Update orchestrator.py

orchestrator.py

@@ -40,24 +40,36 @@ It is provided for Your reference after the user has uploaded a resource.
 This information is primarily for understanding the context of the user's resource.
 For the syllabus, you should provide either the raw data or a dynamic summary.\n"""
 
-if dspy.settings.lm:
-    CONVO_MANAGER = ConversationManager()
-    SYLLABUS_ROUTER = SyllabusGeneratorRouter()
-    INITIAL_RESOURCE_SUMMARIZER = InitialResourceSummarizer()
-    DYNAMIC_SUMMARIZER_MODULE = DynamicResourceSummarizerModule()
-    LEARNING_STYLE_QUESTIONER = LearningStyleQuestioner()
-    PERSONA_PROMPT_GENERATOR = PersonaPromptGenerator()
-    EXPLAINER_MODULE = ExplainerModule()
-    SYLLABUS_FEEDBACK_REQUESTER = dspy.Predict(SyllabusFeedbackRequestSignature, temperature=0.7)
-    SYLLABUS_XML_TO_MARKDOWN_FORMATTER = dspy.Predict(FormatSyllabusXMLToMarkdown, temperature=0.3)
-    TITLE_GENERATOR_PREDICTOR = dspy.Predict(TitleGenerationSignature, temperature=0.4)
-    logger.info("Orchestrator's DSPy modules initialized successfully.")
-else:
-    logger.critical("Orchestrator loaded, but DSPy LM is NOT configured. This will cause errors when the orchestrator is called.")
-    # Set all modules to None so we can check for their existence and fail gracefully.
-    CONVO_MANAGER, SYLLABUS_ROUTER, INITIAL_RESOURCE_SUMMARIZER, DYNAMIC_SUMMARIZER_MODULE, \
-    LEARNING_STYLE_QUESTIONER, PERSONA_PROMPT_GENERATOR, EXPLAINER_MODULE, TITLE_GENERATOR_PREDICTOR, \
-    SYLLABUS_FEEDBACK_REQUESTER, SYLLABUS_XML_TO_MARKDOWN_FORMATTER = (None,) * 10
+def initialize_orchestrator_modules():
+    """
+    Instantiates all DSPy modules AFTER the LM has been configured.
+    This function must be called from the main app script.
+    """
+    global CONVO_MANAGER, SYLLABUS_ROUTER, INITIAL_RESOURCE_SUMMARIZER, DYNAMIC_SUMMARIZER_MODULE, \
+           LEARNING_STYLE_QUESTIONER, PERSONA_PROMPT_GENERATOR, EXPLAINER_MODULE, SYLLABUS_FEEDBACK_REQUESTER, \
+           SYLLABUS_XML_TO_MARKDOWN_FORMATTER, TITLE_GENERATOR_PREDICTOR
+
+    if not dspy.settings.lm:
+        logger.error("Cannot initialize orchestrator modules: DSPy LM is not configured.")
+        return False
+
+    try:
+        CONVO_MANAGER = ConversationManager()
+        SYLLABUS_ROUTER = SyllabusGeneratorRouter()
+        INITIAL_RESOURCE_SUMMARIZER = InitialResourceSummarizer()
+        DYNAMIC_SUMMARIZER_MODULE = DynamicResourceSummarizerModule()
+        LEARNING_STYLE_QUESTIONER = LearningStyleQuestioner()
+        PERSONA_PROMPT_GENERATOR = PersonaPromptGenerator()
+        EXPLAINER_MODULE = ExplainerModule()
+        SYLLABUS_FEEDBACK_REQUESTER = dspy.Predict(SyllabusFeedbackRequestSignature, temperature=0.7)
+        SYLLABUS_XML_TO_MARKDOWN_FORMATTER = dspy.Predict(FormatSyllabusXMLToMarkdown, temperature=0.3)
+        TITLE_GENERATOR_PREDICTOR = dspy.Predict(TitleGenerationSignature, temperature=0.4)
+        logger.info("Orchestrator's DSPy modules initialized successfully.")
+        return True
+    except Exception as e:
+        logger.critical(f"A critical error occurred during orchestrator module initialization: {e}", exc_info=True)
+        return False
+
 
