annotations remain in src/unified_ai_api.py. Downstream tools (e.g., FastAPI, Pydantic, get_type_hints) on Python 3.8 may not handle dict[str, …], list[…], or tuple[…] even with from __future__ import annotations. To maintain full 3.8 support and consistency, please switch these to Dict, List, Tuple, etc., from the typing module.

Key occurrences to update:

• Lines 606, 640, 668: d: dict[str, Any] and return type -> tuple[str, …]
• Line 727: return type -> tuple[str, list[str]]
• Line 1131: meta: dict[str, Any]
• Line 1222: response: dict[str, Any]
• Line 1653: audio_files: list[UploadFile]
• Lines 1660, 1953, 2014, 2097, 2135: various -> dict[str, Any]

Example change:

-from typing import Any
+from typing import Any, Dict, List, Tuple

- d: dict[str, Any],
-) -> tuple[str, …]:
+ d: Dict[str, Any],
+) -> Tuple[str, …]:

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requirements-dev.txt (1)

1-34: Synchronize Test Dependencies between pyproject.toml and requirements-dev.txt

The test extras in pyproject.toml and the “Test Dependencies” block in requirements-dev.txt are out of sync:

• only in pyproject.toml:
– httpx>=0.24.0
• only in requirements-dev.txt:
– flask>=3.1.1,<4.0.0
– httpx>=0.25.0,<0.29.0
– requests==2.32.4

The dev extras already match.

Please reconcile these lists (either by updating pyproject.toml or requirements-dev.txt) so they mirror each other exactly. To prevent future drift, consider adding the following consistency check into your CI pipeline:

#!/usr/bin/env bash
set -euo pipefail
pip install tomli
python - << 'PY'
import re, pathlib, tomli

root = pathlib.Path(".")
py = tomli.loads((root/"pyproject.toml").read_text("utf-8"))
extras = py["project"]["optional-dependencies"]
dev_extras = set(extras.get("dev", []))
test_extras = set(extras.get("test", []))

req_lines = (root/"requirements-dev.txt").read_text("utf-8").splitlines()
def collect(name):
    start = rf"^#\s*{name}\s*\(from .*extra\)"
    seen=False; pkgs=[]
    for l in req_lines:
        if re.match(start, l): seen=True; continue
        if seen and l.strip().startswith("#") and "extra)" in l: break
        if seen and l.strip() and not l.strip().startswith("#"):
            pkgs.append(l.split("#",1)[0].strip())
    return set(pkgs)

req_test = collect("Test Dependencies")
req_dev  = collect("Development Dependencies")

def diff(n, a, b):
    only_a = a-b; only_b=b-a
    if only_a or only_b:
        echo="[FAIL]" if (only_a or only_b) else "[OK]"
        print(f"{echo} {n}\n  only in pyproject: {only_a}\n  only in requirements-dev: {only_b}")
        exit(1)

diff("test", test_extras, req_test)
diff("dev", dev_extras, req_dev)
print("[PASS] all extras match")
PY

Running this as part of CI will immediately flag future mismatches.

src/data/prisma_client.py (3)

41-63: Template never interpolates {script} — Node file is written with placeholders (broken).

The triple-quoted string isn’t an f-string or .format call, so {script} is never injected. The resulting JS file will contain invalid code.

Apply:

-        with Path("temp_prisma_script.js").open("w") as f:
-            f.write("""
+        with Path("temp_prisma_script.js").open("w") as f:
+            f.write(f"""
 const {{ PrismaClient }} = require('@prisma/client');
 const prisma = new PrismaClient();

 async function main() {{
     try {{
         const result = await (async () => {{
-            {script}
+            {script}
         }})();
         console.log(JSON.stringify(result));
         await prisma.$disconnect();
         return result;
     }} catch (e) {{
         console.error(e);
         await prisma.$disconnect();
         process.exit(1);
     }}
 }}
 
 main();
-""")
+""")

Note: The doubled braces around PrismaClient are correct when using f-strings.

---

72-75: Error message string isn’t formatted.

This will literally return “{e.stderr}”.

-            msg = "Prisma command failed: {e.stderr}"
+            msg = f"Prisma command failed: {e.stderr}"

---

94-104: Unsafe string interpolation into JS (command injection risk) and missing f-strings.

User-provided values (email, title, content, etc.) are injected directly into JavaScript without escaping, and the Python strings aren’t f-strings, so placeholders won’t be replaced. This is both broken and unsafe.

