chore(deps): bump the frontend-patches group across 1 directory with 12 updates

backend/backend/__init__.py

@@ -1,4 +1,5 @@
 """Compatibility package for running commands from the backend directory."""
+
 from __future__ import annotations
 
 from pathlib import Path

backend/config.py

@@ -1,9 +1,11 @@
 import os
-from dotenv import load_dotenv
 from pathlib import Path
 
+from dotenv import load_dotenv
+
 load_dotenv()
 
+
 def _normalize_database_url(url: str) -> str:
     if url.startswith("postgres://"):
         return url.replace("postgres://", "postgresql+asyncpg://", 1)
@@ -36,17 +38,23 @@ def _cors_origins(environment: str, raw_origins: str | None) -> list[str]:
     return _parse_csv(DEFAULT_LOCAL_CORS_ORIGINS)
 
 
-ANTHROPIC_API_KEY  = os.getenv("ANTHROPIC_API_KEY", "")
-GEMINI_API_KEY     = os.getenv("GEMINI_API_KEY", "")
-GITHUB_TOKEN       = os.getenv("GITHUB_TOKEN", "")
-DATABASE_URL       = _normalize_database_url(os.getenv("DATABASE_URL", "sqlite+aiosqlite:///./commitiq.db"))
-REPO_STORAGE_PATH  = Path(os.getenv("REPO_STORAGE_PATH", "/tmp/commitiq_repos"))
-MAX_COMMITS        = int(os.getenv("MAX_COMMITS_PER_INGESTION", os.getenv("MAX_COMMITS", "500")))
-LLM_MAX_CALLS      = int(os.getenv("LLM_BUDGET_PER_REPO", os.getenv("LLM_MAX_CALLS_PER_REPO", os.getenv("LLM_MAX_CALLS", "25"))))
+ANTHROPIC_API_KEY = os.getenv("ANTHROPIC_API_KEY", "")
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY", "")
+GITHUB_TOKEN = os.getenv("GITHUB_TOKEN", "")
+DATABASE_URL = _normalize_database_url(
+    os.getenv("DATABASE_URL", "sqlite+aiosqlite:///./commitiq.db")
+)
+REPO_STORAGE_PATH = Path(os.getenv("REPO_STORAGE_PATH", "/tmp/commitiq_repos"))
+MAX_COMMITS = int(os.getenv("MAX_COMMITS_PER_INGESTION", os.getenv("MAX_COMMITS", "500")))
+LLM_MAX_CALLS = int(
+    os.getenv(
+        "LLM_BUDGET_PER_REPO", os.getenv("LLM_MAX_CALLS_PER_REPO", os.getenv("LLM_MAX_CALLS", "25"))
+    )
+)
 LLM_BUDGET_PER_REPO_USD = float(os.getenv("LLM_BUDGET_PER_REPO_USD", "0.50"))
 ENABLE_SEMANTIC_ANALYSIS = _parse_bool(os.getenv("ENABLE_SEMANTIC_ANALYSIS"), default=True)
 ENABLE_GRAPHCODEBERT = _parse_bool(os.getenv("ENABLE_GRAPHCODEBERT"), default=False)
-ENVIRONMENT        = os.getenv("ENVIRONMENT", "development")
-CORS_ORIGINS       = _cors_origins(ENVIRONMENT, os.getenv("CORS_ORIGINS"))
+ENVIRONMENT = os.getenv("ENVIRONMENT", "development")
+CORS_ORIGINS = _cors_origins(ENVIRONMENT, os.getenv("CORS_ORIGINS"))
 
 REPO_STORAGE_PATH.mkdir(parents=True, exist_ok=True)

backend/database.py

@@ -60,14 +60,9 @@ async def _execute_statement(conn, statement: str, *, is_sqlite: bool = _IS_SQLI
 def _migration_statements(migration_sql: str) -> list[str]:
     migration_lines = migration_sql.splitlines()
     uncommented_sql = "\n".join(
-        line for line in migration_lines
-        if not line.lstrip().startswith("--")
+        line for line in migration_lines if not line.lstrip().startswith("--")
     )
-    return [
-        statement.strip()
-        for statement in uncommented_sql.split(";")
-        if statement.strip()
-    ]
+    return [statement.strip() for statement in uncommented_sql.split(";") if statement.strip()]
 
 
 async def _ensure_migration_table(conn) -> None:

backend/features/llm_analysis/claude_client.py

@@ -1,31 +1,34 @@
 """LLM call wrapper for on-demand CommitIQ narratives."""
+
 from typing import AsyncGenerator
 
