from __future__ import annotations import ast import csv import json from pathlib import Path import random from typing import TYPE_CHECKING from griptape.structures import Agent import pandas as pd from uw_programmatic.base_machine import UWBaseMachine if TYPE_CHECKING: from griptape.tools import BaseTool class UWMachine(UWBaseMachine): """State machine with GOAP""" @property def tools(self) -> dict[str, BaseTool]: return {} def start_machine(self) -> None: """Starts the machine.""" # Clear input history. # Clear csv file self.send("enter_first_state") def on_event_gather_parameters(self, event_: dict) -> None: event_source = event_["type"] event_value = event_["value"] match event_source: case "user_input": parameters = event_value self.page_range = parameters["page_range"] self.question_number = parameters["question_number"] self.taxonomy = parameters["taxonomy"] self.send("next_state") case _: err_msg = f"Unexpected Transition Event ID: {event_value}." raise ValueError(err_msg) def on_enter_evaluate_q_count(self) -> None: if len(self.question_list) >= self.question_number: self.send("finish_state") # go to output questions else: self.send("next_state") # go to need more questions def on_event_evaluate_q_count(self, event_: dict) -> None: pass def on_enter_need_more_q(self) -> None: # Create the entire workflow to create another question. self.get_questions_workflow().run() def on_event_need_more_q(self, event_: dict) -> None: event_source = event_["type"] event_value = event_["value"] match event_source: case "griptape_event": event_type = event_value["type"] match event_type: case "FinishStructureRunEvent": structure_id = event_value["structure_id"] match structure_id: case "create_question_workflow": values = event_value["output_task_output"]["value"] questions = [ ast.literal_eval(question["value"]) for question in values ] self.most_recent_questions = ( questions # This is a ListArtifact I'm pretty sure ) self.send("next_state") case _: print(f"Error:{event_} ") case _: print(f"Unexpected: {event_}") def on_enter_assess_generated_q(self) -> None: # TODO: Should it append it to the list already and remove duplicates? or not? # TODO: Merge incoming lists merged_list = [*self.question_list, *self.most_recent_questions] prompt = f"{merged_list}" self.get_structure("similarity_auditor").run(prompt) def on_event_assess_generated_q(self, event_: dict) -> None: event_source = event_["type"] event_value = event_["value"] match event_source: case "griptape_event": event_type = event_value["type"] match event_type: case "FinishStructureRunEvent": structure_id = event_value["structure_id"] match structure_id: case "similarity_auditor": new_question_list = event_value["output_task_output"][ "value" ] try: new_question_list = json.loads( new_question_list ) # This must be in that JSON format except: new_question_list = self.question_list merged_list = [ *self.question_list, *self.most_recent_questions, ] deleted_q = [ question1 for question1 in merged_list if not any( question2["Question"] == question1["Question"] for question2 in new_question_list ) ] self.question_list = new_question_list self.send("next_state") # move on def on_enter_output_q(self) -> None: columns = pd.MultiIndex.from_tuples( [ ("Professor", "Page Range"), ("Professor", "Taxonomy"), ("Professor", "Question"), ("Professor", "Answer"), ("Professor", "Wrong Answers"), ("Student", "Question"), ("Student", "Answers"), ] ) data = pd.DataFrame(columns=columns) for question in range(len(self.question_list)): shuffled_answers = [ self.question_list[question]["Answer"], *self.question_list[question]["Wrong Answers"], ] random.shuffle(shuffled_answers) shuffled_answers = "\n".join(shuffled_answers) new_row = [ self.question_list[question]["Page"], self.question_list[question]["Taxonomy"], self.question_list[question]["Question"], self.question_list[question]["Answer"], self.question_list[question]["Wrong Answers"], self.question_list[question]["Question"], shuffled_answers, ] data.loc[question] = new_row data.columns = ["_".join(col).strip() for col in data.columns.values] writer = pd.ExcelWriter("outputs/professor_guide.xlsx", engine="xlsxwriter") data.to_excel(writer, sheet_name="Quiz Questions", index=False) writer.close() self.send("next_state") def on_event_output_q(self, event_: dict) -> None: pass def on_exit_output_q(self) -> None: # Reset the state machine values self.question_list = [] self.most_recent_questions = [] if __name__ == "__main__": question_list = [ { "Page": "1-2", "Taxonomy": "Knowledge", "Question": "What is Python?", "Answer": "A programming language", "Wrong Answers": ["A snake", "A car brand", "A fruit"], }, { "Page": "3-4", "Taxonomy": "Comprehension", "Question": "What does HTML stand for?", "Answer": "HyperText Markup Language", "Wrong Answers": [ "High Text Machine Language", "Hyperlink Text Mode Language", "None of the above", ], }, ] columns = pd.MultiIndex.from_tuples( [ ("Professor", "Page Range"), ("Professor", "Taxonomy"), ("Professor", "Question"), ("Professor", "Answer"), ("Professor", "Wrong Answers"), ("Student", "Question"), ("Student", "Answers"), ] ) data = pd.DataFrame(columns=columns) for question in range(len(question_list)): shuffled_answers = [ question_list[question]["Answer"], *question_list[question]["Wrong Answers"], ] random.shuffle(shuffled_answers) shuffled_answers = "\n".join(shuffled_answers) new_row = [ question_list[question]["Page"], question_list[question]["Taxonomy"], question_list[question]["Question"], question_list[question]["Answer"], question_list[question]["Wrong Answers"], question_list[question]["Question"], shuffled_answers, ] data.loc[question] = new_row data.columns = ["_".join(col).strip() for col in data.columns.values] writer = pd.ExcelWriter("outputs/professor_guide.xlsx", engine="xlsxwriter") data.to_excel(writer, sheet_name="Quiz Questions", index=False) writer.close()