 # --- Helper functions and the main process_chat_message function follow below ---
 # (The rest of your file remains the same)
@@ -138,6 +150,11 @@ def process_chat_message(
     Processes user message using DSPy modules.
     Handles initial resource processing if `uploaded_resource_data` is provided.
     """
+
+    # def yield_feedback(state_to_update: Dict, feedback_key: str):
+    #     feedback_state = state_to_update.copy()
+    #     feedback_state[STATE_UI_FEEDBACK_MESSAGE] = UI_FEEDBACK_MAP.get(feedback_key, "Processing...")
+    #     return feedback_state
     if not CONVO_MANAGER: 
         logger.error("Orchestrator's DSPy modules are not initialized. Cannot process message.")
         # Return an error state immediately
@@ -145,7 +162,8 @@ def process_chat_message(
         error_state[STATE_STAGE] = STAGE_ERROR
         error_state[STATE_HISTORY].append({'role': 'user', 'parts': [{'text': user_message_text}]})
         error_state[STATE_HISTORY].append({'role': 'model', 'parts': [{'text': "[FATAL ERROR: AI modules not initialized. Please contact support.]"}]})
-        return "[FATAL ERROR: AI modules not initialized]", error_state
+        yield ("final_result", error_state)
+        return 
     
     
     new_state = current_session_state.copy()
@@ -169,7 +187,10 @@ def process_chat_message(
         # --- Initial Resource Processing (only if resources are provided AND it's the start of negotiation) ---
         #Resources can Be only Uploaded at the start.
         if stage == STAGE_START and uploaded_resource_data:
+            yield ("status", "ANALYZING_RESOURCES_INITIAL")
+
             logger.info("First turn with resources. Processing them now...")
+
             
             total_chars = sum(len(text) for text in uploaded_resource_data.values())
             
@@ -218,6 +239,7 @@ def process_chat_message(
             if stage == STAGE_START:
                 new_state[STATE_STAGE] = STAGE_NEGOTIATING
                 stage = STAGE_NEGOTIATING # Update local stage variable
+            
 
             logger.info(f"Orchestrator (DSPy): Stage={stage}. Calling ConversationManager.")
             
@@ -228,6 +250,8 @@ def process_chat_message(
 
             # Get resource overview from state if set, otherwise "None"
             resource_overview_for_manager = new_state.get(STATE_RESOURCE_SUMMARY_OVERVIEW, "No resources were processed or provided by the user for this session.")
+            # yield yield_feedback(new_state, "PROCESSING_INPUT")
+            yield ("status", "PROCESSING_INPUT")
 
             action_code, display_text = CONVO_MANAGER.forward(
                 conversation_history_str=history_str,
@@ -245,6 +269,9 @@ def process_chat_message(
 
             # --- Handle Actions from ConversationManager ---
             if action_code in ["GENERATE", "MODIFY"]:
+                yield ("status", "GENERATING_SYLLABUS")
+
+
                 task_type_str = "generation" if action_code == "GENERATE" else "modification"
                 logger.info(f"Syllabus {task_type_str} requested. Resource type: {new_state.get(STATE_RESOURCE_TYPE_FOR_SYLLABUS)}")
                 retrieved_resource_type = new_state.get(STATE_RESOURCE_TYPE_FOR_SYLLABUS, "NONE")
@@ -257,6 +284,8 @@ def process_chat_message(
                 logger.info(f"Syllabus {task_type_str} requested. Resource type from state: {retrieved_resource_type}")
                 # If type is SUMMARIES, we need to generate them now using DynamicSummarizer
                 if retrieved_resource_type  == "SUMMARIES":
+                    # yield yield_feedback(new_state, "GENERATING_DYNAMIC_SUMMARIES")
+
                     raw_data_for_dynamic_summary = new_state.get('raw_resource_data_for_dynamic_summary')
                     if raw_data_for_dynamic_summary and isinstance(raw_data_for_dynamic_summary, dict):
                         logger.info("Generating dynamic summaries for syllabus router...")
@@ -296,7 +325,11 @@ def process_chat_message(
                 # --- BLOCK 1: XML to Markdown Formatting (and set success flag) ---
                 if generated_xml and not generated_xml.strip().upper().startswith(("<SYLLABUS>\n[ERROR", "<SYLLABUS>[ERROR")):
                     syllabus_generation_was_successful = True # Mark initial generation as successful
+                    yield ("status", "FORMATTING_SYLLABUS")
+
                     logger.info(f"Syllabus XML generated. Length: {len(generated_xml)}. Attempting Markdown formatting.")
+                    # yield yield_feedback(new_state, "FORMATTING_SYLLABUS")
+
                     