Minimal safe fix using json.dumps to correctly quote values in JS:

@@
-        script = """
-        return prisma.user.create({
-            data: {
-                email: '{email}',
-                passwordHash: '{password_hash}',
-                consentVersion: {"'{consent_version}'" if consent_version else "null"},
-                consentGivenAt: {"new Date()" if consent_version else "null"}
-            }
-        });
-        """
+        email_js = json.dumps(email)
+        pwd_js = json.dumps(password_hash)
+        consent_js = "null" if consent_version is None else json.dumps(consent_version)
+        consent_date_js = "null" if consent_version is None else "new Date()"
+        script = f"""
+        return prisma.user.create({{
+            data: {{
+                email: {email_js},
+                passwordHash: {pwd_js},
+                consentVersion: {consent_js},
+                consentGivenAt: {consent_date_js}
+            }}
+        }});
+        """
@@
-        script = """
-        return prisma.journalEntry.create({
-            data: {
-                userId: '{user_id}',
-                title: '{title}',
-                content: '{content}',
-                isPrivate: {str(is_private).lower()},
-                user: {
-                    connect: {
-                        id: '{user_id}'
-                    }
-                }
-            }
-        });
-        """
+        uid_js = json.dumps(user_id)
+        title_js = json.dumps(title)
+        content_js = json.dumps(content)
+        is_private_js = "true" if is_private else "false"
+        script = f"""
+        return prisma.journalEntry.create({{
+            data: {{
+                userId: {uid_js},
+                title: {title_js},
+                content: {content_js},
+                isPrivate: {is_private_js},
+                user: {{
+                    connect: {{
+                        id: {uid_js}
+                    }}
+                }}
+            }}
+        }});
+        """
@@
-        script = """
-        return prisma.user.findUnique({
-            where: { email: '{email}' }
-        });
-        """
+        email_js = json.dumps(email)
+        script = f"""
+        return prisma.user.findUnique({{
+            where: {{ email: {email_js} }}
+        }});
+        """
@@
-        script = """
-        return prisma.journalEntry.findMany({
-            where: { userId: '{user_id}' },
-            take: {limit},
-            orderBy: { createdAt: 'desc' }
-        });
-        """
+        uid_js = json.dumps(user_id)
+        script = f"""
+        return prisma.journalEntry.findMany({{
+            where: {{ userId: {uid_js} }},
+            take: {limit},
+            orderBy: {{ createdAt: 'desc' }}
+        }});
+        """

Follow-ups (optional hardening):

• Use a NamedTemporaryFile to avoid collisions and ensure cleanup.
• Consider passing JSON payload via stdin or environment and using a single static JS runner to eliminate string templating entirely.

Also applies to: 122-136, 150-156, 171-176

src/models/emotion_detection/training_pipeline.py (2)

10-22: Missing imports used throughout the module.

time, json, AdamW, and GoEmotionsDataset are referenced but not imported. This will raise NameError at runtime.

 import logging
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 import numpy as np
 import torch
 import torch.nn.functional as F
 from torch.utils.data import DataLoader
+from torch.optim import AdamW
 from transformers import (
     AutoTokenizer,
     get_linear_schedule_with_warmup,
 )
+import time
+import json
+
+from .dataset_loader import GoEmotionsDataset

---

97-99: Fix logging and path strings — braces aren’t interpolated.

Many log messages and one checkpoint filename use braces without f-strings/format args, outputting literals. Convert to f-strings or parameterized logging.

Examples:

-        logger.info("Using device: {self.device}")
+        logger.info("Using device: %s", self.device)

-        logger.info(
-            "Model initialized with {self.model.count_parameters():,} trainable parameters"
-        )
-        logger.info("Total training steps: {total_steps}")
+        logger.info("Model initialized with %s trainable parameters", f"{self.model.count_parameters():,}")
+        logger.info("Total training steps: %d", total_steps)

-        logger.info("Loading model from checkpoint: {checkpoint_path}")
+        logger.info("Loading model from checkpoint: %s", checkpoint_path)

-        logger.info("Epoch {epoch} completed - Loss: {avg_loss:.4f}, Time: {epoch_time:.1f}s")
+        logger.info("Epoch %d completed - Loss: %.4f, Time: %.1fs", epoch, avg_loss, epoch_time)

-            checkpoint_path = self.output_dir / "checkpoint_epoch_{epoch}.pt"
+            checkpoint_path = self.output_dir / f"checkpoint_epoch_{epoch}.pt"

-            logger.info("Training history saved to {history_path}")
+            logger.info("Training history saved to %s", history_path)

Recommendation: apply the same fix pattern across all logger calls in this file to keep formatting correct and cheap.

