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https://github.com/Rarebuffalo/securelens-backend.git
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fix: persist code scan results to PostgreSQL (replace in-memory store)
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rarebuffalo
@@ -1,10 +1,41 @@
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import logging
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import uuid
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import json
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from fastapi import APIRouter, HTTPException
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from typing import Dict, Any
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"""
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Code Scan Router
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================
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from app.schemas.code_scan import CodeScanRequest, CodeScanResponse, CodeChatRequest, CodeChatResponse
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Two endpoints:
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POST /code-scan/analyze — Clone repo tree, triage + analyze files with AI,
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persist result to PostgreSQL, return scan_id.
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POST /code-scan/chat — Load the persisted scan from DB and answer questions.
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GET /code-scan/models — List available AI models (debug / informational).
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Why we moved away from in-memory scan_store:
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The original implementation stored scan results in a plain Python dict.
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This caused two critical problems:
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1. Any server restart or crash wiped all stored context, breaking chat.
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2. Multiple Uvicorn workers have isolated memory; a scan on worker A
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cannot be chatted with on worker B.
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By persisting to PostgreSQL we get durability, scalability, and historical
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records of all code scans (same pattern as the web scanner).
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"""
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import logging
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import json
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from fastapi import APIRouter, Depends, HTTPException
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from typing import Dict
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from app.database import get_db
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from app.middleware.auth import get_optional_user
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from app.models.code_scan import CodeScanResult
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from app.models.user import User
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from app.schemas.code_scan import (
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CodeScanRequest,
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CodeScanResponse,
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CodeChatRequest,
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CodeChatResponse,
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VulnerabilityIssue,
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)
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from app.services.code_scanner.orchestrator import CodeScanOrchestrator
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from app.config import settings
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@@ -12,98 +43,159 @@ logger = logging.getLogger(__name__)
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router = APIRouter(tags=["code-scan"])
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# In-memory store for scan results to support chat context.
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# In a real production app, this would be stored in the database.
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scan_store: Dict[str, CodeScanResponse] = {}
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# ---------------------------------------------------------------------------
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# POST /code-scan/analyze
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# ---------------------------------------------------------------------------
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@router.post("/code-scan/analyze", response_model=CodeScanResponse)
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async def analyze_codebase(request: CodeScanRequest):
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async def analyze_codebase(
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request: CodeScanRequest,
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db: AsyncSession = Depends(get_db),
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current_user: User | None = Depends(get_optional_user),
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):
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"""
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Full agentic scan of a GitHub repository.
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Flow:
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1. Fetch the full file tree from GitHub.
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2. Ask the AI to triage (select) the most security-critical files.
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3. Analyze each triaged file for OWASP Top-10 vulnerabilities.
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4. Generate an executive summary of all findings.
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5. Persist the result to `code_scan_results` table.
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6. Return the scan_id so the frontend can open a chat session.
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"""
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logger.info(f"Starting code scan for {request.repo_url}")
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try:
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orchestrator = CodeScanOrchestrator(
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repo_url=request.repo_url,
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github_token=request.github_token,
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branch=request.branch or "main"
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branch=request.branch or "main",
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)
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# 1. Fetch repo structure
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all_files = await orchestrator.github.get_repo_tree(request.repo_url, request.branch or "main")
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# 2. Triage files
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# Step 1 — Fetch file tree
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all_files = await orchestrator.github.get_repo_tree(
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request.repo_url, request.branch or "main"
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)
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# Step 2 — AI triage: pick the most security-sensitive files
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triaged_files = await orchestrator.triage_files(all_files)
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logger.info(f"Triaged {len(triaged_files)} files out of {len(all_files)}.")
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# 3. Analyze triaged files
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# Step 3 — Analyze each file
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vulnerabilities = await orchestrator.analyze_files(triaged_files)
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# 4. Generate Summary
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# Step 4 — Generate summary
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summary = await orchestrator.generate_summary(vulnerabilities)
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scan_id = str(uuid.uuid4())
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response = CodeScanResponse(
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# Step 5 — Persist to database
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# Serialise VulnerabilityIssue objects to plain dicts for JSON storage.
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issues_as_dicts = [v.model_dump() for v in vulnerabilities]
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scan_record = CodeScanResult(
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user_id=current_user.id if current_user else None,
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repo_url=request.repo_url,
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summary=summary,
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issues=issues_as_dicts,
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)
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db.add(scan_record)
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await db.flush() # flush to get the auto-generated id without committing yet
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scan_id = scan_record.id
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logger.info(f"Code scan {scan_id} persisted to database.")