-from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
 
-from backend.shared.models import LLMNarrative, Commit, HealthSnapshot
+from backend.config import ANTHROPIC_API_KEY
 from backend.features.llm_analysis.cache import make_cache_key
-from backend.features.llm_analysis.cost_guard import check_budget
-from backend.features.llm_analysis.cost_guard import estimate_cost_usd
+from backend.features.llm_analysis.cost_guard import check_budget, estimate_cost_usd
 from backend.features.llm_analysis.llm_router import get_narrative_non_streaming, model_for_provider
 from backend.features.llm_analysis.prompt_builder import (
-    EXPLAIN_DROP_SYSTEM, PREDICT_MERGE_SYSTEM,
-    build_explain_prompt, build_predict_prompt
+    EXPLAIN_DROP_SYSTEM,
+    PREDICT_MERGE_SYSTEM,
+    build_explain_prompt,
+    build_predict_prompt,
 )
-from backend.config import ANTHROPIC_API_KEY, GEMINI_API_KEY
+from backend.shared.models import Commit, HealthSnapshot, LLMNarrative
 
 # Sonnet pricing (as of 2025):
 # Input:  $3.00 per 1M tokens = $0.000003 per token
 # Output: $15.00 per 1M tokens = $0.000015 per token
-INPUT_COST_PER_TOKEN  = 0.000003
+INPUT_COST_PER_TOKEN = 0.000003
 OUTPUT_COST_PER_TOKEN = 0.000015
 CLAUDE_MODEL = "claude-3-5-sonnet-20241022"
 
 
 def _get_anthropic():
     try:
         import anthropic
+
         return anthropic
     except ImportError:
         return None
@@ -34,6 +37,7 @@ def _get_anthropic():
 def _get_genai():
     try:
         import google.generativeai as genai
+
         return genai
     except ImportError:
         return None
@@ -67,7 +71,7 @@ async def stream_narrative(prompt: str) -> AsyncGenerator[str, None]:
             "You are a code health analyst. Given metric deltas for a Git commit, "
             "explain in plain English why the health score changed. Be specific about "
             "which files and metrics drove the change. Use bullet points. Max 4 sentences. "
-            "Always end with: \"Estimated impact: [Low/Medium/High] risk to next sprint.\" "
+            'Always end with: "Estimated impact: [Low/Medium/High] risk to next sprint." '
             "Never mention token costs or that you're an AI."
         ),
     ) as stream:
@@ -93,10 +97,12 @@ async def get_or_create_narrative(
     Checks cache → checks budget → fires API → stores result.
     """
     commit_result = await db.execute(
-        select(Commit).where(
+        select(Commit)
+        .where(
             Commit.repo_id == repo_id,
             Commit.sha == commit_sha[:12],
-        ).limit(1)
+        )
+        .limit(1)
     )
     commit = commit_result.scalar_one_or_none()
     if not commit:
@@ -116,11 +122,11 @@ async def get_or_create_narrative(
             "prompt_type": prompt_type,
             "explanation": cached.response_text,
             "tokens_used": cached.tokens_input + cached.tokens_output,
-            "cost_usd":    cached.cost_usd,
-            "cached":      True,
-            "model":       cached.model_used,
-            "provider":    "cache",
-            "demo_mode":   False,
+            "cost_usd": cached.cost_usd,
+            "cached": True,
+            "model": cached.model_used,
+            "provider": "cache",
+            "demo_mode": False,
         }
 
     # 3. Check budget — hard limit
@@ -135,23 +141,26 @@ async def get_or_create_narrative(
         raise ValueError(f"No health snapshot for commit {commit_sha}")
 
     after_dict = {
-        "health_score":      snapshot.health_score,
-        "avg_complexity":    snapshot.avg_complexity,
-        "churn_rate":        snapshot.churn_rate,
+        "health_score": snapshot.health_score,
+        "avg_complexity": snapshot.avg_complexity,
+        "churn_rate": snapshot.churn_rate,
         "num_files_changed": snapshot.num_files_changed,
-        "bus_factor_min":    snapshot.bus_factor_min,
-        "top_files_json":    snapshot.top_files_json,
+        "bus_factor_min": snapshot.bus_factor_min,
+        "top_files_json": snapshot.top_files_json,
         "avg_semantic_drift": snapshot.avg_semantic_drift,
         "semantic_health_score": snapshot.semantic_health_score,
         "high_drift_files": snapshot.high_drift_files,
         "semantic_drift_method": snapshot.semantic_drift_method,
     }
 
