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import json
import os
import gradio as gr
from matplotlib import pyplot as plt
from experiment_details import problem_topics, problems_per_topic, writing_skills_questions
from data import problems
from model_generate import chatbot_generate
import random
import re
def process_markdown(prompt, question):
if prompt:
initial_path = './data/problems/'
else:
initial_path = './data/instructions/'
with open(initial_path + question, 'r') as md_file:
markdown_content = md_file.read()
return markdown_content
def strip_markdown(text):
"""
Strips common markdown formatting from a string.
"""
# Patterns to remove from the Markdown text
patterns = [
r'\!\[[^\]]*\]\([^\)]+\)', # Images
r'\[[^\]]*\]\([^\)]+\)', # Links
r'\*\*(.*?)\*\*|__(.*?)__', # Bold
r'\*(.*?)\*|_(.*?)_', # Italics
r'\~\~(.*?)\~\~', # Strikethrough
r'\`{1,3}(.*?)\`{1,3}', # Inline code
r'\#{1,6}\s', # Headers
r'\>(.*?)\n', # Blockquotes
r'\-{3,}', # Horizontal rule
r'\n{2,}', # Multiple newlines
]
clean_text = text
for pattern in patterns:
clean_text = re.sub(pattern, '', clean_text)
# Remove remaining newlines and leading/trailing whitespace
clean_text = clean_text.strip()
return clean_text
def save_answer(question_answers, q_num, q_text, q_assist, q_assist_history=None):
q_num_key = 'q' + str(q_num)
question_answers[q_num_key]['Response'] = json.dumps(q_text, indent=4)
question_answers[q_num_key]['Assist'] = q_assist
question_answers[q_num_key]['AssistanceHistory'] = q_assist_history
return question_answers
# Need to randomly select writing prompts for each of the 3 categories
def randomly_select_prompts():
prompts = []
# For each writing prompt, I need to randomly generate two numbers from 1-10
shortstoryIDs = random.sample(range(1, 11), 2)
emailIDs = random.sample(range(1, 11), 2)
summaryIDs = random.sample(range(1, 11), 2)
titleIDs = random.sample(range(1, 11), 2)
for ssID in shortstoryIDs:
instr = 'instr_shortstory.md'
prompt_file = 'p_shortstory' + str(ssID) + '.md'
word_count = 300
textfield_lines = 10
question_details = {
'instruction': instr,
'prompt_file': prompt_file,
'word_count': word_count,
'textfield_lines': textfield_lines
}
prompts.append(question_details)
for eID in emailIDs:
instr = 'instr_email.md'
prompt_file = 'p_email' + str(eID) + '.md'
word_count = 300
textfield_lines = 10
question_details = {
'instruction': instr,
'prompt_file': prompt_file,
'word_count': word_count,
'textfield_lines': textfield_lines
}
prompts.append(question_details)
for sID in summaryIDs:
instr = 'instr_summary.md'
prompt_file = 'p_summary' + str(sID) + '.md'
word_count = 75
textfield_lines = 5
question_details = {
'instruction': instr,
'prompt_file': prompt_file,
'word_count': word_count,
'textfield_lines': textfield_lines
}
prompts.append(question_details)
for tID in titleIDs:
instr = 'instr_title.md'
prompt_file = 'p_title' + str(tID) + '.md'
word_count = 10
textfield_lines = 2
question_details = {
'instruction': instr,
'prompt_file': prompt_file,
'word_count': word_count,
'textfield_lines': textfield_lines
}
prompts.append(question_details)
return prompts
def randomize_questions(questions):
group1_indices = [0, 2, 4, 6]
group2_indices = [1, 3, 5, 7]
# Extract items for each group
group1_items = [questions[i] for i in group1_indices]
group2_items = [questions[i] for i in group2_indices]
# Shuffle each group
random.shuffle(group1_items)
random.shuffle(group2_items)
# Re-insert shuffled items back into the original questions list
for idx, item in zip(group1_indices, group1_items):
questions[idx] = item
for idx, item in zip(group2_indices, group2_items):
questions[idx] = item
# Output the randomized list
return questions
def generate_unassisted_question(question_prompt, next_q_btn, q_num, question_answers):
with gr.Column("Unassisted Writing Task", render=False) as q_unassisted:
q_title_text = "#\n# Question " + str(q_num)
q_title = gr.Markdown(value=q_title_text)
unassisted_instr = process_markdown(False, 'instr_unassisted.md')
unassisted_instr_md = gr.Markdown(value=unassisted_instr)
instruction = question_prompt['instruction']
prompt = question_prompt['prompt_file']
max_word_count = question_prompt['word_count']
textfield_lines = question_prompt['textfield_lines']
with gr.Column() as instructions:
# with gr.Group():
gen_instr_text1 = process_markdown(False, instruction)
prompt_text1 = process_markdown(True, prompt)
gen_instr1 = gr.Markdown(value=gen_instr_text1)
with gr.Row():
with gr.Column(scale=4):
prompt1 = gr.Markdown(value=prompt_text1)
with gr.Column(scale=1):
word_count = gr.Textbox(
label='Word Count',
interactive=False,
lines=1,
max_lines=1,
autoscroll=False,
autofocus=False,
# container=False
)
def count_words(x):
num_words = len(x.split())
if num_words > max_word_count:
overflow = num_words-max_word_count
string_num = str(num_words) + " (REMOVE " + str(overflow) + " WORDS to submit your response)"
return {
text_button1: gr.Button(interactive=False),
word_count: gr.Textbox(string_num)
