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| import streamlit as st | |
| import pandas as pd | |
| import os | |
| # Import the TestCaseGenerator | |
| from test_case_generator import TestCaseGenerator | |
| def main(): | |
| st.set_page_config( | |
| page_title="Question Generation App", | |
| page_icon="📝", | |
| layout="wide" | |
| ) | |
| # Custom CSS for styling | |
| st.markdown(""" | |
| <style> | |
| .main-title { | |
| font-size: 3em; | |
| color: #2C3E50; | |
| text-align: center; | |
| margin-bottom: 30px; | |
| } | |
| .stButton>button { | |
| background-color: #3498DB; | |
| color: white; | |
| border: none; | |
| padding: 10px 20px; | |
| border-radius: 5px; | |
| transition: all 0.3s; | |
| } | |
| .stButton>button:hover { | |
| background-color: #2980B9; | |
| transform: scale(1.05); | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| # Title | |
| st.markdown("<h1 class='main-title'>📝 AI Question Generator</h1>", unsafe_allow_html=True) | |
| # Sidebar for inputs | |
| st.sidebar.header("Configuration") | |
| # File uploader | |
| uploaded_file = st.sidebar.file_uploader( | |
| "Upload PDF Document", | |
| type=['pdf'], | |
| help="Please upload a PDF file to generate questions from" | |
| ) | |
| # Question type selection | |
| generator = TestCaseGenerator() | |
| question_types = st.sidebar.multiselect( | |
| "Select Question Types", | |
| generator.available_question_types, | |
| default=['hallucination', 'toxicity'] | |
| ) | |
| # Number of questions | |
| num_questions = st.sidebar.slider( | |
| "Number of Questions per Type", | |
| min_value=1, | |
| max_value=20, | |
| value=5 | |
| ) | |
| # Generate button | |
| generate_button = st.sidebar.button("Generate Questions", use_container_width=True) | |
| # Main content area | |
| main_content = st.container() | |
| # Generation logic | |
| if generate_button and uploaded_file and question_types: | |
| with st.spinner('Generating questions...'): | |
| # Create results DataFrame | |
| final_df = pd.DataFrame() | |
| # Generate questions for each selected type | |
| for q_type in question_types: | |
| try: | |
| type_df = generator.generate_testcases( | |
| uploaded_file, | |
| question_type=q_type, | |
| num_testcases=num_questions | |
| ) | |
| type_df['question_type'] = q_type | |
| final_df = pd.concat([final_df, type_df], ignore_index=True) | |
| except Exception as e: | |
| st.error(f"Error generating {q_type} questions: {e}") | |
| # Display results | |
| if not final_df.empty: | |
| st.success(f"Generated {len(final_df)} questions!") | |
| # Display questions in an interactive table | |
| st.dataframe( | |
| final_df[['question_type', 'question', 'answer']], | |
| use_container_width=True | |
| ) | |
| # Download button for Excel | |
| csv = final_df.to_csv(index=False) | |
| st.download_button( | |
| label="Download Questions as CSV", | |
| data=csv, | |
| file_name="generated_questions.csv", | |
| mime="text/csv" | |
| ) | |
| else: | |
| st.warning("No questions could be generated. Please check your inputs.") | |
| elif not uploaded_file: | |
| st.info("Please upload a PDF document to start generating questions.") | |
| if __name__ == "__main__": | |
| main() |