     prev_commit_result = await db.execute(
-        select(Commit).where(
+        select(Commit)
+        .where(
             Commit.repo_id == repo_id,
             Commit.committed_at < commit.committed_at,
-        ).order_by(Commit.committed_at.desc()).limit(1)
+        )
+        .order_by(Commit.committed_at.desc())
+        .limit(1)
     )
     prev_commit = prev_commit_result.scalar_one_or_none()
 
@@ -161,7 +170,7 @@ async def get_or_create_narrative(
         )
         prev_snap = prev_snap_result.scalar_one_or_none()
         before_dict = {
-            "health_score":   prev_snap.health_score if prev_snap else 0,
+            "health_score": prev_snap.health_score if prev_snap else 0,
             "avg_complexity": prev_snap.avg_complexity if prev_snap else 0,
             "bus_factor_min": prev_snap.bus_factor_min if prev_snap else 1,
         }
@@ -227,9 +236,9 @@ async def get_or_create_narrative(
         "prompt_type": prompt_type,
         "explanation": response_text,
         "tokens_used": tokens_in + tokens_out,
-        "cost_usd":    round(cost, 5),
-        "cached":      False,
-        "model":       model_used,
-        "provider":    provider_used,
-        "demo_mode":   demo_mode,
+        "cost_usd": round(cost, 5),
+        "cached": False,
+        "model": model_used,
+        "provider": provider_used,
+        "demo_mode": demo_mode,
     }

backend/features/llm_analysis/cost_guard.py

@@ -1,9 +1,9 @@
-from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy import select
-from backend.shared.models import LLMNarrative
+from sqlalchemy.ext.asyncio import AsyncSession
+
 from backend.config import LLM_BUDGET_PER_REPO_USD, LLM_MAX_CALLS
 from backend.features.llm_analysis.llm_router import provider_from_model
-
+from backend.shared.models import LLMNarrative
 
 PROVIDER_COSTS_PER_1K_OUTPUT = {
     "anthropic": 0.015,
@@ -44,8 +44,12 @@ async def get_usage_summary(repo_id: int, db: AsyncSession) -> dict:
     cached_rows = [row for row in rows if row.is_pre_cached]
     total_tokens = sum(row.tokens_input + row.tokens_output for row in provider_rows)
     total_cost = sum(float(row.cost_usd or 0.0) for row in provider_rows)
-    anthropic_calls = sum(1 for row in provider_rows if provider_from_model(row.model_used) == "anthropic")
-    gemini_calls = sum(1 for row in provider_rows if provider_from_model(row.model_used) == "gemini")
+    anthropic_calls = sum(
+        1 for row in provider_rows if provider_from_model(row.model_used) == "anthropic"
+    )
+    gemini_calls = sum(
+        1 for row in provider_rows if provider_from_model(row.model_used) == "gemini"
+    )
     cached_tokens = sum(row.tokens_input + row.tokens_output for row in cached_rows)
     cache_hits = len(cached_rows)
     return {
@@ -56,7 +60,9 @@ async def get_usage_summary(repo_id: int, db: AsyncSession) -> dict:
         "gemini_calls": gemini_calls,
         "total_tokens": total_tokens,
         "total_cost_usd": round(float(total_cost or 0.0), 6),
-        "cache_savings_usd": round((cached_tokens / 1000.0) * PROVIDER_COSTS_PER_1K_OUTPUT["anthropic"], 6),
+        "cache_savings_usd": round(
+            (cached_tokens / 1000.0) * PROVIDER_COSTS_PER_1K_OUTPUT["anthropic"], 6
+        ),
         "budget_remaining": max(0, LLM_MAX_CALLS - len(provider_rows)),
         "max_calls": LLM_MAX_CALLS,
     }

backend/features/llm_analysis/llm_router.py

@@ -1,4 +1,5 @@
 """Multi-provider LLM routing for CommitIQ narratives."""
+
 from __future__ import annotations
 
 import asyncio
@@ -76,7 +77,9 @@ async def stream_narrative(
         except Exception as exc:
             logger.error("Gemini fallback failed: %s", exc)
 
