Enhance quiz generation functionality by improving JSON response parsing in app.py and quiz_tools.py. Implement robust error handling for various response formats, including string and dictionary types. Introduce utility functions to clean and extract JSON from text, ensuring cleaner data handling and improved reliability in quiz data retrieval.

app.py

@@ -420,7 +420,23 @@ with gr.Blocks(title="TutorX Educational AI", theme=gr.themes.Soft()) as demo:
                         async with ClientSession(sse, write) as session:
                             await session.initialize()
                             response = await session.call_tool("generate_quiz_tool", {"concept": concept.strip(), "difficulty": difficulty_str})
-                            return response
+                            # --- PATCH: Parse quiz JSON for pretty display ---
+                            if hasattr(response, 'content') and isinstance(response.content, list):
+                                for item in response.content:
+                                    if hasattr(item, 'text') and item.text:
+                                        try:
+                                            quiz_data = json.loads(item.text)
+                                            return quiz_data
+                                        except Exception:
+                                            return {"raw": item.text}
+                            if isinstance(response, dict):
+                                return response
+                            if isinstance(response, str):
+                                try:
+                                    return json.loads(response)
+                                except Exception:
+                                    return {"raw": response}
+                            return {"raw": str(response)}
                 except Exception as e:
                     import traceback
                     return {

mcp_server/tools/quiz_tools.py

@@ -14,6 +14,22 @@ PROMPT_TEMPLATE = (Path(__file__).parent.parent / "prompts" / "quiz_generation.t
 # Initialize Gemini model
 MODEL = GeminiFlash()
 
+def clean_json_trailing_commas(json_text: str) -> str:
+    import re
+    return re.sub(r',([ \t\r\n]*[}}\]])', r'\1', json_text)
+
+def extract_json_from_text(text: str):
+    import re, json
+    if not text or not isinstance(text, str):
+        return None
+    # Remove code fences
+    text = re.sub(r'^\s*```(?:json)?\s*', '', text, flags=re.IGNORECASE)
+    text = re.sub(r'\s*```\s*$', '', text, flags=re.IGNORECASE)
+    text = text.strip()
+    # Remove trailing commas
+    cleaned = clean_json_trailing_commas(text)
+    return json.loads(cleaned)
+
 @mcp.tool()
 async def generate_quiz_tool(concept: str, difficulty: str = "medium") -> dict:
     """
@@ -32,7 +48,7 @@ async def generate_quiz_tool(concept: str, difficulty: str = "medium") -> dict:
         )
         llm_response = await MODEL.generate_text(prompt, temperature=0.7)
         try:
-            quiz_data = json.loads(llm_response)
+            quiz_data = extract_json_from_text(llm_response)
         except Exception:
             quiz_data = {"llm_raw": llm_response, "error": "Failed to parse LLM output as JSON"}
         return quiz_data