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import streamlit as st
from PyPDF2 import PdfReader
from langchain.text_splitter import RecursiveCharacterTextSplitter
import os
from langchain_community.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.prompts import PromptTemplate
from dotenv import load_dotenv
from langchain_openai import OpenAI, ChatOpenAI
from langchain_openai import OpenAIEmbeddings
load_dotenv()
os.environ["OPENAI_API_KEY"] = st.secrets["OPENAI_API_KEY"]
os.environ["LANGCHAIN_TRACING_V2"]="true"
os.environ["LANGCHAIN_API_KEY"] = st.secrets["LANGCHAIN_API_KEY"]
def get_pdf_text(pdf_docs):
text = ""
for pdf in pdf_docs:
pdf_reader = PdfReader(pdf)
for page in pdf_reader.pages:
try:
page_text = page.extract_text()
if page_text:
text += page_text
except Exception as e:
print(f"Error reading page: {e}")
return text
def get_text_chunks(text):
text_splitter = RecursiveCharacterTextSplitter(chunk_size=2500, chunk_overlap=750)
chunks = text_splitter.split_text(text)
return chunks
def get_vector_store(text_chunks):
vector_store = FAISS.from_texts(text_chunks, OpenAIEmbeddings())
vector_store.save_local("faiss_index")
def get_conversational_chain():
prompt_template = """You are an assistant for teachers. Your objective is to provide
comprehensive and accurate responses based on the context provided. Make sure that
you generate whole output.
context: {context}
question: {question}
"""
model = ChatOpenAI(model="gpt-3.5-turbo")
prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
return chain
def user_input(user_question):
embeddings = OpenAIEmbeddings()
new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
docs = new_db.similarity_search(user_question)
chain = get_conversational_chain()
result = ""
with st.spinner("Processing..."):
response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
result = response["output_text"]
st.session_state.chat_history.append({"role": "assistant", "content": result})
def main():
st.set_page_config("Chat PDF")
st.header("AI-powered EduPlanner💁")
if "chat_history" not in st.session_state:
st.session_state.chat_history = []
with st.sidebar:
#st.image("pic123.png")
st.title("Menu:")
pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True)
if st.button("Submit & Process"):
if pdf_docs:
with st.spinner("Processing..."):
raw_text = get_pdf_text(pdf_docs)
text_chunks = get_text_chunks(raw_text)
get_vector_store(text_chunks)
st.success("Done")
else:
st.warning("Please upload PDF files first before submitting.")
# Display chat history
for idx, chat in enumerate(st.session_state.chat_history):
with st.chat_message(chat["role"]):
st.write(chat["content"])
if chat["role"] == "assistant":
st.download_button(
label="Download",
data=chat["content"],
file_name=f"response_{idx}.txt",
mime="text/plain",
key=f"download_{idx}",
)
user_question = st.chat_input("Ask a Question from the PDF Files")
if user_question:
if not pdf_docs:
st.warning("Please upload PDF files and process first before asking questions.")
else:
st.session_state.chat_history.append({"role": "user", "content": user_question})
st.chat_message("user").write(user_question)
user_input(user_question)
st.experimental_rerun()
if __name__ == "__main__":
main()
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