| 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 |
| from fpdf import FPDF |
| import os |
| from PIL import Image |
|
|
| |
| st.title('Educational Assistant') |
| img = Image.open("image.jpeg") |
| st.image(img, width=300) |
| st.header('Summary, Quiz Generator, Q&A, and Study Plan') |
| st.sidebar.title('Drop your PDF here') |
|
|
| |
| with st.sidebar.expander("Instructions on how to use this app"): |
| st.markdown(""" |
| **Welcome to the Educational Assistant!** Here's how you can use the app: |
| |
| 1. **Enter your OpenAI API Key**: You need to enter your OpenAI API key in the sidebar (password type) to authenticate your requests. |
| 2. **Upload a PDF file**: Click on the 'Upload PDF' button to upload the document you want to analyze. |
| 3. **Select an Option**: Choose one of the options: |
| - **Generate Summary**: Generate a concise summary of the content in your uploaded PDF. |
| - **Generate Quiz**: Create a quiz with multiple-choice questions based on the content of your PDF. |
| - **Ask a Question**: Ask a specific question about the content, and get a detailed answer. |
| - **Generate Study Plan**: Generate a logical 7-day study plan based on the content in your PDF. |
| 4. **Download PDF**: Once the result is generated, you will be able to download it as a PDF. |
| |
| Happy learning! |
| """) |
|
|
| |
| 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', 'Generate Study Plan')) |
|
|
| |
| question_input = None |
| if option == 'Ask a Question': |
| question_input = st.text_input("Enter your question about the document:") |
|
|
| |
| def generate_pdf(response, filename="response.pdf"): |
| pdf = FPDF() |
| pdf.add_page() |
| |
| |
| pdf.add_font('ArialUnicode', '', 'arialuni.ttf', uni=True) |
| pdf.set_font('ArialUnicode', '', 12) |
| |
| |
| pdf.multi_cell(0, 10, response) |
| |
| |
| pdf.output(filename) |
| |
| |
| return filename |
|
|
| 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_1 = ChatPromptTemplate.from_messages( |
| [ |
| ("system", "You are a smart assistant. Give a summary of the user's PDF. Be polite."), |
| ("user", "{data}") |
| ] |
| ) |
|
|
| |
| llm_summary = ChatOpenAI(model="gpt-4o-mini", openai_api_key=openai_api_key) |
| output_parser = StrOutputParser() |
| chain_1 = prompt_1 | llm_summary | output_parser |
|
|
| |
| prompt_2 = ChatPromptTemplate.from_messages( |
| [ |
| ("system", "You are a smart assistant. Generate 10 multiple-choice quiz questions with 4 options each (including correct and incorrect options) from the user's PDF. Please also include the correct answer in your response. Be polite."), |
| ("user", "{data}") |
| ] |
| ) |
|
|
| |
| llm_quiz = ChatOpenAI(model="gpt-4o-mini", openai_api_key=openai_api_key) |
| output_parser = StrOutputParser() |
| chain_2 = prompt_2 | llm_quiz | output_parser |
|
|
| |
| prompt_3 = ChatPromptTemplate.from_messages( |
| [ |
| ("system", "You are a smart assistant. Answer the user's question based on the content of the PDF. Be polite."), |
| ("user", "{data}\n\nUser's question: {question}") |
| ] |
| ) |
|
|
| |
| llm_qa = ChatOpenAI(model="gpt-4o-mini", openai_api_key=openai_api_key) |
| output_parser = StrOutputParser() |
| chain_3 = prompt_3 | llm_qa | output_parser |
|
|
| |
| prompt_4 = ChatPromptTemplate.from_messages( |
| [ |
| ("system", "You are a smart assistant. Based on the content of the user's PDF, generate a 7-day study plan. Divide the content into 7 topics and assign each topic to a day. Please make it logical and balanced."), |
| ("user", "{data}") |
| ] |
| ) |
|
|
| |
| llm_study_plan = ChatOpenAI(model="gpt-4o-mini", openai_api_key=openai_api_key) |
| output_parser = StrOutputParser() |
| chain_4 = prompt_4 | llm_study_plan | output_parser |
|
|
| if option == 'Generate Summary': |
| |
| summary_response = chain_1.invoke({'data': data}) |
| st.write(summary_response) |
| |
| |
| pdf_filename = generate_pdf(summary_response, filename="summary_response.pdf") |
| st.download_button("Download Summary as PDF", data=open(pdf_filename, "rb").read(), file_name=pdf_filename, mime="application/pdf") |
| |
| elif option == 'Generate Quiz': |
| |
| quiz_response = chain_2.invoke({'data': data}) |
| st.write(quiz_response) |
| |
| |
| pdf_filename = generate_pdf(quiz_response, filename="quiz_response.pdf") |
| st.download_button("Download Quiz as PDF", data=open(pdf_filename, "rb").read(), file_name=pdf_filename, mime="application/pdf") |
| |
| elif option == 'Ask a Question' and question_input: |
| |
| generate_answer = st.button("Generate Answer") |
| |
| if generate_answer: |
| |
| question_answer_response = chain_3.invoke({'data': data, 'question': question_input}) |
| st.write(question_answer_response) |
| |
| |
| pdf_filename = generate_pdf(question_answer_response, filename="question_answer_response.pdf") |
| st.download_button("Download Answer as PDF", data=open(pdf_filename, "rb").read(), file_name=pdf_filename, mime="application/pdf") |
|
|
| elif option == 'Generate Study Plan': |
| |
| study_plan_response = chain_4.invoke({'data': data}) |
| st.write(study_plan_response) |
| |
| |
| pdf_filename = generate_pdf(study_plan_response, filename="study_plan_response.pdf") |
| st.download_button("Download Study Plan as PDF", data=open(pdf_filename, "rb").read(), file_name=pdf_filename, mime="application/pdf") |
|
|
| else: |
| st.sidebar.warning("Please enter your OpenAI API Key to proceed.") |