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nbs/01_clinical_tutor.ipynb

@@ -230,11 +230,11 @@
     "        Returns:\n",
     "            list: Generated SMART goals\n",
     "        \"\"\"\n",
-    "        prompt = f\"\"\"Generate {num_goals} SMART learning goals for a medical trainee in {specialty} ({setting}).\n",
+    "        prompt = f\"\"\"Generate {num_goals} specific learning goals for a medical trainee in {specialty} ({setting}).\n",
     "    \n",
     "    For each goal:\n",
     "    1. Select an appropriate category from: {', '.join(cat.value for cat in LearningCategory)}\n",
-    "    2. Write a specific, measurable goal that builds clinical competence\n",
+    "    2. Write a specific, measurable goal that builds clinical competence. Write the specific goal only for the next case discussion (i.e. it is not longitudinal across several cases). Also, this goal needs to be able to be evaluated by you - there is limited access to doctors to verify facts. \n",
     "    \n",
     "    Format as JSON array with fields:\n",
     "    - category: Learning category name\n",
@@ -244,11 +244,11 @@
     "    [\n",
     "      {{\n",
     "        \"category\": \"Clinical Reasoning\",\n",
-    "        \"smart_version\": \"Accurately diagnose acute coronary syndrome in 5 chest pain cases over 2 weeks with 90% agreement with attending assessment\"\n",
+    "        \"smart_version\": \"Identify a comprehensive list of differential diagnoses for a patient with acute shortness of breath.\"\n",
     "      }},\n",
     "      {{\n",
     "        \"category\": \"Management\",\n",
-    "        \"smart_version\": \"Successfully develop and implement management plans for 3 complex cardiac patients with multiple comorbidities by week 4\"\n",
+    "        \"smart_version\": \"Outline a detailed management plan for a patient with heart failure.\"\n",
     "      }}\n",
     "    ]\n",
     "    \n",
@@ -322,12 +322,12 @@
     "        Returns:\n",
     "            SmartGoal: Generated SMART goal\n",
     "        \"\"\"\n",
-    "        prompt = f\"\"\"Convert this learning goal into a SMART goal (Specific, Measurable, Achievable, Relevant, Time-bound) for {specialty} ({setting}):\n",
+    "        prompt = f\"\"\"Convert this learning goal into a more specific goal (Specific, Measurable, Achievable, Relevant) for a patient in {specialty} ({setting}):\n",
     "\n",
     "\"{user_input}\"\n",
     "\n",
     "1. Select the most appropriate category from: {', '.join(cat.value for cat in LearningCategory)}\n",
-    "2. Rewrite as a SMART goal specific to {setting} in {specialty}\n",
+    "2. Rewrite as a specific goal specific to {setting} in {specialty}\n",
     "\n",
     "Format as JSON with fields:\n",
     "- category: Learning category name\n",
@@ -490,9 +490,14 @@
     "        \"\"\"\n",
     "        return self.current_discussion\n",
     "    \n",
-    "    def clear_discussion(self) -> None:\n",
-    "        \"\"\"Clear current discussion history.\"\"\"\n",
-    "        self.current_discussion = []"
+    "    def clear_discussion() -> Tuple[List, str, Dict]:\n",
+    "        \"\"\"Clear chat history.\"\"\"\n",
+    "        return [], \"\", {\n",
+    "            \"discussion_active\": False,\n",
+    "            \"suggested_goals\": [],\n",
+    "            \"discussion_start\": None,\n",
+    "            \"last_message\": None\n",
+    "        }\n"
    ]
   },
   {

