Refine flashcard generation process and update user interaction in Notebook-Tutor

- Simplify and clarify the steps for generating and exporting flashcards in prompt_templates.py, emphasizing the use of flashcard_tool for export.
- Update the welcome message and file upload prompt in chainlit_frontend.py for a more streamlined user experience.
- Adjust the logic in agents.py to allow for more flexible agent state transitions, particularly for the QAAgent and FlashcardsAgent.
- Ensure a directory is created for saving flashcards in tools.py, enhancing the flashcard creation tool's reliability.

notebook_tutor/agents.py

@@ -54,9 +54,11 @@ def agent_node(state, agent, name):
     # Set the appropriate flags and next state
     if name == "QuizAgent":
         new_state["quiz_created"] = True
-    elif name == "QAAgent":
+
+    if name == "QAAgent":
         new_state["question_answered"] = True
-    elif name == "FlashcardsAgent":
+
+    if name == "FlashcardsAgent":
         new_state["flashcards_created"] = True
 
     return new_state

notebook_tutor/chainlit_frontend.py

@@ -26,13 +26,13 @@ async def start_chat():
         "presence_penalty": 0,
     }
     cl.user_session.set("settings", settings)
-    welcome_message = "Welcome to the Notebook-Tutor! Please upload a Jupyter notebook (.ipynb and max. 5mb) to start."
+    welcome_message = "Welcome to the Notebook-Tutor!"
     await cl.Message(content=welcome_message).send()
 
     files = None
     while files is None:
         files = await cl.AskFileMessage(
-            content="Please upload a Jupyter notebook (.ipynb, max. 5mb):",
+            content="Please upload a Jupyter notebook (.ipynb, max. 5mb) to start:",
             accept={"application/x-ipynb+json": [".ipynb"]},
             max_size_mb=5
         ).send()
@@ -60,6 +60,7 @@ async def start_chat():
 
 @cl.on_message
 async def main(message: cl.Message):
+
     # Retrieve the LangGraph chain from the session
     tutor_chain = cl.user_session.get("tutor_chain")
 

notebook_tutor/prompt_templates.py

@@ -40,15 +40,13 @@ class PromptTemplates:
         1. Analyze User Query: Understand the user's request and determine the key concepts and information they need to learn.
         2. Search Notebook Content: Use the notebook content to gather relevant information and generate accurate and informative flashcards.
         3. Generate Flashcards: Create a series of flashcards content with clear questions on the front and detailed answers on the back. Ensure that the flashcards cover the essential points and concepts requested by the user.
-        4. Export Flashcards: Use the flashcard_tool to create and export the flashcards in a format that can be easily imported into a flashcard management system, such as Anki.
-        5. DO NOT SHARE the link to the flashcard file directly with the user. Instead, provide the list of flashcards in a clear and organized manner.
-
+        4. Export Flashcards: YOU MUST USE the flashcard_tool to create and export the flashcards in a format that can be easily imported into a flashcard management system, such as Anki.
+        5. Provide the list of flashcards in a clear and organized manner.
         Remember, your goal is to help the user learn efficiently and effectively by breaking down the notebook content into manageable, repeatable flashcards."""
 
         self.SupervisorAgent_prompt = "You are a supervisor tasked with managing a conversation between the following agents: QAAgent, QuizAgent, FlashcardsAgent. Given the user request, decide which agent should act next."
 
     def get_rag_qa_prompt(self):
-        # Returns the RAG QA prompt
         return self.rag_QA_prompt
 
     def get_qa_agent_prompt(self):

notebook_tutor/tools.py

@@ -22,6 +22,7 @@ class FlashcardTool(BaseTool):
     ) -> str:
         """Use the tool to create flashcards."""
         filename = f"flashcards_{uuid.uuid4()}.csv"
+
         save_path = os.path.join('flashcards', filename)
 
         os.makedirs(os.path.dirname(save_path), exist_ok=True)