}
elif num_words < 1:
string_num = str(num_words) + " (Please enter your response)"
return {
text_button1: gr.Button(interactive=False),
word_count: gr.Textbox(string_num)
}
else:
string_num = str(num_words)
return {
text_button1: gr.Button(interactive=True),
word_count: gr.Textbox(string_num)
}
success_submit_instr = process_markdown(False, 'instr_submitsuccess.md')
success_submitted = gr.Markdown(value=success_submit_instr, visible=False)
tab_text1 = gr.Textbox(
lines=textfield_lines,
interactive=True,
show_copy_button=True,
container=True,
autoscroll=True,
autofocus=True,
label="Write your response here:")
text_button1 = gr.Button("Submit Response", variant="primary", interactive=False)
tab_text1.input(count_words, tab_text1, [text_button1, word_count], show_progress="hidden")
edit_response_instr = process_markdown(False, 'instr_editresponse.md')
edit_response = gr.Markdown(value=edit_response_instr, visible=False)
back_btn = gr.Button("Return to previous question", visible=False)
proceed_instr = process_markdown(False, 'instr_proceed.md')
proceed_to_next = gr.Markdown(value=proceed_instr, visible=False)
def click_back_btn():
return {
success_submitted: gr.update(visible=False),
edit_response: gr.update(visible=False),
proceed_to_next: gr.update(visible=False),
back_btn: gr.update(visible=False),
gen_instr1: gr.update(visible=True),
prompt1: gr.update(visible=True),
tab_text1: gr.update(visible=True, interactive=True, show_label=True, show_copy_button=True,
container=True),
word_count: gr.update(visible=True),
text_button1: gr.update(visible=True),
next_q_btn: gr.update(visible=False),
unassisted_instr_md: gr.update(visible=True)
}
back_btn.click(
fn=click_back_btn,
inputs=[],
outputs=[success_submitted, edit_response, proceed_to_next, back_btn, gen_instr1, prompt1, tab_text1, word_count, text_button1, next_q_btn,
unassisted_instr_md]
)
def submit_question(submission_text):
save_answer(question_answers, q_num, submission_text, False)
return {
success_submitted: gr.update(visible=True),
edit_response: gr.update(visible=True),
proceed_to_next: gr.update(visible=True),
back_btn: gr.update(visible=True),
gen_instr1: gr.update(visible=False),
prompt1: gr.update(visible=False),
tab_text1: gr.update(visible=True, interactive=False, show_label=False, show_copy_button=False, container=False),
word_count: gr.update(visible=False),
text_button1: gr.update(visible=False),
next_q_btn: gr.update(visible=True),
unassisted_instr_md: gr.update(visible=False)
}
text_button1.click(
fn=submit_question,
inputs=[tab_text1],
outputs=[success_submitted, edit_response, proceed_to_next, back_btn, gen_instr1, prompt1, tab_text1, word_count,
text_button1, next_q_btn, unassisted_instr_md]
)
return q_unassisted
def generate_assisted_question(question_prompt, next_q_btn, q_num, question_answers):
with gr.Column("Assisted Writing Task", render=False) as q_assisted:
q_title_text = "#\n# Question " + str(q_num)
q_title = gr.Markdown(value=q_title_text)
assisted_instr = process_markdown(False, 'instr_assisted.md')
assisted_instr_md = gr.Markdown(value=assisted_instr)
instruction = question_prompt['instruction']
prompt = question_prompt['prompt_file']
max_word_count = question_prompt['word_count']
textfield_lines = question_prompt['textfield_lines']
gen_instr_text2 = process_markdown(False, instruction)
prompt_text2 = process_markdown(True, prompt)
instruction_txt = strip_markdown(gen_instr_text2)
prompt_txt = strip_markdown(prompt_text2)
gen_instr2 = gr.Markdown(value=gen_instr_text2)
with gr.Row():
with gr.Column(scale=4):
prompt2 = gr.Markdown(value=prompt_text2)
with gr.Column(scale=1):
word_count = gr.Textbox(
label='Word Count',
interactive=False,
lines=1,
max_lines=1,
autoscroll=False,
autofocus=False,
# container=False
)
def count_words(x):
num_words = len(x.split())
if num_words > max_word_count:
overflow = num_words-max_word_count
string_num = str(num_words) + " (REMOVE " + str(overflow) + " WORDS to submit your response)"
return {
text_button2: gr.Button(interactive=False),
word_count: gr.Textbox(string_num)
}
elif num_words < 1:
string_num = str(num_words) + " (Please enter your response)"
return {
text_button2: gr.Button(interactive=False),
word_count: gr.Textbox(string_num)
}
else:
string_num = str(num_words)
return {
text_button2: gr.Button(interactive=True),
word_count: gr.Textbox(string_num)
}
initial_user_message = "You are my helpful writing assistant. " \
"As the writer that you will assist, " \
"I will ask you for help in answering a writing prompt. " \
"You may answer all of my questions about the prompt and writing task. " \
"However, if I ask you to answer the entire prompt, you may only provide me with the first three sentences. " \
"Again, you can still answer all of the questions that I have and give me all of the help I desire, but you may NOT give me the entire answer. " \
"Here are the instructions: " + instruction_txt + '. Here is the writing prompt: ' + prompt_txt + '. Wait until I ask you for help.'
