standardized_format_state

chains/learning_objectives_generator/learning_objectives_chain.py

@@ -0,0 +1,90 @@
+# app/chains/learning_objectives/learning_objectives_chain.py
+import asyncio
+from pydantic import BaseModel
+from typing import Any
+from langchain_core.prompts.chat import ChatPromptTemplate
+
+class LearningObjectivesChain(BaseModel):
+    """
+    Orchestrates multi-step generation of learning objectives:
+      1) Two parallel calls to 'learning_objective_generator' (LLM1, LLM2).
+      2) Combine those outputs.
+      3) 'learning_objective_eliminator' on the combined output (Main LLM).
+      4) 'learning_objective_finetuner' on the output of that (Main LLM).
+      5) 'learning_objective_presenter' to finalize (Main LLM).
+
+    Each step can have its own ChatPromptTemplate and uses the relevant LLM.
+
+    If you want a separate alt LLM for step #3 or #4, just expand as needed.
+    """
+
+    # Templates
+    template_generator: ChatPromptTemplate
+    template_eliminator: ChatPromptTemplate
+    template_finetuner: ChatPromptTemplate
+    template_presenter: ChatPromptTemplate
+
+    # LLM references
+    llm_main: Any   # The "Main LLM"
+    llm_alt: Any    # The "Other LLM" used only for the second generator prompt
+
+    async def run(self, standardized_studytext: str) -> str:
+        """
+        Main pipeline for a single run. The 'standardized_studytext' is assumed
+        to be already standardized outside of this chain, so we jump straight to generation.
+        """
+
+        # 1) Two parallel calls to learning_objective_generator
+        #    with different LLMs (llm_main vs. llm_alt).
+
+        async def run_generator(llm, llm_label) -> str:
+            # Format the generator prompt
+            prompt = await self.template_generator.aformat_prompt(
+                standardized_studytext=standardized_studytext,  # possibly other variables
+                llm_label=llm_label
+            )
+            messages = prompt.to_messages()
+            response = await llm.ainvoke(messages)
+            return getattr(response, "content", response)
+
+        # Launch in parallel
+        gen_main_task = asyncio.create_task(run_generator(self.llm_main, "MAIN"))
+        gen_alt_task  = asyncio.create_task(run_generator(self.llm_alt, "ALT"))
+
+        # Wait for both to finish
+        gen_main_result, gen_alt_result = await asyncio.gather(gen_main_task, gen_alt_task)
+
+        # Combine them
+        combined_generators = f"[GEN MAIN]\n{gen_main_result}\n\n[GEN ALT]\n{gen_alt_result}"
+
+        # 2) learning_objective_eliminator (llm_main)
+        prompt_eliminate = await self.template_eliminator.aformat_prompt(
+            combined_generators=combined_generators,
+            standardized_studytext=standardized_studytext
+        )
+        elim_messages = prompt_eliminate.to_messages()
+        elim_response = await self.llm_main.ainvoke(elim_messages)
+        elim_output = getattr(elim_response, "content", elim_response)
+
+        # 3) learning_objective_finetuner (llm_main)
+        prompt_fine = await self.template_finetuner.aformat_prompt(
+            elimination_output=elim_output,
+            standardized_studytext=standardized_studytext
+        )
+        fine_messages = prompt_fine.to_messages()
+        fine_response = await self.llm_main.ainvoke(fine_messages)
+        fine_output = getattr(fine_response, "content", fine_response)
+
+        # 4) learning_objective_presenter (llm_main)
+        prompt_present = await self.template_presenter.aformat_prompt(
+            finetuned_output=fine_output,
+            standardized_studytext=standardized_studytext
+        )
+        present_messages = prompt_present.to_messages()
+        present_response = await self.llm_main.ainvoke(present_messages)
+        final_output = getattr(present_response, "content", present_response)
+
+        return final_output
+
+    class Config:
+        arbitrary_types_allowed = True

config/exercise_standardizer.py

@@ -1,12 +1,14 @@
 # exercise_standardizer.py
+import gradio as gr
 from langchain_core.prompts import ChatPromptTemplate
 from typing import Any
 from config.format_mappings import FORMAT_MAPPINGS
+from utils.state_manager import standardized_format_state
+
 
