Fix recent scans view button and keep scan results when refreshing or bookmarking #308

README.md

@@ -87,6 +87,7 @@ Open `http://localhost:5173` in your browser.
 | Method | Route | Description |
 |---|---|---|
 | `GET` | `/health` | Health check |
+| `GET` | `/jobs` | List scan jobs (paginated) |
 | `POST` | `/scan` | Upload ZIP and scan |
 | `POST` | `/scan-url` | Import GitHub repo URL and scan |
 | `POST` | `/fix` | Generate proposed fixes |

backend/README.md

@@ -30,3 +30,17 @@ SQLite database (`patchpilot.db`) is auto-created in `backend/` on first server
 | `scanner`     | TEXT    | `semgrep`, `osv`, or `gitleaks`                           |
 | `message`     | TEXT    | Description of the finding                                |
 | `created_at`  | TEXT    | Timestamp of finding creation                             |
+
+## API Reference
+
+| Method | Route | Description |
+|---|---|---|
+| `GET` | `/health` | Health check |
+| `GET` | `/jobs` | List scan jobs (paginated) |
+| `GET` | `/jobs/{job_id}/findings` | Get findings for a specific job |
+| `DELETE` | `/jobs/{job_id}` | Delete a job workspace |
+| `POST` | `/scan` | Upload ZIP and scan |
+| `POST` | `/scan-url` | Import GitHub repo URL and scan |
+| `POST` | `/fix` | Generate proposed fixes |
+| `POST` | `/verify` | Verify fixes |
+| `POST` | `/evidence-pack` | Build and download evidence ZIP |

backend/app/main.py

@@ -8,6 +8,7 @@
 import random
 import re
 import shutil
+import sys
 import tempfile
 import threading
 import uuid
@@ -82,6 +83,11 @@
 except ImportError:  # Python 3.10
     import tomli as tomllib  # type: ignore[no-redef]
 
+# Prepend the virtual environment's executable directory to PATH to auto-detect scanners
+venv_bin = os.path.dirname(sys.executable)
+if venv_bin and venv_bin not in os.environ.get("PATH", ""):
+    os.environ["PATH"] = venv_bin + os.pathsep + os.environ.get("PATH", "")
+
 _MAX_UPLOAD_MB_RAW = os.environ.get("MAX_UPLOAD_MB")
 RANKER = load_ranker()
 
@@ -1215,6 +1221,38 @@ async def download_audit_pdf(job_id: str):
     )
 
 
+@app.get(
+    "/jobs",
+    dependencies=[Depends(verify_api_key)],
+)
+async def list_jobs(
+    limit: int = Query(20, ge=1, le=100),
+    offset: int = Query(0, ge=0),
+):
+    db = await get_db()
+    try:
+        db.row_factory = aiosqlite.Row
+        cur = await db.execute("SELECT COUNT(*) as total FROM jobs")
+        total_row = await cur.fetchone()
+        total = total_row["total"] if total_row else 0
+
+        cur = await db.execute(
+            """
+            SELECT job_id, project_name, scan_method, status,
+                   finding_count, raw_finding_count, created_at
+            FROM jobs
+            ORDER BY created_at DESC
+            LIMIT ? OFFSET ?
+            """,
+            (limit, offset),
+        )
+        rows = await cur.fetchall()
+    finally:
+        await db.close()
+
+    return {"total": total, "jobs": [dict(r) for r in rows]}
+
+
 @app.get(
     "/jobs/{job_id}/findings",
     dependencies=[Depends(verify_api_key)],
@@ -1242,6 +1280,7 @@ async def get_findings(job_id: str):
 
     return {
         "job_id": job_id,
+        "project_name": job_row.get("project_name") if job_row else None,
         "raw_finding_count": raw_finding_count,
         "finding_count": finding_count,
         "findings": findings,

backend/app/ml/deduplicator.py

@@ -1,7 +1,10 @@
+import logging
 from collections import defaultdict
 
 from sklearn.cluster import DBSCAN
 
+logger = logging.getLogger(__name__)
+
 
 def get_model():
     return None
@@ -41,7 +44,11 @@ def deduplicate(
     if not SENTENCE_TRANSFORMERS_AVAILABLE:
         return findings
 
-    embeddings = embed_findings(findings)
+    try:
+        embeddings = embed_findings(findings)
+    except Exception as exc:
+        logger.warning("Finding deduplication skipped: %s", exc)
+        return findings
 
     clustering = DBSCAN(
         eps=epsilon,

backend/app/ml/embedder.py

@@ -1,11 +1,31 @@
+import logging
+
 import numpy as np
 
+logger = logging.getLogger(__name__)
+
 try:
     from sentence_transformers import SentenceTransformer
-
-    MODEL = SentenceTransformer("all-MiniLM-L6-v2")
 except ImportError:
-    MODEL = None
+    SentenceTransformer = None
+
+MODEL = None
+
+
+def _get_model():
+    global MODEL
+
+    if SentenceTransformer is None:
+        raise RuntimeError(
+            "sentence-transformers is not installed. "
+            "Install it using: pip install sentence-transformers"
+        )
+
+    if MODEL is None:
+        logger.info("Loading sentence-transformers model for deduplication...")
+        MODEL = SentenceTransformer("all-MiniLM-L6-v2")
+
+    return MODEL
 
