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main.py
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main.py
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import nltk
nltk.download('punkt')
nltk.download('stopwords')
nltk.download('brown')
nltk.download('wordnet')
import streamlit as st
st.set_page_config(
page_icon='cyclone',
page_title="Question Generator",
initial_sidebar_state="auto",
menu_items={
"About" : "Hi this our project."
}
)
from text_processing import clean_text, get_text_from_document
from question_generation import generate_questions_async
from visualization import display_word_cloud
from data_export import export_to_csv, export_to_pdf
from feedback import collect_feedback, analyze_feedback, export_feedback_data
from utils import get_session_id, initialize_state, get_state, set_state, display_info, QuestionGenerationError, entity_linking
import asyncio
import time
import pandas as pd
from data_export import send_email_with_attachment
st.set_option('deprecation.showPyplotGlobalUse',False)
with st.sidebar:
select_model = st.selectbox("Select Model", ("T5-large","T5-small"))
if select_model == "T5-large":
modelname = "DevBM/t5-large-squad"
elif select_model == "T5-small":
modelname = "AneriThakkar/flan-t5-small-finetuned"
def main():
st.title(":blue[Question Generator System]")
session_id = get_session_id()
state = initialize_state(session_id)
if 'feedback_data' not in st.session_state:
st.session_state.feedback_data = []
with st.sidebar:
show_info = st.toggle('Show Info',False)
if show_info:
display_info()
st.subheader("Customization Options")
# Customization options
input_type = st.radio("Select Input Preference", ("Text Input","Upload Document"))
with st.expander("Choose the Additional Elements to show"):
show_context = st.checkbox("Context",False)
show_answer = st.checkbox("Answer",True)
show_options = st.checkbox("Options",True)
show_entity_link = st.checkbox("Entity Link For Wikipedia",True)
show_qa_scores = st.checkbox("QA Score",True)
show_blank_question = st.checkbox("Fill in the Blank Questions",True)
num_beams = st.slider("Select number of beams for question generation", min_value=2, max_value=10, value=2)
context_window_size = st.slider("Select context window size (number of sentences before and after)", min_value=1, max_value=5, value=1)
num_questions = st.slider("Select number of questions to generate", min_value=1, max_value=1000, value=5)
col1, col2 = st.columns(2)
with col1:
extract_all_keywords = st.toggle("Extract Max Keywords",value=False)
with col2:
enable_feedback_mode = st.toggle("Enable Feedback Mode",False)
text = None
if input_type == "Text Input":
text = st.text_area("Enter text here:", value="Joe Biden, the current US president is on a weak wicket going in for his reelection later this November against former President Donald Trump.", help="Enter or paste your text here")
elif input_type == "Upload Document":
file = st.file_uploader("Upload Document File", type=['pdf', 'docx', 'doc', 'pptx', 'ppt', 'html', 'tex', 'txt'])
if file is not None:
try:
text = get_text_from_document(file)
except Exception as e:
st.error(f"Error reading file: {str(e)}")
text = None
if text:
text = clean_text(text)
with st.expander("Show text"):
st.write(text)
# st.text(text)
generate_questions_button = st.button("Generate Questions",help="This is the generate questions button")
# st.markdown('<span aria-label="Generate questions button">Above is the generate questions button</span>', unsafe_allow_html=True)
if generate_questions_button and text:
start_time = time.time()
with st.spinner("Generating questions..."):
try:
state['generated_questions'] = asyncio.run(generate_questions_async(text, num_questions, context_window_size, num_beams, extract_all_keywords,modelname))
if not state['generated_questions']:
st.warning("No questions were generated. The text might be too short or lack suitable content.")
else:
st.success(f"Successfully generated {len(state['generated_questions'])} questions!")
except QuestionGenerationError as e:
st.error(f"An error occurred during question generation: {str(e)}")
except Exception as e:
st.error(f"An unexpected error occurred: {str(e)}")
print("\n\n!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n\n")
data = get_state(session_id)
print(data)
end_time = time.time()
print(f"Time Taken to generate: {end_time-start_time}")
set_state(session_id, 'generated_questions', state['generated_questions'])
# sort question based on their quality score
state['generated_questions'] = sorted(state['generated_questions'],key = lambda x: x['overall_score'], reverse=True)
# Display generated questions
if state['generated_questions']:
st.header("Generated Questions:",divider='blue')
for i, q in enumerate(state['generated_questions']):
st.subheader(body=f":orange[Q{i+1}:] {q['question']}")
if show_blank_question is True:
st.write(f"**Fill in the Blank Question:** {q['blank_question']}")
if show_context is True:
st.write(f"**Context:** {q['context']}")
if show_answer is True:
st.write(f"**Answer:** {q['answer']}")
if show_options is True:
st.write(f"**Options:**")
for j, option in enumerate(q['options']):
st.write(f"{chr(65+j)}. {option}")
if show_entity_link is True:
linked_entity = entity_linking(q['answer'])
if linked_entity:
st.write(f"**Entity Link:** {linked_entity}")
if show_qa_scores is True:
m1,m2,m3,m4 = st.columns([1.7,1,1,1])
m1.metric("Overall Quality Score", value=f"{q['overall_score']:,.2f}")
m2.metric("Relevance Score", value=f"{q['relevance_score']:,.2f}")
m3.metric("Complexity Score", value=f"{q['complexity_score']:,.2f}")
m4.metric("Spelling Correctness", value=f"{q['spelling_correctness']:,.2f}")
# q['context'] = st.text_area(f"Edit Context {i+1}:", value=q['context'], key=f"context_{i}")
if enable_feedback_mode:
collect_feedback(
i,
question = q['question'],
answer = q['answer'],
context = q['context'],
options = q['options'],
)
st.write("---")
# Export buttons
# if st.session_state.generated_questions:
if state['generated_questions']:
with st.sidebar:
# Adding error handling while exporting the files
# ---------------------------------------------------------------------
try:
csv_data = export_to_csv(state['generated_questions'])
st.download_button(label="Download CSV", data=csv_data, file_name='questions.csv', mime='text/csv')
pdf_data = export_to_pdf(state['generated_questions'])
st.download_button(label="Download PDF", data=pdf_data, file_name='questions.pdf', mime='application/pdf')
except Exception as e:
st.error(f"Error exporting CSV: {e}")
with st.expander("View Visualizations"):
questions = [tpl['question'] for tpl in state['generated_questions']]
overall_scores = [tpl['overall_score'] for tpl in state['generated_questions']]
st.subheader('WordCloud of Questions',divider='rainbow')
display_word_cloud(questions)
st.subheader('Overall Scores',divider='violet')
overall_scores = pd.DataFrame(overall_scores,columns=['Overall Scores'])
st.line_chart(overall_scores)
# View Feedback Statistics
with st.expander("View Feedback Statistics"):
analyze_feedback()
if st.button("Export Feedback"):
feedback_data = export_feedback_data()
pswd = st.secrets['EMAIL_PASSWORD']
send_email_with_attachment(
email_subject='feedback from QGen',
email_body='Please find the attached feedback JSON file.',
recipient_emails=['apjc01unique@gmail.com', 'channingfisher7@gmail.com'],
sender_email='apjc01unique@gmail.com',
sender_password=pswd,
attachment=feedback_data
)
print("********************************************************************************")
if __name__ == '__main__':
try:
main()
except Exception as e:
st.error(f"An unexpected error occurred: {str(e)}")
st.error("Please try refreshing the page. If the problem persists, contact support.")