flake8

app/pipeline/chat/code_feedback_pipeline.py

@@ -2,7 +2,6 @@
 import os
 from typing import Dict, Optional, List
 
-from langchain.output_parsers import PydanticOutputParser
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import PromptTemplate
 from langchain_core.runnables import Runnable
@@ -16,9 +15,6 @@
 from ...llm import CompletionArguments
 from ...llm.langchain import IrisLangchainChatModel
 from ...pipeline import Pipeline
-from ...pipeline.chat.output_models.output_models.selected_file_model import (
-    SelectedFiles,
-)
 from ...web.status.status_update import StatusCallback
 
 logger = logging.getLogger(__name__)

app/pipeline/chat/course_chat_pipeline.py

@@ -26,10 +26,6 @@
 from app.domain.chat.interaction_suggestion_dto import (
     InteractionSuggestionPipelineExecutionDTO,
 )
-from ...domain.chat.lecture_chat.lecture_chat_pipeline_execution_dto import (
-    LectureChatPipelineExecutionDTO,
-)
-from ...domain.data.course_dto import CourseDTO
 from ...llm import CapabilityRequestHandler, RequirementList
 from ..prompts.iris_course_chat_prompts import (
     tell_iris_initial_system_prompt,

app/pipeline/chat/exercise_chat_pipeline.py

@@ -1,4 +1,3 @@
-import json
 import logging
 import traceback
 from concurrent.futures import ThreadPoolExecutor
@@ -202,7 +201,8 @@ def _run_exercise_chat_pipeline(
                         "(the student should think it's your own idea)! "
                         "\n" + feedback + "\n"
                         "Remember: This is not coming from the student. This is not a message from the student. "
-                        "Is is an automated analysis of the student's code. NEVER claim that the student said this, e.g with 'You mentioned...'"
+                        "Is is an automated analysis of the student's code. "
+                        "NEVER claim that the student said this, e.g with 'You mentioned...'"
                     )
                 except Exception as e:
                     self.callback.error(

app/pipeline/prompts/iris_interaction_suggestion_prompts.py

@@ -120,7 +120,8 @@
 course_chat_history_exists_prompt = """
 The following messages represent the chat history of your conversation with the student so far.
 Use it to generate questions that are consistent with the conversation and informed by the student's progress.
-Important: The generated questions should be based on the chat history. They should be suggestions for natural follow-up questions or statements like a student would ask a human tutor.
+Important: The generated questions should be based on the chat history. They should be suggestions for natural follow-up
+ questions or statements like a student would ask a human tutor.
 The questions should be engaging, insightful so that the student continues to engage in the conversation.
 Avoid repeating or reusing previous questions or messages; always in all circumstances craft new and original questions.
 Never re-use any questions that are already asked. Instead, always write new and original questions.
@@ -130,7 +131,8 @@
 The following messages represent the chat history of your conversation with the student so far.
 Use it to generate questions that are consistent with the conversation and informed by the student's progress
 in the exercise.
-Important: The generated questions should be related to the chat history. They should be suggestions for natural follow-up questions or statements like a student would ask a human tutor.
+Important: The generated questions should be related to the chat history. They should be suggestions for natural
+follow-up questions or statements like a student would ask a human tutor.
 The questions should be engaging, insightful so that the student continues to engage in the conversation.
 Avoid repeating or reusing previous questions or messages; always in all circumstances craft new and original questions.
 Never re-use any questions that are already asked. Instead, always write new and original questions.

app/pipeline/prompts/lecture_retrieval_prompts.py

@@ -2,14 +2,16 @@
 You decide if a student question to an AI tutor is a contentful question or not.
 A contentful question is a question that is not a greeting, a thank you, or a statement.
 It is only contentful if it can be potentially answered by looking into the lecture materials.
-If the question is contentful, return 'YES'. If the question is not contentful and a lecture lookup is probably useless, return 'NO'.
+If the question is contentful, return 'YES'. If the question is not contentful and a lecture lookup is probably useless,
+return 'NO'.
 """
 
 assessment_prompt_final = """
 Now, decide if the student question is a contentful question or not.
 A contentful question is a question that is not a greeting, a thank you, or a statement.
 It is only contentful if it can be potentially answered by looking into the lecture materials.
-If the question is contentful, return 'YES'. If the question is not contentful and a lecture lookup is probably useless, return 'NO'.
+If the question is contentful, return 'YES'. If the question is not contentful and a lecture lookup is probably useless,
+return 'NO'.
 Do not answer the question. Only return 'YES' or 'NO'.
 """
 
@@ -28,7 +30,7 @@
 You write good and performant vector database queries, in particular for Weaviate,
 from chat histories between an AI tutor and a student.
 The student has sent a query in the context of the lecture {course_name} and the exercise {exercise_name}.
-For more exercise context here is the problem statement: 
+For more exercise context here is the problem statement:
 ---
 {problem_statement}
 ---

app/pipeline/shared/citation_pipeline.py

@@ -1,6 +1,6 @@
 import os
 from asyncio.log import logger
-from typing import Optional, List, Union
+from typing import List, Union
 
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.prompts import ChatPromptTemplate, PromptTemplate