nbs/02_learning_interface.ipynb

@@ -206,10 +206,10 @@
     "    async def process_chat(\n",
     "        self,\n",
     "        message: str,\n",
-    "        history: List[List[str]],\n",
+    "        history: List[List[Dict[str, str]]],\n",
     "        state: Dict[str, Any]\n",
-    "    ) -> AsyncGenerator[Tuple[List[List[str]], str, Dict[str, Any]], None]:\n",
-    "        \"\"\"Process chat messages with streaming.\"\"\"\n",
+    "    ) -> AsyncGenerator[Tuple[List[List[Dict[str, str]]], str, Dict[str, Any]], None]:\n",
+    "        \"\"\"Process chat messages with streaming and proper message format.\"\"\"\n",
     "        try:\n",
     "            if not message.strip():\n",
     "                yield history, \"\", state\n",
@@ -224,8 +224,10 @@
     "            if history is None:\n",
     "                history = []\n",
     "            \n",
-    "            # Add user message\n",
-    "            history.append([message, \"\"])\n",
+    "            # Add user message in correct format\n",
+    "            user_message = {\"role\": \"user\", \"content\": message}\n",
+    "            assistant_message = {\"role\": \"assistant\", \"content\": \"\"}\n",
+    "            history.append([user_message, assistant_message])\n",
     "            \n",
     "            # Update display\n",
     "            yield history, \"\", state\n",
@@ -234,7 +236,7 @@
     "            current_response = \"\"\n",
     "            async for token in self.tutor.discuss_case(message):\n",
     "                current_response += token\n",
-    "                history[-1][1] = current_response\n",
+    "                history[-1][1][\"content\"] = current_response\n",
     "                yield history, \"\", state\n",
     "            \n",
     "            state[\"last_message\"] = datetime.now().isoformat()\n",
@@ -244,9 +246,9 @@
     "            logger.error(f\"Error in chat: {str(e)}\")\n",
     "            if history is None:\n",
     "                history = []\n",
-    "            history.append([message, \"I apologize, but I encountered an error. Please try again.\"])\n",
+    "            error_message = {\"role\": \"assistant\", \"content\": \"I apologize, but I encountered an error. Please try again.\"}\n",
+    "            history.append([{\"role\": \"user\", \"content\": message}, error_message])\n",
     "            yield history, \"\", state\n",
-    "\n",
     "    def _update_discussion_status(self, state: Dict[str, Any]) -> str:\n",
     "        \"\"\"Update discussion status display.\"\"\"\n",
     "        try:\n",
@@ -475,12 +477,6 @@
     "                    return self._update_displays(state)\n",
     "                return [[], [], [], \"No active learning goal\"]\n",
     "\n",
-    "            def process_audio(audio: np.ndarray) -> str:\n",
-    "                \"\"\"Convert audio to text.\"\"\"\n",
-    "                if audio is None:\n",
-    "                    return \"\"\n",
-    "                return \"Audio transcription would appear here\"\n",
-    "\n",
     "            def clear_discussion() -> Tuple[List, str, Dict]:\n",
     "                \"\"\"Clear chat history.\"\"\"\n",
     "                return [], \"\", {\n",
@@ -489,7 +485,14 @@
     "                    \"discussion_start\": None,\n",
     "                    \"last_message\": None\n",
     "                }\n",
-    "\n",
+    "            \n",
+    "            def process_audio(audio: np.ndarray) -> str:\n",
+    "                \"\"\"Convert audio to text.\"\"\"\n",
+    "                if audio is None:\n",
+    "                    return \"\"\n",
+    "                # Add your audio processing logic here\n",
+    "                return \"Audio transcription would appear here\"\n",
+    "                \n",
     "            # Wire up events\n",
     "            specialty.change(\n",
     "                update_context,\n",

wardbuddy/clinical_tutor.py

@@ -138,11 +138,11 @@ class ClinicalTutor:
         Returns:
             list: Generated SMART goals
         """
-        prompt = f"""Generate {num_goals} SMART learning goals for a medical trainee in {specialty} ({setting}).
+        prompt = f"""Generate {num_goals} specific learning goals for a medical trainee in {specialty} ({setting}).
     
     For each goal:
     1. Select an appropriate category from: {', '.join(cat.value for cat in LearningCategory)}
-    2. Write a specific, measurable goal that builds clinical competence
+    2. Write a specific, measurable goal that builds clinical competence. Write the specific goal only for the next case discussion (i.e. it is not longitudinal across several cases). Also, this goal needs to be able to be evaluated by you - there is limited access to doctors to verify facts. 
     
