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| import streamlit as st | |
| from llm_functions import load_data, split_text, initialize_llm, generate_questions, create_retrieval_qa_chain | |
| # Initialization of session states | |
| # Since Streamlit always reruns the script when a widget changes, we need to initialize the session states | |
| if 'questions' not in st.session_state: | |
| st.session_state['questions'] = 'empty' | |
| st.session_state['seperated_question_list'] = 'empty' | |
| st.session_state['questions_to_answers'] = 'empty' | |
| st.session_state['submitted'] = 'empty' | |
| with st.container(): | |
| st.markdown("""# LabSoft Study Buddy | |
| """) | |
| st.divider() | |
| # Get user's OpenAI API Key | |
| openai_api_key = st.text_input(label="OpenAI API Key ", placeholder="Ex: sk-2twmA8tfCb8un4...", key="openai_api_key_input", help="How to get an OpenAI API Key: https://www.howtogeek.com/885918/how-to-get-an-openai-api-key/") | |
| # Container for markdown text | |
| with st.container(): | |
| st.markdown("""Make sure you've entered your OpenAI API Key. | |
| Don't have an API key yet? | |
| Read [this](https://medium.com/@joristechtalk/how-to-get-an-openai-api-key-for-chatgpt-fd2cf6a436c5) article on how to get an API key.""") | |
| # Let user upload a file | |
| uploaded_file = st.file_uploader("Upload your study material", type=['pdf']) | |
| if uploaded_file is not None: | |
| # Check whether user entered an API key | |
| if not openai_api_key: | |
| st.error("Please enter your OpenAI API Key") | |
| else: | |
| # Load data from PDF | |
| text_from_pdf = load_data(uploaded_file) | |
| # Split text for question generation | |
| documents_for_question_gen = split_text(text_from_pdf, chunk_size=10000, chunk_overlap=200) | |
| # Split text for question answering | |
| documents_for_question_answering = split_text(text_from_pdf, chunk_size=500, chunk_overlap=200) | |
| # Initialize large language model for question generation | |
| llm_question_gen = initialize_llm(openai_api_key=openai_api_key, model="gpt-3.5-turbo", temperature=0.4) | |
| # Initialize large language model for question answering | |
| llm_question_answering = initialize_llm(openai_api_key=openai_api_key, model="gpt-3.5-turbo", temperature=0.1) | |
| # Create questions if they have not yet been generated | |
| if st.session_state['questions'] == 'empty': | |
| with st.spinner("Generating questions..."): | |
| # Assign the generated questions to the session state. This way, the questions are only generated once. | |
| st.session_state['questions'] = generate_questions(llm=llm_question_gen, chain_type="refine", documents=documents_for_question_gen) | |
| if st.session_state['questions'] != 'empty': | |
| # Show questions on screen. You could use st.code for easy copy-pasting. | |
| st.info(st.session_state['questions']) | |
| # Split questions into a list | |
| st.session_state['questions_list'] = st.session_state['questions'].split('\n') | |
| with st.form(key='my_form'): | |
| # Create a list of questions that have to be answered | |
| st.session_state['questions_to_answers'] = st.multiselect(label="Select questions to answer", options=st.session_state['questions_list']) | |
| submitted = st.form_submit_button('Generate answers') | |
| if submitted: | |
| st.session_state['submitted'] = True | |
| if st.session_state['submitted']: | |
| # Initialize the Retrieval QA Chain | |
| print(documents_for_question_answering) | |
| with st.spinner("Generating answers..."): | |
| generate_answer_chain = create_retrieval_qa_chain(openai_api_key=openai_api_key, documents=documents_for_question_answering, llm=llm_question_answering) | |
| # For each question, generate an answer | |
| for question in st.session_state['questions_to_answers']: | |
| # Generate answer | |
| answer = generate_answer_chain.run(question) | |
| # Show answer on screen | |
| st.write(f"Question: {question}") | |
| st.info(f"Answer: {answer}") | |