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return CodeScanResponse(
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scan_id=scan_id,
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repo_url=request.repo_url,
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summary=summary,
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issues=vulnerabilities
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issues=vulnerabilities,
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)
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# Save to in-memory store for the chat feature
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scan_store[scan_id] = response
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return response
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except Exception as e:
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logger.error(f"Code scan failed: {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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# ---------------------------------------------------------------------------
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# POST /code-scan/chat
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# ---------------------------------------------------------------------------
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@router.post("/code-scan/chat", response_model=CodeChatResponse)
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async def chat_with_scan(
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request: CodeChatRequest,
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db: AsyncSession = Depends(get_db),
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):
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"""
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Conversational Q&A grounded in a previously completed code scan.
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We load the scan from PostgreSQL using the scan_id, so this works
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correctly across server restarts and multiple workers.
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"""
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if not settings.ai_api_key:
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raise HTTPException(
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status_code=400,
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detail="AI Chat is disabled because no AI API key is configured.",
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)
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# Load the scan record from DB
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result = await db.execute(
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select(CodeScanResult).where(CodeScanResult.id == request.scan_id)
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)
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scan_data = result.scalar_one_or_none()
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if not scan_data:
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raise HTTPException(
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status_code=404,
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detail="Scan ID not found. The scan may have expired or never existed.",
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)
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# Build a rich context-aware prompt
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prompt = (
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"You are SecureLens AI, an expert application security assistant. "
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"You are helping a developer understand a security scan report for their codebase. "
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f"Here is the context of the scan for the repository '{scan_data.repo_url}':\n\n"
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f"Executive Summary:\n{scan_data.summary}\n\n"
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f"Vulnerabilities Found:\n{json.dumps(scan_data.issues, indent=2)}\n\n"
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f"Developer's Question: {request.message}\n\n"
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"Answer clearly, concisely, and professionally. "
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"Provide specific code fixes when requested. "
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"Reference the exact file paths and line numbers from the scan data when relevant."
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)
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try:
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# Use the unified AI service so it respects whatever provider is configured
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from app.services.ai import call_ai
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reply = await call_ai(prompt, temperature=0.5)
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return CodeChatResponse(reply=reply)
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except Exception as e:
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logger.error(f"AI Chat Error: {str(e)}")
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raise HTTPException(
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status_code=500,
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detail="I encountered an error trying to process your request.",
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)
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# ---------------------------------------------------------------------------
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# GET /code-scan/models (informational / debug)
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# ---------------------------------------------------------------------------
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@router.get("/code-scan/models")
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async def list_available_models():
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if not settings.gemini_api_key:
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raise HTTPException(status_code=500, detail="GEMINI_API_KEY is not set.")
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"""
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Lists AI models available to the configured provider.
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Only meaningful when using the Gemini provider.
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"""
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if not settings.ai_api_key:
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raise HTTPException(status_code=500, detail="No AI API key is set.")
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try:
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from google import genai
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client = genai.Client(api_key=settings.gemini_api_key)
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client = genai.Client(api_key=settings.ai_api_key)
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models = []
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for model in client.models.list():
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if 'generateContent' in model.supported_actions:
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if "generateContent" in model.supported_actions:
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models.append(model.name)
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return {"models": models}
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"Error fetching models: {e}")
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@router.post("/code-scan/chat", response_model=CodeChatResponse)
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async def chat_with_scan(request: CodeChatRequest):
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if not settings.gemini_api_key:
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raise HTTPException(status_code=400, detail="AI Chat is disabled because GEMINI_API_KEY is not configured.")
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scan_data = scan_store.get(request.scan_id)
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if not scan_data:
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raise HTTPException(status_code=404, detail="Scan ID not found or expired.")
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prompt = (
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"You are SecureLens AI, an expert application security assistant. "
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"You are helping a developer understand a security scan report for their codebase. "
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f"Here is the context of the scan for the repository {scan_data.repo_url}:\n"
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f"Summary: {scan_data.summary}\n"
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f"Vulnerabilities: {json.dumps([v.model_dump() for v in scan_data.issues])}\n\n"
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f"User Message: {request.message}\n\n"
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"Answer the user's questions clearly, concisely, and professionally. Provide code fixes if requested."
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)
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try:
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from google import genai
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from google.genai import types
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client = genai.Client(api_key=settings.gemini_api_key)
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response = await client.aio.models.generate_content(
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model='gemini-2.0-flash',
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contents=prompt,
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config=types.GenerateContentConfig(
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temperature=0.5,
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)
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)
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reply = response.text or "No response from AI."
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return CodeChatResponse(reply=reply)
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except Exception as e:
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logger.error(f"AI Chat Error: {str(e)}")
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raise HTTPException(status_code=500, detail="I encountered an error trying to process your request.")
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