     Format as JSON array with fields:
     - category: Learning category name
@@ -152,11 +152,11 @@ class ClinicalTutor:
     [
       {{
         "category": "Clinical Reasoning",
-        "smart_version": "Accurately diagnose acute coronary syndrome in 5 chest pain cases over 2 weeks with 90% agreement with attending assessment"
+        "smart_version": "Identify a comprehensive list of differential diagnoses for a patient with acute shortness of breath."
       }},
       {{
         "category": "Management",
-        "smart_version": "Successfully develop and implement management plans for 3 complex cardiac patients with multiple comorbidities by week 4"
+        "smart_version": "Outline a detailed management plan for a patient with heart failure."
       }}
     ]
     
@@ -230,12 +230,12 @@ class ClinicalTutor:
         Returns:
             SmartGoal: Generated SMART goal
         """
-        prompt = f"""Convert this learning goal into a SMART goal (Specific, Measurable, Achievable, Relevant, Time-bound) for {specialty} ({setting}):
+        prompt = f"""Convert this learning goal into a more specific goal (Specific, Measurable, Achievable, Relevant) for a patient in {specialty} ({setting}):
 
 "{user_input}"
 
 1. Select the most appropriate category from: {', '.join(cat.value for cat in LearningCategory)}
-2. Rewrite as a SMART goal specific to {setting} in {specialty}
+2. Rewrite as a specific goal specific to {setting} in {specialty}
 
 Format as JSON with fields:
 - category: Learning category name
@@ -398,6 +398,12 @@ Format as JSON with fields:
         """
         return self.current_discussion
     
-    def clear_discussion(self) -> None:
-        """Clear current discussion history."""
-        self.current_discussion = []
+    def clear_discussion() -> Tuple[List, str, Dict]:
+        """Clear chat history."""
+        return [], "", {
+            "discussion_active": False,
+            "suggested_goals": [],
+            "discussion_start": None,
+            "last_message": None
+        }
+

wardbuddy/learning_interface.py

@@ -117,10 +117,10 @@ class LearningInterface:
     async def process_chat(
         self,
         message: str,
-        history: List[List[str]],
+        history: List[List[Dict[str, str]]],
         state: Dict[str, Any]
-    ) -> AsyncGenerator[Tuple[List[List[str]], str, Dict[str, Any]], None]:
-        """Process chat messages with streaming."""
+    ) -> AsyncGenerator[Tuple[List[List[Dict[str, str]]], str, Dict[str, Any]], None]:
+        """Process chat messages with streaming and proper message format."""
         try:
             if not message.strip():
                 yield history, "", state
@@ -135,8 +135,10 @@ class LearningInterface:
             if history is None:
                 history = []
             
-            # Add user message
-            history.append([message, ""])
+            # Add user message in correct format
+            user_message = {"role": "user", "content": message}
+            assistant_message = {"role": "assistant", "content": ""}
+            history.append([user_message, assistant_message])
             
             # Update display
             yield history, "", state
@@ -145,7 +147,7 @@ class LearningInterface:
             current_response = ""
             async for token in self.tutor.discuss_case(message):
                 current_response += token
-                history[-1][1] = current_response
+                history[-1][1]["content"] = current_response
                 yield history, "", state
             
             state["last_message"] = datetime.now().isoformat()
@@ -155,9 +157,9 @@ class LearningInterface:
             logger.error(f"Error in chat: {str(e)}")
             if history is None:
                 history = []
-            history.append([message, "I apologize, but I encountered an error. Please try again."])
+            error_message = {"role": "assistant", "content": "I apologize, but I encountered an error. Please try again."}
+            history.append([{"role": "user", "content": message}, error_message])
             yield history, "", state
-
     def _update_discussion_status(self, state: Dict[str, Any]) -> str:
         """Update discussion status display."""
         try:
@@ -386,12 +388,6 @@ class LearningInterface:
                     return self._update_displays(state)
                 return [[], [], [], "No active learning goal"]
 
-            def process_audio(audio: np.ndarray) -> str:
-                """Convert audio to text."""
-                if audio is None:
-                    return ""
-                return "Audio transcription would appear here"
-
             def clear_discussion() -> Tuple[List, str, Dict]:
                 """Clear chat history."""
                 return [], "", {
@@ -400,7 +396,14 @@ class LearningInterface:
                     "discussion_start": None,
                     "last_message": None
                 }
-
+            
+            def process_audio(audio: np.ndarray) -> str:
+                """Convert audio to text."""
+                if audio is None:
+                    return ""
+                # Add your audio processing logic here
+                return "Audio transcription would appear here"
+                
             # Wire up events
             specialty.change(
                 update_context,