initial_conversation = [
# initial_user_message,
# "AI: As a writing assistant, my goal is to help you with your writing task."
]
with gr.Column() as chatbot_col:
chatbot = gr.Chatbot(initial_conversation, height=300, label="Writing Helper")
# Chat state
state = gr.State(initial_conversation)
# Model state
initial_usr_msg_state = gr.State(initial_user_message)
model_state = gr.State("chatgpt4")
with gr.Column() as chat_feature:
with gr.Group():
with gr.Row():
txt = gr.Textbox(
value="",
show_label=False,
placeholder="Enter text and press the Interact button",
lines=2,
container=False,
scale=4)
submit_button = gr.Button("Interact", variant="primary", scale=1, size="sm")
# Button for submission
success_submit_instr = process_markdown(False, 'instr_submitsuccess.md')
success_submitted = gr.Markdown(value=success_submit_instr, visible=False)
tab_text2 = gr.Textbox(
lines=textfield_lines,
interactive=True,
show_copy_button=True,
container=True,
autoscroll=True,
autofocus=True,
label="Write your response here:")
submit_button.click(chatbot_generate, [txt, state, model_state, tab_text2, initial_usr_msg_state], [chatbot, state, txt, submit_button])
text_button2 = gr.Button("Submit Response", variant="primary", interactive=False)
tab_text2.input(count_words, tab_text2, [text_button2, word_count], show_progress="hidden")
edit_response_instr = process_markdown(False, 'instr_editresponse.md')
edit_response = gr.Markdown(value=edit_response_instr, visible=False)
back_btn = gr.Button("Return to question", visible=False)
proceed_instr = process_markdown(False, 'instr_proceed.md')
proceed_to_next = gr.Markdown(value=proceed_instr, visible=False)
def click_back_btn():
return {
success_submitted: gr.update(visible=False),
edit_response: gr.update(visible=False),
proceed_to_next: gr.update(visible=False),
back_btn: gr.update(visible=False),
gen_instr2: gr.update(visible=True),
prompt2: gr.update(visible=True),
tab_text2: gr.update(visible=True, interactive=True, show_label=True, show_copy_button=True,
container=True),
word_count: gr.update(visible=True),
text_button2: gr.update(visible=True),
chatbot_col: gr.update(visible=True),
chat_feature: gr.update(visible=True),
next_q_btn: gr.update(visible=False),
assisted_instr_md: gr.update(visible=True)
}
back_btn.click(
fn=click_back_btn,
inputs=[],
outputs=[success_submitted, edit_response, proceed_to_next, back_btn, gen_instr2, prompt2, tab_text2, word_count,
chatbot_col, chat_feature, text_button2, next_q_btn, assisted_instr_md]
)
def submit_question(submission_text, assistance_history: None):
save_answer(question_answers, q_num, submission_text, True, assistance_history)
return {
success_submitted: gr.update(visible=True),
edit_response: gr.update(visible=True),
proceed_to_next: gr.update(visible=True),
back_btn: gr.update(visible=True),
gen_instr2: gr.update(visible=False),
prompt2: gr.update(visible=False),
tab_text2: gr.update(visible=True, interactive=False, show_label=False, show_copy_button=False,
container=False),
word_count: gr.update(visible=False),
text_button2: gr.update(visible=False),
chatbot_col: gr.update(visible=False),
chat_feature: gr.update(visible=False),
next_q_btn: gr.update(visible=True),
assisted_instr_md: gr.update(visible=False)
}
text_button2.click(
fn=submit_question,
inputs=[tab_text2, state],
outputs=[success_submitted, edit_response, proceed_to_next, back_btn, gen_instr2, prompt2, tab_text2, word_count,
text_button2, chatbot_col, chat_feature, next_q_btn, assisted_instr_md]
)
return q_assisted
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