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| 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_pdf_text | |
| 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 PDF")) | |
| 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 PDF": | |
| file = st.file_uploader("Upload PDF Files") | |
| if file is not None: | |
| try: | |
| text = get_pdf_text(file) | |
| except Exception as e: | |
| st.error(f"Error reading PDF 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.") |