Also applies to: 241-244, 251-252, 430-431, 497-501, 575-576

src/models/emotion_detection/api_demo.py (1)

44-49: Incorrect keyword arguments and type for excluded_paths in add_rate_limiting() call.

add_rate_limiting expects requests_per_minute (not rate_limit) and excluded_paths is a Set[str]; passing a list will raise a TypeError due to set union ops inside the middleware.

Apply this fix:

 add_rate_limiting(
     app,
-    rate_limit=100,
-    window_size=60,
-    excluded_paths=["/health", "/docs", "/redoc", "/openapi.json"],
+    requests_per_minute=100,
+    excluded_paths={"/health", "/docs", "/redoc", "/openapi.json"},
 )

src/data/pipeline.py (3)

11-11: Python 3.8 incompatibility: datetime.UTC is unavailable

from datetime import UTC is only available in newer Python versions (3.11+). Use timezone.utc for Python 3.8.

-from datetime import UTC, datetime
+from datetime import datetime, timezone

And update usages accordingly (see a separate suggestion for Line 218).

---

218-219: Fix usage to match Python 3.8 timezone API

After changing the import, use timezone.utc.

-        timestamp = datetime.now(UTC).strftime("%Y%m%d_%H%M%S")
+        timestamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")

---

13-17: Missing imports: JournalEntryPreprocessor and embedding classes referenced below

JournalEntryPreprocessor (used in init) and EmbeddingPipeline + embedders (used later) aren’t imported, which will raise NameError at runtime in Python 3.8 (no postponed annotations here).

 from pathlib import Path
 from typing import Any, Dict, List, Optional, Union
 import pandas as pd
 from .feature_engineering import FeatureEngineer
 from .validation import DataValidator
+from .preprocessing import JournalEntryPreprocessor
+from .embeddings import (
+    EmbeddingPipeline,
+    TfidfEmbedder,
+    Word2VecEmbedder,
+    FastTextEmbedder,
+)

requirements-dev.txt (1)

19-21: Flask>=3.1.1 breaks Python 3.8 — lower to 3.0.3.

This constraint will fail on Python 3.8 (Flask 3.1+ requires >=3.9). Use 3.0.3 to retain 3.8 compatibility, consistent with pyproject dev extra.

-# Legacy Test Support (for Flask-based security tests)
-flask>=3.1.1,<4.0.0
+# Legacy Test Support (for Flask-based security tests)
+flask>=3.0.3,<4.0.0

pyproject.toml (2)

51-64: Mirror Flask constraint in test extra (or drop duplication in requirements-dev).

To keep your “Exact mirror of pyproject.toml dev+test” claim consistent and avoid drift with requirements-dev.txt, either:

• Add Flask to the test extra here, or
• Remove the standalone “Legacy Test Support” Flask pin from requirements-dev.txt.

Suggested change (add to test extra with the same constraint):

   factory-boy>=3.3.0,  # For test data generation
+  flask>=3.0.3,<4.0.0,  # Legacy tests requiring Flask; 3.0.x supports Python 3.8
 ]

---

171-171: Align tooling targets with Python 3.8 to avoid contradictory suggestions.

Ruff and Black target py39 while the project supports Python 3.8 and this PR moves hints away from PEP 585. Consider:

• Set Ruff target-version to py38.
• Optionally add UP006 to ignored rules to prevent “use builtin generics” churn during 3.8 support work.

Proposed diffs:

-[tool.ruff]
-target-version = "py39"
+[tool.ruff]
+target-version = "py38"

 [tool.ruff.lint]
 ignore = [
   ...
   "UP035",   # Import from collections.abc (acceptable)
+  "UP006",   # Allow typing.List/Dict/etc for Python 3.8 compatibility
   ...
 ]

Similarly for Black:

 [tool.black]
-target-version = ['py39']
+target-version = ['py38']

src/security_headers.py (1)

9-9: Drop unused typing imports to satisfy Ruff F401.

Callable and Optional are not used in this module.

