reworked AGENTS.md

.github/workflows/fix_eval_warnings.yml

@@ -31,6 +31,7 @@ jobs:
           GITHUB_TOKEN: ${{ github.token }}
           GITHUB_REPOSITORY: ${{ github.repository }}
           RUN_ID: ${{ github.event.workflow_run.id }}
+          PYTHONPATH: .
         run: |
           python3 python/tools/fix_eval_warnings.py build.log
 

python/tools/fix_eval_warnings.py

@@ -1,52 +1,75 @@
 #!/usr/bin/env python3
-"""
-Script to detect "evaluation warning:" in logs and suggest fixes using GitHub Models.
-"""
+"""fix_eval_warnings."""
+
+from __future__ import annotations
+
+import logging
 import os
-import sys
-import re
-import requests
-import json
+from dataclasses import dataclass
 from pathlib import Path
 
-# Configuration
-GITHUB_TOKEN = os.environ.get("GITHUB_TOKEN")
-GITHUB_REPOSITORY = os.environ.get("GITHUB_REPOSITORY")
-PR_NUMBER = os.environ.get("PR_NUMBER") # If triggered by PR
-RUN_ID = os.environ.get("RUN_ID")
+import requests
+import typer
 
-# GitHub Models API Endpoint (OpenAI compatible)
-# https://github.com/marketplace/models
-API_BASE = "https://models.inference.ai.azure.com"
-# Default to gpt-4o, but allow override via env var
-MODEL_NAME = os.environ.get("MODEL_NAME", "gpt-4o")
+from python.common import configure_logger
 
-def get_log_content(run_id):
-    """Fetches the logs for a specific workflow run."""
-    print(f"Fetching logs for run ID: {run_id}")
-    headers = {
-        "Authorization": f"Bearer {GITHUB_TOKEN}",
-        "Accept": "application/vnd.github+json",
-        "X-GitHub-Api-Version": "2022-11-28"
-    }
+logger = logging.getLogger(__name__)
 
+
+@dataclass
+class Config:
+    """Configuration for the script.
+
+    Attributes:
+        github_token (str): GitHub token for API authentication.
+        model_name (str): The name of the LLM model to use. Defaults to "gpt-4o".
+        api_base (str): The base URL for the GitHub Models API.
+            Defaults to "https://models.inference.ai.azure.com".
+    """
+
+    github_token: str
+    model_name: str = "gpt-4o"
+    api_base: str = "https://models.inference.ai.azure.com"
+
+
+def get_log_content(run_id: str) -> None:
+    """Fetch the logs for a specific workflow run.
+
+    Args:
+        run_id (str): The run ID.
+    """
+    logger.info(f"Fetching logs for run ID: {run_id}")
     # List artifacts to find logs (or use jobs API)
     # For simplicity, we might need to use 'gh' cli in the workflow to download logs
     # But let's try to read from a file if passed as argument, which is easier for the workflow
-    return None
 
-def parse_warnings(log_file_path):
-    """Parses the log file for evaluation warnings."""
+
+def parse_warnings(log_file_path: Path) -> list[str]:
+    """Parse the log file for evaluation warnings.
+
+    Args:
+        log_file_path (Path): The path to the log file.
+
+    Returns:
+        list[str]: A list of warning messages.
+    """
     warnings = []
-    with open(log_file_path, 'r', encoding='utf-8', errors='ignore') as f:
-        for line in f:
-            if "evaluation warning:" in line:
-                warnings.append(line.strip())
+    with log_file_path.open(encoding="utf-8", errors="ignore") as f:
+        warnings.extend(line.strip() for line in f if "evaluation warning:" in line)
     return warnings
 
-def generate_fix(warning_msg):
-    """Calls GitHub Models to generate a fix for the warning."""
-    print(f"Generating fix for: {warning_msg}")
+
+def generate_fix(warning_msg: str, config: Config) -> str | None:
+    """Call GitHub Models to generate a fix for the warning.
+
+    Args:
+        warning_msg (str): The warning message.
+        config (Config): The configuration object.
+
+    Returns:
+        Optional[str]: The suggested fix or None.
+    """
+    logger.info(f"Generating fix for: {warning_msg}")
 
     prompt = f"""
     I encountered the following Nix evaluation warning:
@@ -57,71 +80,82 @@ def generate_fix(warning_msg):
     If possible, provide the exact code change in a diff format or a clear description of what to change.
     """
 
-    headers = {
-        "Content-Type": "application/json",
-        "Authorization": f"Bearer {GITHUB_TOKEN}"
-    }
+    headers = {"Content-Type": "application/json", "Authorization": f"Bearer {config.github_token}"}
 
     payload = {
         "messages": [
-            {
-                "role": "system",
-                "content": "You are an expert NixOS and Nix language developer."
-            },
-            {
-                "role": "user",
-                "content": prompt
-            }
+            {"role": "system", "content": "You are an expert NixOS and Nix language developer."},
+            {"role": "user", "content": prompt},
         ],
-        "model": MODEL_NAME,
-        "temperature": 0.1
+        "model": config.model_name,
+        "temperature": 0.1,
     }
 
     try:
-        response = requests.post(
-            f"{API_BASE}/chat/completions",
-            headers=headers,
-            json=payload
-        )
+        response = requests.post(f"{config.api_base}/chat/completions", headers=headers, json=payload, timeout=30)
         response.raise_for_status()
         result = response.json()
-        return result['choices'][0]['message']['content']
-    except Exception as e:
-        print(f"Error calling LLM: {e}")
+        return result["choices"][0]["message"]["content"]  # type: ignore[no-any-return]
+    except Exception:
+        logger.exception("Error calling LLM")
         return None
 
