Finalize backend integration with chat endpoint and Redis memory

api/chat.py

@@ -22,13 +22,18 @@ async def chat(user_id: str, message: str):
 
     # Generate AI response
     try:
-        response_stream = llm_client.generate(
-            prompt=message,
-            stream=True
-        )
+        full_response = ""
+        response_stream = llm_client.generate(prompt=message, stream=True)
 
-        # Stream response back
-        return StreamingResponse(response_stream, media_type="text/event-stream")
+        # Collect streamed response
+        for chunk in response_stream:
+            full_response += chunk
+
+        # Save updated conversation
+        conversation_history.append({"role": "assistant", "content": full_response})
+        save_user_state(user_id, {"conversation": json.dumps(conversation_history)})
+
+        return {"response": full_response}
 
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"LLM generation failed: {e}")

app.py

@@ -51,7 +51,7 @@ else:
                         json={"user_id": user, "message": user_input}
                     )
                     if response.status_code == 200:
-                        ai_response = response.text  # Adjust based on actual response format
+                        ai_response = response.text
                         st.markdown(f"**AI Coach:** {ai_response}")
                     else:
                         st.error("Failed to get response from AI Coach.")