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import gradio as gr
import PyPDF2
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores.faiss import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain import OpenAI, VectorDBQA
import os
openai_api_key = os.environ["OPENAI_API_KEY"]
def pdf_to_text(pdf_file, query):
# Open the PDF file in binary mode
with open(pdf_file.name, 'rb') as pdf_file:
# Create a PDF reader object
pdf_reader = PyPDF2.PdfReader(pdf_file)
# Create an empty string to store the text
text = ""
# Loop through each page of the PDF
for page_num in range(len(pdf_reader.pages)):
# Get the page object
page = pdf_reader.pages[page_num]
# Extract the texst from the page and add it to the text variable
text += page.extract_text()
#embedding step
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_text(text)
embeddings = OpenAIEmbeddings()
#vector store
vectorstore = FAISS.from_texts(texts, embeddings)
#inference
qa = VectorDBQA.from_chain_type(llm=OpenAI(), chain_type="stuff", vectorstore=vectorstore)
return qa.run(query)
# Define the Gradio interface
pdf_input = [gr.inputs.File(label="PDF File")]
query_input = gr.inputs.Textbox(label="Query")
outputs = gr.outputs.Textbox(label="Chatbot Response")
interface = gr.Interface(fn=pdf_to_text, inputs=[pdf_input, query_input])
# Run the interface
interface.launch(debug = True) |