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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
# Set up Streamlit UI
st.title('Educational Assistant')
st.header('Summary, Quiz Generator, Q&A, and Study Plan')
st.sidebar.title('Drop your PDF here')
# Input OpenAI API key from keyboard
openai_api_key = st.sidebar.text_input("Enter your OpenAI API Key", type="password")
user_file_upload = st.sidebar.file_uploader(label='', type='pdf')
# Sidebar option selection for Summary, Quiz, Q&A, or Study Plan
option = st.sidebar.radio("Choose an option", ('Generate Summary', 'Generate Quiz', 'Ask a Question', 'Generate Study Plan'))
# Input for asking questions (only visible when "Ask a Question" is selected)
question_input = None
if option == 'Ask a Question':
question_input = st.text_input("Enter your question about the document:")
# Function to generate a PDF and allow download
def generate_pdf(response, filename="response.pdf"):
pdf = FPDF()
pdf.add_page()
# Adding a Unicode-compatible font (like Arial Unicode MS or other compatible font)
pdf.add_font('ArialUnicode', '', 'arialuni.ttf', uni=True) # Path to font, make sure this is correct for your system
pdf.set_font('ArialUnicode', '', 12)
# Add the response text
pdf.multi_cell(0, 10, response)
# Save to a temporary file
pdf.output(filename)
# Return the file path
return filename
if openai_api_key:
# Set OpenAI API key
openai.api_key = openai_api_key
if user_file_upload:
# Read the uploaded file
pdf_data = user_file_upload.read()
# Save the uploaded file to a temporary location
with open("temp_pdf_file.pdf", "wb") as f:
f.write(pdf_data)
# Load the temporary PDF file
loader = PyPDFLoader("temp_pdf_file.pdf")
data = loader.load_and_split()
## Prompt Template for Summary
prompt_1 = ChatPromptTemplate.from_messages(
[
("system", "You are a smart assistant. Give a summary of the user's PDF. Be polite."),
("user", "{data}")
]
)
# Pass the OpenAI API key explicitly to the ChatOpenAI instance
llm_summary = ChatOpenAI(model="gpt-4o-mini", openai_api_key=openai_api_key) # Pass the key here
output_parser = StrOutputParser()
chain_1 = prompt_1 | llm_summary | output_parser
## Prompt Template for Quiz
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}")
]
)
# Pass the OpenAI API key explicitly to the ChatOpenAI instance
llm_quiz = ChatOpenAI(model="gpt-4o-mini", openai_api_key=openai_api_key) # Pass the key here
output_parser = StrOutputParser()
chain_2 = prompt_2 | llm_quiz | output_parser
## Prompt Template for Question-Answering
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}")
]
)
# Pass the OpenAI API key explicitly to the ChatOpenAI instance
llm_qa = ChatOpenAI(model="gpt-4o-mini", openai_api_key=openai_api_key) # Pass the key here
output_parser = StrOutputParser()
chain_3 = prompt_3 | llm_qa | output_parser
## Prompt Template for Study Plan
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}")
]
)
# Pass the OpenAI API key explicitly to the ChatOpenAI instance
llm_study_plan = ChatOpenAI(model="gpt-4o-mini", openai_api_key=openai_api_key) # Pass the key here
output_parser = StrOutputParser()
chain_4 = prompt_4 | llm_study_plan | output_parser
if option == 'Generate Summary':
# Generate summary
summary_response = chain_1.invoke({'data': data})
st.write(summary_response)
# Generate PDF for the summary and offer it as a download
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':
# Generate quiz
quiz_response = chain_2.invoke({'data': data})
st.write(quiz_response)
# Generate PDF for the quiz and offer it as a download
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:
# Add a "Generate Answer" button
generate_answer = st.button("Generate Answer")
if generate_answer:
# Generate answer for the user's question
question_answer_response = chain_3.invoke({'data': data, 'question': question_input})
st.write(question_answer_response)
# Generate PDF for the question answer and offer it as a download
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':
# Generate study plan
study_plan_response = chain_4.invoke({'data': data})
st.write(study_plan_response)
# Generate PDF for the study plan and offer it as a download
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.") |