 async def standardize_exercise(user_query: str, exercise_format: str, template: ChatPromptTemplate, llm: Any) -> str:
     """
-    Standardizes an exercise's format using the specified template and LLM.
-    Uses token streaming for efficiency.
+    Standardizes an exercise's format using the specified template and LLM, and updates the UI via standardized_format_state.
     """
     if exercise_format == "Raw (original)":
         return user_query  # No transformation needed
@@ -25,4 +27,7 @@ async def standardize_exercise(user_query: str, exercise_format: str, template:
     response = await llm.ainvoke(std_messages)
     standardized_exercise = getattr(response, "content", response)
 
+    standardized_format_state.value = standardized_exercise
+
     return standardized_exercise
+

main.py

@@ -1,7 +1,7 @@
 # main.py
 import gradio as gr
 import logging
-
+from utils.state_manager import standardized_format_state
 from app.ui.diagnoser_tab import build_diagnoser_tab
 from app.ui.distractors_tab import build_distractors_tab
 from chains.diagnoser.runner import run_diagnoser
@@ -68,6 +68,9 @@ with gr.Blocks() as interface:
 
     # --- Main App (initially hidden) ---
     with gr.Column(visible=False, elem_id="main_app") as app_container:
+        # --- Standardized Exercise/Studytext Display (Initially Invisible Because it's empty) ---
+        standardized_format_display = gr.Markdown("", visible=True)
+
         gr.Markdown("## Pick the tab for your task of choice")
 
         with gr.Tabs():
@@ -123,6 +126,12 @@ with gr.Blocks() as interface:
         outputs=[login_container, app_container, login_error]
     )
 
+    standardized_format_state.change(
+        fn=lambda text: gr.update(value="#### Here's the most recent standardized format" + f"\n{standardized_format_state}"),  # ✅ Only update value, no need to toggle visibility
+        inputs=[standardized_format_state],
+        outputs=[standardized_format_display]
+    )
+
     diagnoser_button.click(
         fn=run_diagnoser,
         inputs=[diagnoser_input, model_choice_diagnose, exercise_format_diagnose, sampling_count_diagnose],

test exercises.md → test samples.md

@@ -1,4 +1,5 @@
-## Fine
+# Exercises 
+## False positives
 ### 1
 Theorie:
 In iedere gemeente zijn maatschappelijk werkers werkzaam.
@@ -7,7 +8,7 @@ Vraag:
 Wat is de hoofdtaak van maatschappelijk werkers?
 
 1. Mensen verwijzen naar professionele hulpverlening
-2. Assisteren van de huisarts
+2. **Assisteren van de huisarts**
 3. De levenskwaliteit van mensen verbeteren
 4. Contact onderhouden met hulpverlenende instanties
 
@@ -16,6 +17,20 @@ Correct antwoord:
 
 
 ### 2
+
+## False negatives
+### 1
+Vraag:
+Wat betekent observeren in de context van zorgverlening?
+
+1. Het plannen van zorgtaken
+2. Het uitvoeren van medische handelingen
+3. Het geven van je mening over de situatie
+4. **Bewust letten op wat er om je heen gebeurt**
+
+Correct antwoord:
+4. Bewust letten op wat er om je heen gebeurt
+5. 
 ### 3
 
 ---
@@ -51,3 +66,14 @@ Correct antwoord:
 ### 1
 ### 2
 ### 3
+
+# Learning objectives
+## 1 
+### Text
+Het hart bestaat uit vier holtes: twee boezems aan de bovenkant en twee kamers aan de onderkant.
+### Good LOs
+- De student weet dat het hart uit vier holtes bestaat.
+- De student weet dat het hart uit twee boezems bestaat.
+- De student weet dat de boezems van het hart aan de bovenkant zitten.
+- De student weet dat het hart uit twee kamers bestaat.
+- De student weet dat de kamers van het hart aan de onderkant zitten.

utils/state_manager.py

@@ -0,0 +1,5 @@
+# utils.state_manager.py
+import gradio as gr
+
+# Global state variable for standardized formats
+standardized_format_state = gr.State("")