-    raise RuntimeError("All LLM providers unavailable. Configure ANTHROPIC_API_KEY or GEMINI_API_KEY.")
+    raise RuntimeError(
+        "All LLM providers unavailable. Configure ANTHROPIC_API_KEY or GEMINI_API_KEY."
+    )
 
 
 async def _stream_anthropic(prompt: str, max_tokens: int) -> AsyncGenerator[str, None]:
@@ -127,7 +130,9 @@ def _sync_stream():
             yield text
 
 
-async def get_narrative_non_streaming(prompt: str, max_tokens: int = 600) -> tuple[str, LLMProvider]:
+async def get_narrative_non_streaming(
+    prompt: str, max_tokens: int = 600
+) -> tuple[str, LLMProvider]:
     full_text: list[str] = []
     provider_used = LLMProvider.NONE
     async for token, provider in stream_narrative(prompt, max_tokens):

backend/features/llm_analysis/prompt_builder.py

@@ -4,8 +4,8 @@
 Input is always a JSON diff of metric values.
 Target: under 700 input tokens.
 """
-import json
 
+import json
 
 EXPLAIN_DROP_SYSTEM = """You are a senior engineering analyst reviewing codebase health data.
 Given two commit health snapshots and their metric diff, explain in 3-4 sentences why the
@@ -31,21 +31,19 @@ def build_explain_prompt(
     Total cost: ~$0.003–0.004 per call.
     """
     diff = {
-        "commit_message":   commit_msg[:120],
-        "health_before":    before.get("health_score", 0),
-        "health_after":     after.get("health_score", 0),
-        "health_delta":     round(
-            after.get("health_score", 0) - before.get("health_score", 0), 2
-        ),
+        "commit_message": commit_msg[:120],
+        "health_before": before.get("health_score", 0),
+        "health_after": after.get("health_score", 0),
+        "health_delta": round(after.get("health_score", 0) - before.get("health_score", 0), 2),
         "complexity_before": before.get("avg_complexity", 0),
-        "complexity_after":  after.get("avg_complexity", 0),
-        "complexity_delta":  round(
+        "complexity_after": after.get("avg_complexity", 0),
+        "complexity_delta": round(
             after.get("avg_complexity", 0) - before.get("avg_complexity", 0), 2
         ),
-        "churn_rate":        after.get("churn_rate", 0),
-        "files_changed":     after.get("num_files_changed", 0),
+        "churn_rate": after.get("churn_rate", 0),
+        "files_changed": after.get("num_files_changed", 0),
         "bus_factor_before": before.get("bus_factor_min", 1),
-        "bus_factor_after":  after.get("bus_factor_min", 1),
+        "bus_factor_after": after.get("bus_factor_min", 1),
         "avg_semantic_drift": after.get("avg_semantic_drift", 0),
         "semantic_health_score": after.get("semantic_health_score", 100),
         "high_drift_files": after.get("high_drift_files", 0),
@@ -62,15 +60,15 @@ def build_predict_prompt(branch_metrics: dict, main_metrics: dict) -> str:
     Cost: ~$0.002–0.003 per call.
     """
     comparison = {
-        "main_health":     main_metrics.get("health_score", 0),
-        "branch_health":   branch_metrics.get("health_score", 0),
-        "health_delta":    round(
+        "main_health": main_metrics.get("health_score", 0),
+        "branch_health": branch_metrics.get("health_score", 0),
+        "health_delta": round(
             branch_metrics.get("health_score", 0) - main_metrics.get("health_score", 0), 2
         ),
-        "complexity_main":   main_metrics.get("avg_complexity", 0),
+        "complexity_main": main_metrics.get("avg_complexity", 0),
         "complexity_branch": branch_metrics.get("avg_complexity", 0),
-        "churn_branch":      branch_metrics.get("churn_rate", 0),
+        "churn_branch": branch_metrics.get("churn_rate", 0),
         "bus_factor_branch": branch_metrics.get("bus_factor_min", 1),
-        "files_to_merge":    branch_metrics.get("num_files_changed", 0),
+        "files_to_merge": branch_metrics.get("num_files_changed", 0),
     }
     return f"Predict health impact of merging this branch:\n{json.dumps(comparison, indent=2)}"