-from typing import Callable, Dict, List, Optional
+from typing import Dict, List

requirements-dev.txt (1)

14-16: Unify httpx (and other) versions with pyproject “test” extra.

pyproject’s test extra uses httpx>=0.24.0, while this file uses >=0.25.0,<0.29.0. If this file must be an exact mirror, align them to avoid resolver drift.

Option A: Match pyproject (change here):

-httpx>=0.25.0,<0.29.0
+httpx>=0.24.0

Option B: Bump pyproject’s test extra to match this file (and keep the <0.29.0 cap) if that’s intentional.

src/models/emotion_detection/training_pipeline.py (1)

171-173: Ruff UP006 recommending builtin generics conflicts with 3.8 support — ignore it.

Your move to typing.List/Dict is intentional for 3.8. Make sure UP006 is ignored (see pyproject.toml suggestion).

Also applies to: 286-289

src/data/preprocessing.py (1)

9-9: Remove unused import "List".

Ruff flagged typing.List as unused. Safe to drop it.

-from typing import List, Optional
+from typing import Optional

src/api_rate_limiter.py (3)

372-377: Return type updated to 3.8-compatible Tuple/Dict — keep despite Ruff UP006.

Using Tuple[bool, str, Dict[str, Any]] is correct for Python 3.8. Ruff’s UP006 suggestion (“use tuple/dict”) targets 3.9+ and should be suppressed for this PR’s 3.8 target.

To quiet false-positive lint for this branch, set Ruff’s target version to py38 (or disable UP006):

TOML (outside this file):

# pyproject.toml
[tool.ruff]
target-version = "py38"

[tool.ruff.lint]
extend-ignore = ["UP006"]

---

440-441: Tighten get_stats() return type for consistency.

For parity with your updated allow_request signature and broader PR theme, annotate get_stats as Dict[str, Any].

-def get_stats(self) -> Dict:
+def get_stats(self) -> Dict[str, Any]:

---

31-32: Normalize dataclass field types to Optional[Set[str]] (3.8-compat + clarity).

These fields are None-by-default but annotated as bare set. Use Optional[Set[str]] for accuracy and consistency with this PR’s typing changes.

-    whitelisted_ips: set = None
-    blacklisted_ips: set = None
+    whitelisted_ips: Optional[Set[str]] = None
+    blacklisted_ips: Optional[Set[str]] = None

src/models/summarization/api_demo.py (1)

94-94: texts: List[str] — LGTM (targets Python 3.8).

This aligns with the PR’s migration away from PEP 585 generics. Ignore Ruff UP006 in this code path as long as 3.8 is supported.

If lint complains, set Ruff target-version to py38 (or ignore UP006) as noted in the rate limiter comment.

src/models/emotion_detection/api_demo.py (1)

98-106: List[...] annotations in response model — LGTM, keep despite Ruff UP006.

These are correct for Python 3.8. Suppress UP006 in lint config (or set target-version to py38) while 3.8 support is required.

scripts/maintenance/typehint_codemod.py (3)

22-28: Minor: use a set comprehension.

Cleaner and satisfies Ruff C401.

-            existing = set(x.strip() for x in line.split("import", 1)[1].split(","))
+            existing = {x.strip() for x in line.split("import", 1)[1].split(",")}

---

13-14: Guard against false positives in A | B conversion.

The broad A | B rule risks rewriting bitwise ORs in value expressions. If you keep regex-only, consider limiting to common annotation contexts (after : or ->) or add a command-line flag to disable union conversion and only handle the safe X|None case by default.

---

80-81: Add trailing newline.

Satisfies POSIX and Ruff W292.

 if __name__ == "__main__":
     main()
+ 

src/security/jwt_manager.py (3)

16-16: Remove unused Union import

Union isn't used in this module. Drop it to keep imports clean.

-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, List, Optional

---

55-55: Be explicit about blacklist token map types

Annotating the blacklist structure improves clarity and static checks. Recommend specifying token->expiration mapping type.

-        self.blacklisted_tokens: dict = {}  # Changed to dict: {token: exp_datetime}
+        self.blacklisted_tokens: Dict[str, Optional[datetime]] = {}  # {token: exp_datetime}

---

57-57: Ruff UP006 vs. Python 3.8 goal: suppress or configure

Ruff’s UP006 (prefer built-in generics) conflicts with the PR’s Python 3.8 compatibility (typing.List/Dict is required). Either:

• Configure Ruff target-version to py38 and disable UP006 in pyproject.toml, or
• Locally suppress with per-file/line ignores.

