| import streamlit as st | |
| from langchain_community.document_loaders import PyPDFLoader | |
| import openai | |
| from langchain.prompts import ChatPromptTemplate | |
| from langchain_core.output_parsers import StrOutputParser | |
| from langchain.chat_models import ChatOpenAI | |
| import pandas as pd | |
| from datetime import datetime, timedelta | |
| st.title('Educational Assistant') | |
| st.header('Summary, Quiz Generator, Q&A, and Topics to be Covered') | |
| st.sidebar.title('Drop your PDF here') | |
| openai_api_key = st.sidebar.text_input("Enter your OpenAI API Key", type="password") | |
| user_file_upload = st.sidebar.file_uploader(label='', type='pdf') | |
| option = st.sidebar.radio("Choose an option", ('Generate Summary', 'Generate Quiz', 'Ask a Question', 'Topics to be Covered')) | |
| if openai_api_key: | |
| openai.api_key = openai_api_key | |
| if user_file_upload: | |
| pdf_data = user_file_upload.read() | |
| with open("temp_pdf_file.pdf", "wb") as f: | |
| f.write(pdf_data) | |
| loader = PyPDFLoader("temp_pdf_file.pdf") | |
| data = loader.load_and_split() | |
| prompt_4 = ChatPromptTemplate.from_messages( | |
| [ | |
| ("system", "You are a smart assistant. Analyze the user's PDF and generate 7 topics with detailed themes for the next 7 days. Output format: 'Day X: Task\tTheme'"), | |
| ("user", "{data}") | |
| ] | |
| ) | |
| llm_topics = ChatOpenAI(model="gpt-4o-mini", openai_api_key=openai_api_key) | |
| output_parser = StrOutputParser() | |
| chain_4 = prompt_4 | llm_topics | output_parser | |
| if option == 'Topics to be Covered': | |
| topics_response = chain_4.invoke({'data': data}) | |
| topics_list = topics_response.split("\n") | |
| start_date = datetime.today() | |
| table_data = [] | |
| for i in range(7): | |
| day_date = start_date + timedelta(days=i) | |
| topic_parts = topics_list[i].split('\t') if i < len(topics_list) else [f"Day {i+1}: you will figure out my llm program", "you will figure out my llm program"] | |
| table_data.append([day_date.strftime("%d-%b-%y"), topic_parts[0], topic_parts[1]]) | |
| df = pd.DataFrame(table_data, columns=["Day", "Tasks", "Theme"]) | |
| st.write("### Topics to be Covered in the Next 7 Days") | |
| st.table(df) | |
| else: | |
| st.sidebar.warning("Please enter your OpenAI API Key to proceed.") | |