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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
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.")
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