No code change required here; just tooling alignment.

Would you like me to draft a pyproject.toml Ruff section that sets target-version=py38 and ignores UP006 across the repo?

Also applies to: 69-69, 82-82

src/models/voice_processing/api_demo.py (1)

130-130: Prefer precise type: Optional[Dict[str, Any]] over Optional[Dict]

Specifying key/value types helps static analysis and readability.

-    details: Optional[Dict] = Field(None, description="Additional error details")
+    details: Optional[Dict[str, Any]] = Field(None, description="Additional error details")

src/unified_ai_api.py (1)

208-210: Ruff UP006 conflicts with 3.8 typing strategy — configure tool rather than code

UP006 wants built-in generics, but this PR intentionally uses typing aliases for Python 3.8. Recommend setting Ruff’s target-version to py38 and ignoring UP006 in the config, instead of altering code.

If you’d like, I can propose a minimal pyproject.toml snippet to set target-version and ignore UP006 for this repo.

Also applies to: 532-533, 606-607, 727-727, 879-879, 1131-1131, 1222-1222, 1653-1661, 1953-1953, 2014-2014, 2097-2097, 2135-2135

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requirements-dev.txt (1)

30-30: Duplicate Flask entry under dev block: clarify intent to avoid confusion

Since this file is an “exact mirror” of dev+test extras, the duplication is fine. To make it explicit for future maintainers, consider annotating the second occurrence.

Apply this tiny annotation:

-flask>=3.0.3,<4.0.0
+flask>=3.0.3,<4.0.0  # duplicated intentionally (dev+test mirror)

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src/data/pipeline.py (3)

9-11: Fix Python 3.8-incompatible datetime.UTC and name shadowing

datetime.UTC doesn’t exist before Python 3.11. Also, importing both the module (import datetime) and the class (from datetime import datetime) shadows the module name with the class. Replace with datetime.timezone.utc for 3.8 compatibility and drop the redundant module import.

Apply this diff:

-import datetime
-from datetime import UTC, datetime
+from datetime import datetime, timezone
@@
-        timestamp = datetime.now(UTC).strftime("%Y%m%d_%H%M%S")
+        timestamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")

Also applies to: 219-219

---

221-229: Interpolate file paths and related log messages

The output filenames currently contain literal braces and won’t include the timestamp; this risks overwriting files with constant names. Also, the corresponding logger.info calls won’t interpolate without f-strings. Convert these to f-strings.

Apply this diff:

-        featured_df.to_csv(
-            Path(output_dir, "journal_features_{timestamp}.csv").as_posix(),
-            index=False,
-        )
-        logger.info("Saved featured data to {output_dir}/journal_features_{timestamp}.csv")
+        featured_df.to_csv(
+            Path(output_dir, f"journal_features_{timestamp}.csv").as_posix(),
+            index=False,
+        )
+        logger.info(f"Saved featured data to {output_dir}/journal_features_{timestamp}.csv")
@@
-        embeddings_path = Path(output_dir, "journal_embeddings_{timestamp}.csv").as_posix()
+        embeddings_path = Path(output_dir, f"journal_embeddings_{timestamp}.csv").as_posix()
         self.embedding_pipeline.save_embeddings_to_csv(embeddings_df, embeddings_path)
@@
-        if topics_df is not None:
-            topics_df.to_csv(
-                Path(output_dir, "journal_topics_{timestamp}.csv").as_posix(),
-                index=False,
-            )
-            logger.info("Saved topic data to {output_dir}/journal_topics_{timestamp}.csv")
+        if topics_df is not None:
+            topics_df.to_csv(
+                Path(output_dir, f"journal_topics_{timestamp}.csv").as_posix(),
+                index=False,
+            )
+            logger.info(f"Saved topic data to {output_dir}/journal_topics_{timestamp}.csv")
@@
-        if save_intermediates:
-            raw_df.to_csv(Path(output_dir, "journal_raw_{timestamp}.csv").as_posix(), index=False)
-            logger.info(
-                "Saved raw data to {output_dir}/journal_raw_{timestamp}.csv",
-                extra={"format_args": True},
-            )
+        if save_intermediates:
+            raw_df.to_csv(Path(output_dir, f"journal_raw_{timestamp}.csv").as_posix(), index=False)
+            logger.info(f"Saved raw data to {output_dir}/journal_raw_{timestamp}.csv")
@@
-            processed_df.to_csv(
-                Path(output_dir, "journal_processed_{timestamp}.csv").as_posix(),
-                index=False,
-            )
-            logger.info("Saved processed data to {output_dir}/journal_processed_{timestamp}.csv")
+            processed_df.to_csv(
+                Path(output_dir, f"journal_processed_{timestamp}.csv").as_posix(),
+                index=False,
+            )
+            logger.info(f"Saved processed data to {output_dir}/journal_processed_{timestamp}.csv")

