Add HF token for ZeroGPU auth

backend/app/api/routes.py

@@ -28,6 +28,16 @@ logger = get_logger("api")
 router = APIRouter(prefix="/api")
 
 
+# ── Config ─────────────────────────────────────────────────────────────
+
+@router.get("/config")
+async def get_config():
+    """Return backend configuration to the frontend (e.g. HF_TOKEN for ZeroGPU)."""
+    return {
+        "hf_token": os.environ.get("HF_TOKEN", "")
+    }
+
+
 # ── Health ─────────────────────────────────────────────────────────────
 
 @router.get("/health")

debug_analysis.py

@@ -0,0 +1,29 @@
+import asyncio
+from app import run_analysis_fn, create_case_fn, upload_photos_fn, ensure_init
+from backend.app.db.database import db
+
+async def setup():
+    ensure_init()
+    # Create a case
+    msg, _ = create_case_fn("TEST-001", "Officer John", "NYC", "2023-01-01", "Notes")
+    # Add a mock photo
+    import os
+    from PIL import Image
+    os.makedirs("test_photos", exist_ok=True)
+    img_path = "test_photos/mock.jpg"
+    Image.new("RGB", (100, 100)).save(img_path)
+    
+    # upload
+    upload_photos_fn(1, [img_path])
+    
+    # Run analysis
+    try:
+        res = run_analysis_fn(1)
+        print("Analysis result:", res)
+    except Exception as e:
+        print("Error during analysis:", e)
+        import traceback
+        traceback.print_exc()
+
+if __name__ == "__main__":
+    asyncio.run(setup())

frontend/js/alt_app.js

@@ -22,10 +22,27 @@ let additionalFiles = []; // For add photos modal
 async function callGradioApi(apiName, dataArr) {
     const GRADIO_API = '/gradio_api/call/';
 
+    // Fetch config to get Hugging Face Token for ZeroGPU authentication
+    let hfToken = '';
+    try {
+        const confRes = await fetch(`${API_BASE}/config`);
+        if (confRes.ok) {
+            const confData = await confRes.json();
+            hfToken = confData.hf_token || '';
+        }
+    } catch (e) {
+        console.warn('Failed to fetch config', e);
+    }
+
+    const headers = { 'Content-Type': 'application/json' };
+    if (hfToken) {
+        headers['Authorization'] = `Bearer ${hfToken}`;
+    }
+
     // Step 1: POST to queue the job
     const res = await fetch(GRADIO_API + apiName, {
         method: 'POST',
-        headers: { 'Content-Type': 'application/json' },
+        headers: headers,
         credentials: 'include',
         body: JSON.stringify({ data: dataArr })
     });
@@ -40,9 +57,14 @@ async function callGradioApi(apiName, dataArr) {
     // Step 2: Stream the result via fetch (not EventSource)
     const streamUrl = GRADIO_API + apiName + '/' + eventId;
 
+    const streamHeaders = { 'Accept': 'text/event-stream' };
+    if (hfToken) {
+        streamHeaders['Authorization'] = `Bearer ${hfToken}`;
+    }
+
     const sseRes = await fetch(streamUrl, {
         method: 'GET',
-        headers: { 'Accept': 'text/event-stream' },
+        headers: streamHeaders,
         credentials: 'include',
         cache: 'no-cache',
     });

frontend/js/app.js

@@ -631,39 +631,7 @@ function renderFaultAnalysis(fault, parties) {
 
 // ── Run Analysis ──────────────────────────────────────────────────────
 
-async function runAnalysis() {
-    if (!currentCaseId) return;
-
-    const overlay = document.getElementById('analysis-overlay');
-    const stepEl = document.getElementById('analysis-step');
-    const detailEl = document.getElementById('analysis-detail');
-
-    overlay.classList.remove('hidden');
-    stepEl.textContent = 'Analyzing accident scene photos...';
-    detailEl.textContent = 'Running AI vision analysis on each photo. This may take several minutes.';
 
-    try {
-        const resp = await fetch(`${API_BASE}/cases/${currentCaseId}/analyze`, {
-            method: 'POST',
-        });
-
-        if (!resp.ok) {
-            const err = await resp.json();
-            throw new Error(err.detail || 'Analysis failed');
-        }
-
-        const data = await resp.json();
-        overlay.classList.add('hidden');
-        showToast(`Analysis complete: ${data.violations_found} violations found`, 'success');
-
-        // Reload case detail
-        openCase(currentCaseId);
-
-    } catch (e) {
-        overlay.classList.add('hidden');
-        showToast(`Analysis failed: ${e.message}`, 'error');
-    }
-}
 
 // ── Report ────────────────────────────────────────────────────────────
 

test_spaces_startup.py

@@ -0,0 +1,19 @@
+import gradio as gr
+import spaces
+from fastapi import FastAPI
+import asyncio
+
+app = FastAPI()
+
+@spaces.GPU()
+def test_fn():
+    return "Test"
+
+with gr.Blocks() as demo:
+    gr.Button("Test").click(test_fn)
+
+app_mounted = gr.mount_gradio_app(app, demo, path="/")
+
+if hasattr(spaces, "zero") and hasattr(spaces.zero, "startup"):
+    print("Found 'startup' in spaces.zero!")
+