ai_engine.py

from typing import Dict
from triage import Finding

def build_prompt(finding: Finding) -> str:
    """
    Build a clear prompt string from one Finding.
    """
    prompt = f"""
You are a security assistant.

Semgrep found a potential issue.

Rule ID: {finding.rule_id}
File: {finding.file_path}
Lines: {finding.start_line}-{finding.end_line}
Message: {finding.message}

Code:
----------------
{finding.code_snippet}
----------------

Tasks:
1. Decide if this issue is actually exploitable in this specific context.
2. If it is exploitable, propose a safer fixed version of the code.
3. Explain briefly (2-3 sentences) why your fix is safer.
Respond in JSON with the keys:
- "is_exploitable": true/false
- "fix": string with fixed code (or empty if not needed)
- "explanation": short text
"""
    return prompt.strip()

def mock_ai_analyze(finding: Finding) -> Dict[str, str]:
    """
    Fake AI analysis for now (no real LLM).
    """
    prompt = build_prompt(finding)

    return {
        "is_exploitable": "unknown",
        "fix": "# TODO: AI-generated fix will go here\n" + finding.code_snippet,
        "explanation": "This is a mock analysis. In the real version, an LLM will read the prompt and return a real fix."
    }