-def main():
-    if len(sys.argv) < 2:
-        print("Usage: fix_eval_warnings.py <log_file>")
-        sys.exit(1)
 
-    log_file = sys.argv[1]
-    if not os.path.exists(log_file):
-        print(f"Log file not found: {log_file}")
-        sys.exit(1)
+def main(
+    log_file: Path = typer.Argument(..., help="Path to the build log file"),  # noqa: B008
+    model_name: str = typer.Option("gpt-4o", envvar="MODEL_NAME", help="LLM Model Name"),
+) -> None:
+    """Detect evaluation warnings in logs and suggest fixes using GitHub Models.
+
+    Args:
+        log_file (Path): Path to the build log file containing evaluation warnings.
+        model_name (str): The name of the LLM model to use for generating fixes.
+            Defaults to "gpt-4o", can be overridden by MODEL_NAME environment variable.
+    """
+    configure_logger()
+
+    github_token = os.environ.get("GITHUB_TOKEN")
+    if not github_token:
+        logger.warning("GITHUB_TOKEN not set. LLM calls will fail.")
+
+    config = Config(github_token=github_token or "", model_name=model_name)
+
+    if not log_file.exists():
+        logger.error(f"Log file not found: {log_file}")
+        raise typer.Exit(code=1)
 
     warnings = parse_warnings(log_file)
     if not warnings:
-        print("No evaluation warnings found.")
-        sys.exit(0)
+        logger.info("No evaluation warnings found.")
+        raise typer.Exit(code=0)
 
-    print(f"Found {len(warnings)} warnings.")
+    logger.info(f"Found {len(warnings)} warnings.")
 
     # Process unique warnings to save tokens
     unique_warnings = list(set(warnings))
 
     fixes = []
     for warning in unique_warnings:
-        fix = generate_fix(warning)
+        if not config.github_token:
+            logger.warning("Skipping LLM call due to missing GITHUB_TOKEN")
+            continue
+
+        fix = generate_fix(warning, config)
         if fix:
             fixes.append(f"## Warning\n`{warning}`\n\n## Suggested Fix\n{fix}\n")
 
     # Output fixes to a markdown file for the PR body
-    with open("fix_suggestions.md", "w") as f:
-        f.write("# Automated Fix Suggestions\n\n")
-        f.write("\n---\n".join(fixes))
+    if fixes:
+        with Path("fix_suggestions.md").open("w") as f:
+            f.write("# Automated Fix Suggestions\n\n")
+            f.write("\n---\n".join(fixes))
+        logger.info("Fix suggestions written to fix_suggestions.md")
+    else:
+        logger.info("No fixes generated.")
 
-    print("Fix suggestions written to fix_suggestions.md")
+
+app = typer.Typer()
+app.command()(main)
 
 if __name__ == "__main__":
-    main()
+    app()

tests/test_fix_eval_warnings.py

@@ -0,0 +1,62 @@
+"""Tests for fix_eval_warnings."""
+
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+import pytest
+from typer.testing import CliRunner
+
+from python.tools.fix_eval_warnings import Config, app, generate_fix, parse_warnings
+
+runner = CliRunner()
+
+
+@pytest.fixture
+def log_file(tmp_path: Path) -> Path:
+    """Create a dummy log file."""
+    log_path = tmp_path / "build.log"
+    log_path.write_text("Some output\nevaluation warning: 'system' is deprecated\nMore output", encoding="utf-8")
+    return log_path
+
+
+def test_parse_warnings(log_file: Path) -> None:
+    """Test parsing warnings from a log file."""
+    warnings = parse_warnings(log_file)
+    assert len(warnings) == 1
+    assert warnings[0] == "evaluation warning: 'system' is deprecated"
+
+
+@patch("python.tools.fix_eval_warnings.requests.post")
+def test_generate_fix(mock_post: MagicMock) -> None:
+    """Test generating a fix."""
+    mock_response = MagicMock()
+    mock_response.json.return_value = {"choices": [{"message": {"content": "Use stdenv.hostPlatform.system"}}]}
+    mock_post.return_value = mock_response
+
+    config = Config(github_token="dummy_token")
+    fix = generate_fix("evaluation warning: 'system' is deprecated", config)
+
+    assert fix == "Use stdenv.hostPlatform.system"
+    mock_post.assert_called_once()
+
+
+@patch("python.tools.fix_eval_warnings.logger")
+@patch("python.tools.fix_eval_warnings.generate_fix")
+def test_main(mock_generate_fix: MagicMock, mock_logger: MagicMock, log_file: Path) -> None:
+    """Test the main CLI."""
+    mock_generate_fix.return_value = "Fixed it"
+
+    # We need to mock GITHUB_TOKEN env var or the script will warn/fail
+    with patch.dict("os.environ", {"GITHUB_TOKEN": "dummy"}):
+        result = runner.invoke(app, [str(log_file)])
+
+    assert result.exit_code == 0
+    # Verify logger calls instead of stdout, as CliRunner might not capture logging output correctly
+    # when logging is configured to write to sys.stdout directly.
+    assert any("Found 1 warnings" in str(call) for call in mock_logger.info.call_args_list)
+    assert any(
+        "Fix suggestions written to fix_suggestions.md" in str(call)
+        for call in mock_logger.info.call_args_list
+    )
+    assert Path("fix_suggestions.md").exists()
+    Path("fix_suggestions.md").unlink()