feat: add diagnostic logs for generation flows

app/appconfig.py

@@ -425,6 +425,33 @@ def get_model(name, path=AI_MODELS_PATH) -> dict:
     return copy.deepcopy(models[_model_index(models, name)])
 
 
+
+def model_request_url(model) -> str:
+    """Return the HTTP endpoint used for a configured AI model."""
+
+    raw_url = str(model.get("url", "") or "").strip()
+    api_type = str(model.get("api_type", "auto") or "auto").strip()
+    if api_type == "images_edits":
+        return _append_default_endpoint(raw_url, "images/edits")
+    return _append_default_endpoint(raw_url, "chat/completions")
+
+
+def _append_default_endpoint(raw_url, endpoint):
+    if not raw_url:
+        return raw_url
+    parts = urllib.parse.urlsplit(raw_url)
+    path = parts.path.rstrip("/")
+    lowered = path.lower()
+    endpoint_path = "/" + endpoint.strip("/")
+    if lowered.endswith(endpoint_path):
+        return raw_url
+    base_markers = ("", "/v1", "/v1beta", "/api/v1", "/api/v1beta")
+    if lowered in base_markers or lowered.endswith(base_markers[1:]):
+        path = path + endpoint_path
+        return urllib.parse.urlunsplit(
+            (parts.scheme, parts.netloc, path, parts.query, parts.fragment)
+        )
+    return raw_url
 def _test_request_payload(model):
     if model["api_type"] == "images_edits":
         payload = {"model": model["model"], "prompt": "ping"}
@@ -455,7 +482,7 @@ def test_ai_model(name, path=AI_MODELS_PATH) -> dict:
             "utf-8"
         )
         request = urllib.request.Request(
-            model["url"],
+            model_request_url(model),
             data=data,
             headers={
                 "Authorization": "Bearer " + model["api_key"],