Update app.py

app.py

@@ -1291,11 +1291,14 @@ def transcribe_video_bytes(video_bytes: bytes, whisper_model_name: str = "small"
 
 # generate MCQs from summary (reuse existing function if present)
 def generate_mcqs_from_summary_local(summary: str, num_qs: int = 10, model: str = MODEL):
-    # Reuse the same approach as your Streamlit function generate_mcqs_from_summary
+    """
+    Generate MCQs from a text summary using the OpenRouter model
+    via call_ollama().
+    """
     prompt = f"""
 Generate {num_qs} distinct multiple-choice questions that cover the following summary.
 For each question include:
-- Exactly 4 labeled options A) B) C) D) 
+- Exactly 4 labeled options A) B) C) D)
 - A single-letter answer line like: Answer: <A/B/C/D>
 
 Use exactly this format; do not add extra commentary or code fences.
@@ -1310,9 +1313,13 @@ Answer: <A/B/C/D>
 Summary:
 {summary}
 """
-    out = call_ollama(prompt, model=model, timeout=600)
-    if out.startswith("OLLAMA_ERROR"):
+    # 👇 OpenRouter call – no model/timeout args here
+    out = call_ollama(prompt)
+
+    # Match the error format used in call_ollama()
+    if out.startswith("LOCAL_MODEL_ERROR"):
         return [{"question": out, "options": [], "answer": ""}]
+
     return parse_mcqs_freeform(out)
 
 # Endpoint: transcribe -> summarize (video)