mirror of
https://github.com/RichieCahill/dotfiles.git
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128 lines
3.7 KiB
Python
Executable File
128 lines
3.7 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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Script to detect "evaluation warning:" in logs and suggest fixes using GitHub Models.
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"""
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import os
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import sys
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import re
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import requests
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import json
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from pathlib import Path
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# Configuration
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GITHUB_TOKEN = os.environ.get("GITHUB_TOKEN")
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GITHUB_REPOSITORY = os.environ.get("GITHUB_REPOSITORY")
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PR_NUMBER = os.environ.get("PR_NUMBER") # If triggered by PR
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RUN_ID = os.environ.get("RUN_ID")
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# GitHub Models API Endpoint (OpenAI compatible)
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# https://github.com/marketplace/models
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API_BASE = "https://models.inference.ai.azure.com"
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# Default to gpt-4o, but allow override via env var
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MODEL_NAME = os.environ.get("MODEL_NAME", "gpt-4o")
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def get_log_content(run_id):
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"""Fetches the logs for a specific workflow run."""
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print(f"Fetching logs for run ID: {run_id}")
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headers = {
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"Authorization": f"Bearer {GITHUB_TOKEN}",
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"Accept": "application/vnd.github+json",
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"X-GitHub-Api-Version": "2022-11-28"
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}
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# List artifacts to find logs (or use jobs API)
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# For simplicity, we might need to use 'gh' cli in the workflow to download logs
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# But let's try to read from a file if passed as argument, which is easier for the workflow
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return None
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def parse_warnings(log_file_path):
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"""Parses the log file for evaluation warnings."""
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warnings = []
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with open(log_file_path, 'r', encoding='utf-8', errors='ignore') as f:
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for line in f:
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if "evaluation warning:" in line:
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warnings.append(line.strip())
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return warnings
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def generate_fix(warning_msg):
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"""Calls GitHub Models to generate a fix for the warning."""
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print(f"Generating fix for: {warning_msg}")
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prompt = f"""
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I encountered the following Nix evaluation warning:
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`{warning_msg}`
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Please explain what this warning means and suggest how to fix it in the Nix code.
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If possible, provide the exact code change in a diff format or a clear description of what to change.
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"""
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {GITHUB_TOKEN}"
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}
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payload = {
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"messages": [
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{
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"role": "system",
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"content": "You are an expert NixOS and Nix language developer."
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},
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{
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"role": "user",
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"content": prompt
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}
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],
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"model": MODEL_NAME,
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"temperature": 0.1
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}
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try:
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response = requests.post(
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f"{API_BASE}/chat/completions",
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headers=headers,
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json=payload
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)
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response.raise_for_status()
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result = response.json()
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return result['choices'][0]['message']['content']
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except Exception as e:
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print(f"Error calling LLM: {e}")
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return None
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def main():
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if len(sys.argv) < 2:
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print("Usage: fix_eval_warnings.py <log_file>")
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sys.exit(1)
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log_file = sys.argv[1]
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if not os.path.exists(log_file):
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print(f"Log file not found: {log_file}")
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sys.exit(1)
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warnings = parse_warnings(log_file)
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if not warnings:
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print("No evaluation warnings found.")
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sys.exit(0)
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print(f"Found {len(warnings)} warnings.")
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# Process unique warnings to save tokens
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unique_warnings = list(set(warnings))
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fixes = []
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for warning in unique_warnings:
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fix = generate_fix(warning)
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if fix:
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fixes.append(f"## Warning\n`{warning}`\n\n## Suggested Fix\n{fix}\n")
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# Output fixes to a markdown file for the PR body
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with open("fix_suggestions.md", "w") as f:
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f.write("# Automated Fix Suggestions\n\n")
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f.write("\n---\n".join(fixes))
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print("Fix suggestions written to fix_suggestions.md")
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if __name__ == "__main__":
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main()
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