updated .env

app.py

@@ -64,7 +64,7 @@ def generate_questions(
     return (
         "## Questions Ready for Download Below",
         gr.update(
-            visible=True, value=f"{Path.cwd().joinpath('outputs/professor_guide.xlsx')}"
+            visible=True, value=f"{Path.cwd().joinpath('outputs/professor_guide.csv')}"
         ),
         gr.update(
             interactive=False
@@ -131,4 +131,4 @@ with gr.Blocks() as demo:
 #     with gr.Blocks() as failed:
 #         gr.Markdown("Environment Error - No username and password provided")
 #     failed.launch()
-demo.launch()
+demo.launch(auth=(os.environ.get('HF_USERNAME',''),os.environ.get('HF_PASSWORD','')))

uw_programmatic/base_machine.py

@@ -14,6 +14,8 @@ from griptape.artifacts import ListArtifact, TextArtifact
 from griptape.configs import Defaults
 from griptape.configs.drivers import (
     OpenAiDriversConfig,
+    AnthropicDriversConfig,
+    GoogleDriversConfig,
 )
 from griptape.drivers import (
     GriptapeCloudVectorStoreDriver,
@@ -56,6 +58,7 @@ Defaults.drivers_config = OpenAiDriversConfig(
     prompt_driver=OpenAiChatPromptDriver(model="gpt-4o", max_tokens=4096)
 )
 
+
 def custom_dict_merge(dict1: dict, dict2: dict) -> dict:
     result = dict1.copy()
     for key, value in dict2.items():

uw_programmatic/config.yaml

@@ -34,7 +34,7 @@ rulesets:
   incorrect_answers_creator:
     name: Create Wrong Answers
     rules:
-    - '"Return ONLY a list of 3 incorrect answers. No markdown, no commentary, no
+    - '"Return ONLY a list of 4 incorrect answers. No markdown, no commentary, no
       backticks.'
     - '"All incorrect answers should be different, but plausible answers to the question.'
     - '"Incorrect answers may reference material from the knowledge base, but must

uw_programmatic/uw_machine.py

@@ -1,15 +1,19 @@
 from __future__ import annotations
 
 import ast
+import xlsxwriter
 import csv
 import json
 from pathlib import Path
 import random
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Any
 from griptape.structures import Agent
+from h11 import Event
 import pandas as pd
 
-from uw_programmatic.base_machine import UWBaseMachine
+from base_machine import UWBaseMachine
+from griptape.events import EventBus, EventListener, FinishStructureRunEvent, BaseEvent
+from griptape.loaders import PdfLoader
 
 if TYPE_CHECKING:
     from griptape.tools import BaseTool
@@ -26,6 +30,8 @@ class UWMachine(UWBaseMachine):
         """Starts the machine."""
         # Clear input history.
         # Clear csv file
+        with Path(Path.cwd().joinpath("outputs/similarity_step.csv")).open("w") as file:
+            file.write("")
         self.send("enter_first_state")
 
     def on_event_gather_parameters(self, event_: dict) -> None:
@@ -83,6 +89,21 @@ class UWMachine(UWBaseMachine):
     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
+        with Path(Path.cwd().joinpath("outputs/similarity_step.csv")).open(
+            "a", newline=""
+        ) as file:
+            writer = csv.DictWriter(
+                file,
+                fieldnames=[
+                    "Question",
+                    "Answer",
+                    "Wrong Answers",
+                    "Page",
+                    "Taxonomy",
+                ],
+            )
+            writer.writerow({"Question": "LIST OF QUESTIONS GENERATED THIS ROUND"})
+            writer.writerows(self.most_recent_questions)
         merged_list = [*self.question_list, *self.most_recent_questions]
         prompt = f"{merged_list}"
         self.get_structure("similarity_auditor").run(prompt)
@@ -107,43 +128,60 @@ class UWMachine(UWBaseMachine):
                                     )  # 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
+                                    )
+                                ]
+                                with Path(
+                                    Path.cwd().joinpath("outputs/similarity_step.csv")
+                                ).open("a", newline="") as file:
+                                    writer = csv.DictWriter(
+                                        file,
+                                        fieldnames=[
+                                            "Question",
+                                            "Answer",
+                                            "Wrong Answers",
+                                            "Page",
+                                            "Taxonomy",
+                                        ],
+                                    )
+                                    writer.writerow(
+                                        {"Question": "QUESTIONS REMOVED THIS ROUND!"}
+                                    )
+                                    if len(deleted_q):
+                                        writer.writerows(deleted_q)
+                                    else:
+                                        writer.writerow({"Question": "No q removed"})
                                 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()
+        with Path(Path.cwd().joinpath("outputs/professor_guide.csv")).open(
+            "w", newline=""
+        ) as file:
+            writer = csv.writer(file)
+            for question in range(len(self.question_list)):
+                # TODO: Shuffle answers according to row, keep correct answer in random section. Answer column is a number.
+                new_row = [self.question_list[question]["Question"]]
+                wrong_answers = list(self.question_list[question]["Wrong Answers"])
+                column = random.randint(1, len(wrong_answers) + 1)
+                new_row.append(column)
+                for i in range(1, len(wrong_answers) + 2):
+                    if i == column:
+                        new_row.append(self.question_list[question]["Answer"])
+                    else:
+                        new_row.append(wrong_answers.pop())
+                new_row.append(self.question_list[question]["Page"])
+                new_row.append(self.question_list[question]["Taxonomy"])
+                writer.writerow(new_row)
         self.send("next_state")
 
     def on_event_output_q(self, event_: dict) -> None:
@@ -177,36 +215,21 @@ class UWMachine(UWBaseMachine):
             },
         ]
 
-        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()
+        with Path(Path.cwd().joinpath("outputs/professor_guide.csv")).open(
+            "w", newline=""
+        ) as file:
+            writer = csv.writer(file)
+            for question in range(len(question_list)):
+                # TODO: Shuffle answers according to row, keep correct answer in random section. Answer column is a number.
+                new_row = [question_list[question]["Question"]]
+                wrong_answers = list(question_list[question]["Wrong Answers"])
+                column = random.randint(1, len(wrong_answers) + 1)
+                new_row.append(column)
+                for i in range(1, len(wrong_answers) + 2):
+                    if i == column:
+                        new_row.append(question_list[question]["Answer"])
+                    else:
+                        new_row.append(wrong_answers.pop())
+                new_row.append(question_list[question]["Page"])
+                new_row.append(question_list[question]["Taxonomy"])
+                writer.writerow(new_row)