 
 def embed_findings(findings: list[dict]) -> np.ndarray:
@@ -18,15 +38,9 @@ def embed_findings(findings: list[dict]) -> np.ndarray:
     Returns:
         np.ndarray of shape (n, 384)
     """
-    if MODEL is None:
-        raise RuntimeError(
-            "sentence-transformers is not installed. "
-            "Install it using: pip install sentence-transformers"
-        )
-
     texts = [
         f"{getattr(finding, 'title', '')} {getattr(finding, 'description', '')}"
         for finding in findings
     ]
 
-    return MODEL.encode(texts, convert_to_numpy=True)
+    return _get_model().encode(texts, convert_to_numpy=True)

backend/app/ml/fp_predictor.py

@@ -2,8 +2,17 @@
 import os
 
 import joblib
-import torch
-from transformers import AutoModel, AutoTokenizer
+
+try:
+    import torch
+    from transformers import AutoModel, AutoTokenizer
+
+    TORCH_AND_TRANSFORMERS_AVAILABLE = True
+except ImportError:
+    torch = None
+    AutoModel = None
+    AutoTokenizer = None
+    TORCH_AND_TRANSFORMERS_AVAILABLE = False
 
 logger = logging.getLogger(__name__)
 
@@ -25,6 +34,13 @@ def _load_models(self):
         if self._models_loaded:
             return
 
+        if not TORCH_AND_TRANSFORMERS_AVAILABLE:
+            logger.warning(
+                "torch or transformers is not installed. ML inference will be skipped."
+            )
+            self._models_loaded = True
+            return
+
         if not os.path.exists(CLASSIFIER_PATH):
             logger.warning(
                 f"FP Classifier not found at {CLASSIFIER_PATH}. ML inference will be skipped."
@@ -66,7 +82,7 @@ def adjust_scores(self, findings: list[dict]) -> list[float]:
             for f in findings
         ]
 
-        if not self.is_ready or not findings:
+        if not TORCH_AND_TRANSFORMERS_AVAILABLE or not self.is_ready or not findings:
             return scores
 
         try:

backend/tests/conftest.py

@@ -0,0 +1,15 @@
+import sys
+from unittest.mock import MagicMock
+
+
+class DummyTensor:
+    pass
+
+
+# Mock out heavy ML libraries to allow running tests without them
+mock_torch = MagicMock()
+mock_torch.Tensor = DummyTensor
+sys.modules["torch"] = mock_torch
+
+sys.modules["transformers"] = MagicMock()
+sys.modules["sentence_transformers"] = MagicMock()

backend/tests/test_job_endpoints.py

@@ -219,3 +219,60 @@ def test_stream_job_completed(self):
 
         if JOB_ID in ACTIVE_SCANS:
             del ACTIVE_SCANS[JOB_ID]
+
+
+class TestListJobs:
+    def test_list_jobs_happy_path(self):
+        mock_jobs = [
+            {
+                "job_id": "job1",
+                "project_name": "repo1",
+                "scan_method": "zip",
+                "status": "completed",
+                "finding_count": 5,
+                "raw_finding_count": 10,
+                "created_at": "2026-07-15 20:00:00",
+            },
+            {
+                "job_id": "job2",
+                "project_name": "repo2",
+                "scan_method": "url",
+                "status": "scanning",
+                "finding_count": None,
+                "raw_finding_count": None,
+                "created_at": "2026-07-15 19:00:00",
+            },
+        ]
+
+        count_cur = AsyncMock()
+        count_cur.fetchone = AsyncMock(return_value={"total": 2})
+
+        jobs_cur = AsyncMock()
+        jobs_cur.fetchall = AsyncMock(return_value=mock_jobs)
+
+        db = AsyncMock()
+        db.execute = AsyncMock(side_effect=[count_cur, jobs_cur])
+        db.__aenter__ = AsyncMock(return_value=db)
+        db.__aexit__ = AsyncMock(return_value=False)
+
+        with patch("app.main.get_db", AsyncMock(return_value=db)):
+            res = client.get("/jobs?limit=20&offset=0")
+
+        assert res.status_code == 200
+        data = res.json()
+        assert data["total"] == 2
+        assert len(data["jobs"]) == 2
+        assert data["jobs"][0]["job_id"] == "job1"
+        assert data["jobs"][1]["job_id"] == "job2"
+
+    def test_list_jobs_invalid_limit_too_low(self):
+        res = client.get("/jobs?limit=0")
+        assert res.status_code == 422
+
+    def test_list_jobs_invalid_limit_too_high(self):
+        res = client.get("/jobs?limit=101")
+        assert res.status_code == 422
+
+    def test_list_jobs_invalid_offset(self):
+        res = client.get("/jobs?offset=-1")
+        assert res.status_code == 422