Update app.py

app.py

@@ -381,36 +381,39 @@ agent_with_memory = initialize_agent(
     verbose=True
 )
 
+from pydantic import BaseModel
+
+class AskRequest(BaseModel):
+    question: str
+
 @app.post("/ask")
-async def ask(request: Request):
-    payload = await request.json()
-    question = payload.get("question", "").strip()
+async def ask(payload: AskRequest):
+    question = payload.question.strip()
 
     if not question:
         return JSONResponse(status_code=400, content={"error": "Question cannot be empty."})
 
     try:
-        # Run the LLM agent
+        # Run the agent
         agent_result = agent_with_memory.invoke({"input": question})
         raw_answer = agent_result.get("output", "") if isinstance(agent_result, dict) else str(agent_result)
 
-        # Clean: remove any links from the answer
+        # Clean answer (remove markdown links)
         import re
         cleaned_answer = re.sub(r"\[(.*?)\]\((https?://.*?)\)", r"\1", raw_answer)
 
-        # Tools invoked separately for tab results
+        # Call tools
         video_results = fetch_youtube_videos.invoke({"query": question})
         doc_sources = retrieve_documents.invoke({"query": question})
         people_images = detect_people_and_images.invoke({"answer": cleaned_answer})
 
-        # Insights tab
         insights = []
         seen = set()
         for p in people_images:
             name = p["name"].lower().strip()
             if name not in seen:
-                seen.add(name)
                 insights.append(f"{name} was detected in the answer and linked to visual/media content.")
+                seen.add(name)
 
         if doc_sources:
             insights.append(f"{len(doc_sources)} source documents contributed to this answer.")
@@ -429,4 +432,4 @@ async def ask(request: Request):
 
     except Exception as e:
         logging.error(f"Tool-based agent error: {e}")
-        return JSONResponse(status_code=500, content={"error": "Failed to process question."})
+        return JSONResponse(status_code=500, content={"error": "Failed to process question."})