                     if SYLLABUS_XML_TO_MARKDOWN_FORMATTER:
                         try:
@@ -370,6 +403,8 @@ def process_chat_message(
                 logger.info("Finalization requested by manager.")
                 last_syllabus_in_history = get_last_syllabus_content_from_history(history)
                 if last_syllabus_in_history:
+                    # yield yield_feedback(new_state, "FINALIZING_SYLLABUS")
+
                     new_state[STATE_FINAL_SYLLABUS] = f"<syllabus>\n{last_syllabus_in_history}\n</syllabus>" # Store it
                     
                     # Ask for learning style
@@ -384,9 +419,12 @@ def process_chat_message(
                     history.append({'role': 'model', 'parts': [{'text': ai_reply_for_user}]})
 
             elif action_code == "PERSONA":
+                yield ("status", "GENERATING_PERSONA")
                 logger.info("Persona generation triggered by manager.")
                 final_syllabus_xml_str = new_state.get(STATE_FINAL_SYLLABUS)
                 if final_syllabus_xml_str:
+                    # yield yield_feedback(new_state, "GENERATING_PERSONA")
+
                     logger.info("Generating explainer prompt body...")
                     explainer_prompt_body = PERSONA_PROMPT_GENERATOR.forward(
                         conversation_history_str=format_history_for_dspy(history)
@@ -408,6 +446,8 @@ def process_chat_message(
                             history_str="None", # No prior *explainer* history for this first turn
                             user_query_str=explainer_intro_query
                         )
+                        yield ("status", "TUTOR_INTRODUCTION")
+
                         ai_reply_for_user = explainer_intro_response
                         history.append({'role': 'model', 'parts': [{'text': ai_reply_for_user}]})
                     else:
@@ -434,7 +474,11 @@ def process_chat_message(
 
         # --- Explanation Phase (STAGE_EXPLAINING) ---
         elif stage == STAGE_EXPLAINING:
+            yield ("status", "EXPLAINER_RESPONSE")
+
             logger.info(f"Orchestrator (DSPy): Stage={stage}. Calling ExplainerModule.")
+            # yield yield_feedback(new_state, "EXPLAINER_RESPONSE")
+
             explainer_sys_prompt = modified_explainer_prompt or new_state.get(STATE_EXPLAINER_PROMPT)
             expl_start_idx = new_state.get(STATE_EXPLANATION_START_INDEX, 0)
 
@@ -498,10 +542,15 @@ def process_chat_message(
             history.append({'role': 'model', 'parts': [{'text': ai_reply_for_user}]})
     
     # --- Final State Update & Return ---
-    new_state[STATE_HISTORY] = history
+    # new_state[STATE_HISTORY] = history
     logger.debug(f"Orchestrator (DSPy) returning: Stage='{new_state.get(STATE_STAGE)}', History Len={len(history)}, AI Reply starts: '{ai_reply_for_user[:50]}...'")
     logger.debug(f"Flags: DisplaySyllabus='{new_state.get(STATE_DISPLAY_SYLLABUS_FLAG) is not None}', TransitionExplainer='{new_state.get(STATE_TRANSITION_EXPLAINER_FLAG)}'")
+    new_state[STATE_HISTORY] = history
+    # new_state.pop(STATE_UI_FEEDBACK_MESSAGE, None) # Clear feedback message for the final state
     
-    return ai_reply_for_user, new_state
+    new_state[STATE_HISTORY] = history
+    yield ("final_result", new_state)
+
+