Also applies to: 231-236, 238-249

---

50-61: Add missing imports for embedding components

It looks like TfidfEmbedder, Word2VecEmbedder, FastTextEmbedder, and EmbeddingPipeline are defined in src/data/embeddings.py but not imported in src/data/pipeline.py. Without these imports the code will raise NameError at runtime.

Please add the following import at the top of src/data/pipeline.py (e.g. right after the existing local imports):

 from .preprocessing import JournalEntryPreprocessor

+from .embeddings import EmbeddingPipeline, TfidfEmbedder, Word2VecEmbedder, FastTextEmbedder

 # Configure logging

requirements-dev.txt (1)

19-21: Flask spec correctly adjusted for Py3.8 compatibility

Using flask>=3.0.3,<4.0.0 ensures 3.8 works (3.0.3 is the last 3.8-supported line). Nice fix for the legacy Flask-based tests.

src/data/pipeline.py (2)

94-97: Fix logger interpolation (no f-string or %-style formatting currently applied)

These messages include expressions inside braces that won’t evaluate as written. Use f-strings for clarity and correctness.

Apply this diff:

-        logger.info(
-            "Pipeline processing {len(raw_df)} journal entries",
-            extra={"format_args": True},
-        )
+        logger.info(f"Pipeline processing {len(raw_df)} journal entries")
@@
-        logger.info("Generated {len(embeddings_df)} embeddings using {self.embedding_method}")
+        logger.info(f"Generated {len(embeddings_df)} embeddings using {self.embedding_method}")

Also applies to: 124-125

---

168-174: Normalize logging and fix string interpolation in _load_data()

• The DataFrame count message contains a non-evaluated expression.
• user_info and limit_info are built with literal braces.
• JSON/CSV/data source type messages won’t interpolate.

Convert these to f-strings for correctness and consistency.

Apply this diff:

-        if source_type == "dataframe" and isinstance(data_source, pd.DataFrame):
-            logger.info(
-                "Using provided DataFrame with {len(data_source)} entries",
-                extra={"format_args": True},
-            )
+        if source_type == "dataframe" and isinstance(data_source, pd.DataFrame):
+            logger.info(f"Using provided DataFrame with {len(data_source)} entries")
             return data_source
@@
-        if source_type == "db":
-            user_info = " for user {user_id}" if user_id else ""
-            limit_info = " (limit: {limit})" if limit else ""
-            logger.info("Loading data from database{user_info}{limit_info}")
+        if source_type == "db":
+            user_info = f" for user {user_id}" if user_id else ""
+            limit_info = f" (limit: {limit})" if limit else ""
+            logger.info(f"Loading data from database{user_info}{limit_info}")
             return load_entries_from_db(limit=limit, user_id=user_id)
@@
-        if source_type == "json" and isinstance(data_source, str):
-            logger.info(
-                "Loading data from JSON file: {data_source}",
-                extra={"format_args": True},
-            )
+        if source_type == "json" and isinstance(data_source, str):
+            logger.info(f"Loading data from JSON file: {data_source}")
             return load_entries_from_json(data_source)
@@
-        if source_type == "csv" and isinstance(data_source, str):
-            logger.info("Loading data from CSV file: {data_source}", extra={"format_args": True})
+        if source_type == "csv" and isinstance(data_source, str):
+            logger.info(f"Loading data from CSV file: {data_source}")
             return load_entries_from_csv(data_source)
@@
-        logger.error("Invalid data source type: {source_type}", extra={"format_args": True})
+        logger.error(f"Invalid data source type: {source_type}")

Also applies to: 176-180, 183-190, 192-193

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scripts/maintenance/typehint_codemod.py (2)

346-346: Remove unnecessary f-string prefixes.

F-strings without placeholders should be regular strings.

