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import os
from dotenv import load_dotenv
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain import vectorstores
from langchain import chains
from langchain import llms
from langchain.embeddings import HuggingFaceEmbeddings
import gradio as gr
load_dotenv()
llm = llms.AI21(ai21_api_key=os.getenv('AI21_API_KEY'))
def process_pdf(pdf_file):
pdf_reader = PdfReader(pdf_file)
texts = ""
for page in pdf_reader.pages:
texts += page.extract_text()
text_splitter = CharacterTextSplitter(
separator="\n",
chunk_size=1000,
chunk_overlap=0
)
chunks = text_splitter.split_text(texts)
embeddings = HuggingFaceEmbeddings()
db = vectorstores.Chroma.from_texts(chunks, embeddings)
retriever = db.as_retriever(search_type="similarity", search_kwargs={"k":10})
qa = chains.ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever)
return qa
def answer_question(pdf_file, question, chat_history):
if not pdf_file:
return "Please upload a PDF file first."
qa = process_pdf(pdf_file)
result = qa({"question": question, "chat_history": chat_history})
chat_history.append((question, result["answer"]))
return result["answer"]
def main():
with gr.Blocks() as demo:
gr.Markdown("# PDF QA")
with gr.Row():
pdf_file = gr.File(label="Upload your PDF", file_types=[".pdf"])
question = gr.Textbox(label="Ask a question about the PDF")
output = gr.Textbox(label="Answer")
chat_history = gr.State([])
submit_btn = gr.Button("Submit")
submit_btn.click(answer_question, inputs=[pdf_file, question, chat_history], outputs=output)
demo.launch()
if __name__ == "__main__":
main() |