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import gradio as gr
from PyPDF2 import PdfReader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
from langchain.callbacks import get_openai_callback
import os
def process_pdf_and_answer_question(pdf, user_question, openai_key):
if pdf is None or user_question == "":
return "Please upload a PDF and enter a question.", None
if not pdf.name.lower().endswith('.pdf'):
return "Please upload a PDF file.", None
pdf_reader = PdfReader(pdf)
text = ""
for page in pdf_reader.pages:
text += page.extract_text()
text_splitter = CharacterTextSplitter(
separator="\n",
chunk_size=1000,
chunk_overlap=200,
length_function=len
)
chunks = text_splitter.split_text(text)
# Set OpenAI API key from the provided input
os.environ["OPENAI_API_KEY"] = openai_key
embeddings = OpenAIEmbeddings()
knowledge_base = FAISS.from_texts(chunks, embeddings)
docs = knowledge_base.similarity_search(user_question)
llm = OpenAI()
chain = load_qa_chain(llm, chain_type="stuff")
with get_openai_callback() as cb:
response = chain.run(input_documents=docs, question=user_question)
print(cb)
return response
# Define the Gradio Interface
gradio_app = gr.Interface(
fn=process_pdf_and_answer_question,
inputs=[
gr.File(label="Upload a PDF"),
gr.Textbox(label="Ask a question about this PDF:"),
gr.Textbox(label="Enter your OpenAI API Key:")
],
outputs=gr.Textbox(label="Response")
)
if __name__ == "__main__":
gradio_app.launch(share=True)
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