Spaces:
Runtime error
Runtime error
| import streamlit as st | |
| from doc_loading import get_article_text, read_pdf_text | |
| from utils import get_topics | |
| from llm_functions import generate_qa_pairs, evaluate_answer, get_conversational_chain, get_topics_from_chunk | |
| from langchain.text_splitter import RecursiveCharacterTextSplitter | |
| from langchain_openai import OpenAIEmbeddings | |
| from langchain.vectorstores import FAISS | |
| from langchain_openai import ChatOpenAI | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| # Setting up basics | |
| st.set_page_config(page_title="LLM UC") | |
| st.header("Let's check what you know") | |
| # Greeting the user | |
| st.write("Welcome!!") | |
| # st.session_state | |
| # Sidebar with selectbox | |
| if "option" not in st.session_state: | |
| option = st.selectbox("How are you going to input your document?", | |
| ("Upload PDF", "Blog link", "YouTube Link", "Paste copied article")) | |
| # Conditionally show components based on user's choice | |
| file_name = "" | |
| main_text = "" | |
| if option == "Upload PDF": | |
| uploaded_file = st.file_uploader("Choose a PDF file", type=["pdf"], key="uploading_pdf") | |
| if uploaded_file is not None: | |
| # Process the uploaded file | |
| st.write("PDF uploaded successfully!, we will read only first 5 pages") | |
| main_text = read_pdf_text(uploaded_file) | |
| elif option == "Paste copied article": | |
| main_text = st.text_area("Paste your article here", key="article_key") | |
| if st.button('Submit'): | |
| # Process the pasted text | |
| st.write("Text submitted successfully!") | |
| else: | |
| link_input = st.text_input("Paste your blog/youtube link here.", key='link_key') | |
| if st.button('Submit'): | |
| # Process the link | |
| st.write("link submitted") | |
| if option == "YouTube Link": | |
| st.write("This functionality in under construction.") | |
| main_text = "" | |
| else: | |
| # lets try now | |
| article_text = get_article_text(link_input) | |
| if article_text: | |
| main_text = "" | |
| st.write("This functionality in under construction.") | |
| else: | |
| st.write("Unable to fetch text from url. Can you please check the link?") | |
| # Show a warning if the user hasn't selected an option or if the uploaded file is not a PDF | |
| if option == "Upload PDF": | |
| if uploaded_file is not None and uploaded_file.type != "application/pdf": | |
| st.error("Please choose a PDF file only.") | |
| if "total_text" not in st.session_state: | |
| st.session_state['total_text'] = main_text | |
| if len(main_text) > 0: | |
| # creating chunks of the given article | |
| text_splitter = RecursiveCharacterTextSplitter( | |
| chunk_size=1000, | |
| chunk_overlap=100, | |
| length_function=len, | |
| is_separator_regex=False | |
| ) | |
| texts = text_splitter.create_documents([main_text]) | |
| # building vector database | |
| embeddings = OpenAIEmbeddings() | |
| # store in vector db | |
| db = FAISS.from_documents(texts, embeddings) | |
| hash_name = f"{option.replace(' ', '-')}" | |
| db.save_local(f'faiss_{hash_name}_index') | |
| # Create a toggle button | |
| toggle_button_state = st.checkbox("check this, if i should quiz you") | |
| # Display a message based on the toggle button state | |
| if toggle_button_state: | |
| # make the quiz | |
| st.write("Quiz incoming...") | |
| # give selection of toughness | |
| toughness_selection = None | |
| while "toughness_selection" not in st.session_state: | |
| toughness_selection = st.selectbox("Select the question toughness", | |
| ("Easy", "Moderate", "Tough")) | |
| st.session_state['toughness_selection'] = toughness_selection | |
| top_topics = None | |
| if "top_topics" not in st.session_state: | |
| topics_chain = get_topics_from_chunk() | |
| top_topics = get_topics(texts[:10], topics_chain) | |
| top_topics.append("Any") | |
| st.session_state['top_topics'] = top_topics | |
| topic_selection = None | |
| if "topic_selection" not in st.session_state: | |
| top_topics = st.session_state['top_topics'] | |
| topic_selection = st.selectbox("Select the topic i should quiz from!!", | |
| tuple(top_topics), key="topic_selection") | |
| st.session_state['topics_selection'] = topic_selection | |
| toughness_selection = st.session_state['toughness_selection'] | |
| if "response" not in st.session_state: | |
| ques_chain = generate_qa_pairs() | |
| topic_selection = st.session_state['topic_selection'] | |
| toughness_selection = st.session_state['toughness_selection'] | |
| # st.write(f"here we go, a {toughness_selection} level question from {topic_selection} topic.") | |
| docs_for_questions = db.similarity_search(topic_selection, k=5) | |
| response = ques_chain.invoke({"context": docs_for_questions, | |
| "topic": topic_selection, | |
| 'toughness': toughness_selection}) | |
| st.session_state['response'] = response[0] | |
| if "scoring" not in st.session_state: | |
| eval_chain = evaluate_answer() | |
| response = st.session_state['response'] | |
| st.write(f"\n Question: {response['question']}") | |
| user_answer = st.text_input(f"Answer here: ", key="my_ans") | |
| if st.button(f"Evaluate"): | |
| score = eval_chain({"context": response['answer'], | |
| "answer": user_answer}) | |
| st.write(f"You scored {score['score']}/10") | |
| if int(score['score'])<6: | |
| st.write(f"The correct answer would be: {response['answer']}") | |
| else: | |
| st.write("Good Job!!!") | |
| st.session_state['scoring'] = score['score'] | |
| elif st.button("Don't know"): | |
| st.write(f"The correct answer would be: {response['answer']}") | |
| else: | |
| st.write("What's your question?") | |
| # let the user ask question. | |
| if "input_question" not in st.session_state or st.session_state['input_question'] == "": | |
| input_question = st.text_input("Here, input your question and click `Answer this`", key="Question") | |
| st.session_state['input_question'] = input_question | |
| answer_button = st.button('Answer this', key='my_question') | |
| if answer_button: | |
| input_question = st.session_state['input_question'] | |
| docs = db.similarity_search(input_question, k=5) | |
| chain = get_conversational_chain() | |
| response = chain({"input_documents" : docs, | |
| "question": input_question, | |
| }) | |
| st.write(response['output_text']) | |
| del st.session_state['input_question'] | |