app.py

import os
from PyPDF2 import PdfReader
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import ElasticVectorSearch, Pinecone, Weaviate, FAISS
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.chains.question_answering import load_qa_chain
import gradio as gr
from langchain.embeddings import HuggingFaceEmbeddings
from langchain import HuggingFaceHub




reader = PdfReader("Faculty of Computer Science and Information Technology (1).pdf")
  # read data from the file and put them into a variable called raw_text
raw_text = ''
for i, page in enumerate(reader.pages):
    text = page.extract_text()
    if text:
        raw_text += text
text_splitter = CharacterTextSplitter(
    separator = "\n",
    chunk_size = 1000,
    chunk_overlap  = 200,
    length_function = len,
  )
texts = text_splitter.split_text(raw_text)


def main(prompt):
  embeddings = HuggingFaceEmbeddings()
  db = FAISS.from_texts(texts, embeddings)
  llm=HuggingFaceHub(repo_id="google/flan-t5-xxl",model_kwargs={"temperature":1, "max_length":512})
  chain=load_qa_chain(llm,chain_type="stuff")
  query =prompt
  docs = db.similarity_search(query)
  return chain.run(input_documents=docs, question=query)

interface=gr.Interface(fn=main,inputs=[gr.components.Textbox(label="Type your Question")],
                       outputs=gr.components.Textbox(label="Answer.."),
                       )
interface.launch(debug=True)