 # Line 346
-            print(f"  ⏭️  No changes needed")
+            print("  ⏭️  No changes needed")

 # Line 387
-        print(f"\nTo apply these changes, run without --dry-run")
+        print("\nTo apply these changes, run without --dry-run")

Also applies to: 387-387

---

254-262: Duplicate handling needed for typing imports.

Similar to the issue in fix_remaining_py38_types.py, this could add duplicate imports.

Apply this diff to prevent duplicate imports:

         for i, line in enumerate(lines):
             if line.strip().startswith('from typing import'):
                 existing_imports = line.replace('from typing import ', '').strip()
-                new_imports = ', '.join(sorted(imports_to_add))
+                # Parse existing imports to avoid duplicates
+                existing_set = set(imp.strip() for imp in existing_imports.split(','))
+                combined_imports = sorted(existing_set | imports_to_add)
                 if existing_imports:
                     new_import_line = (
-                        f"from typing import {existing_imports}, {new_imports}"
+                        f"from typing import {', '.join(combined_imports)}"
                     )
                     lines[i] = new_import_line
                 else:
-                    lines[i] = f"from typing import {new_imports}"
+                    lines[i] = f"from typing import {', '.join(combined_imports)}"
                 break

scripts/maintenance/fix_remaining_py38_types.py (2)

134-135: Fix formatting issues flagged by static analysis.

Multiple formatting issues need attention to maintain code quality.

Apply these fixes:

-                r'from typing import', 
-                f'from typing import {new_imports}', 
+                r'from typing import',
+                f'from typing import {new_imports}',
                 content

 # Line 248 - Remove unnecessary f-string prefix:
-            print(f"  ⏭️  No changes needed")
+            print("  ⏭️  No changes needed")

 # Line 259 - Remove trailing whitespace:
-        r for r in results 
+        r for r in results
         if not r.get('modified', False) and 'error' not in r

 # Line 275 - Remove unnecessary f-string prefix:
-        print(f"\nTo apply these changes, run without --dry-run")
+        print("\nTo apply these changes, run without --dry-run")

Also applies to: 248-248, 259-259, 275-275

---

61-96: Type-matching whitelist is too restrictive; consider expanding coverage.

The current whitelist for union pattern matching only includes basic built-in types, which may miss legitimate type hints involving custom classes, typing module types (e.g., Any, Callable), or imported types.

Consider expanding the type name whitelist or using a more sophisticated heuristic:

 def _fix_union_patterns(content: str, imports_to_add: set, changes_made: list) -> str:
     """Fix union type patterns (A | B -> Union[A, B])."""
     union_pattern = r'([a-zA-Z_][a-zA-Z0-9_]*)\s*\|\s*([a-zA-Z_][a-zA-Z0-9_]*)'
     union_matches = re.findall(union_pattern, content)
     if union_matches:
         # Filter out matches that are likely not type annotations
         filtered_matches = []
         for left, right in union_matches:
             # Skip if it looks like a bitwise operation in code
             type_names = [
-                'None', 'str', 'int', 'float', 'bool', 'list', 'dict', 'set', 'tuple'
+                'None', 'str', 'int', 'float', 'bool', 'list', 'dict', 'set', 'tuple',
+                'Any', 'Callable', 'Optional', 'Union', 'List', 'Dict', 'Set', 'Tuple',
+                'Type', 'Literal', 'TypeVar', 'Generic'
             ]
-            if not (left in type_names or right in type_names):
+            # Also check if either side looks like a capitalized type name
+            is_likely_type = (left in type_names or right in type_names or 
+                             left[0].isupper() or right[0].isupper())
+            if not is_likely_type:
                 continue
             filtered_matches.append((left, right))

scripts/maintenance/typehint_codemod.py (1)

263-275: Simplify conditional logic.

The nested if-else structure can be simplified using elif.

Apply this diff to improve readability:

-    else:
-        # Add new typing import after last import
-        if last_import_line >= 0:
+    elif last_import_line >= 0:
             import_line = (
                 f"from typing import {', '.join(sorted(imports_to_add))}"
             )
             lines.insert(last_import_line + 1, import_line)
-        else:
+    else:
             import_line = (
                 f"from typing import {', '.join(sorted(imports_to_add))}"
             )
             lines.insert(0, import_line)

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Review profile: CHILL
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• MCP integration is disabled by default for public repositories
• Jira integration is disabled by default for public repositories
• Linear integration is disabled by default for public repositories

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