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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)
examples = [
[os.path.abspath("NASDAQ_AAPL_2020.pdf"), "how much are the outstanding shares ?"],
[os.path.abspath("NASDAQ_AAPL_2020.pdf"), "what is competitors strategy ?"],
[os.path.abspath("NASDAQ_AAPL_2020.pdf"), "who is the chief executive officer ?"],
[os.path.abspath("NASDAQ_MSFT_2020.pdf"), "How much is the guided revenue for next quarter?"],
[os.path.abspath("example_file.pdf"), "what are the name of all authors?"],
[os.path.abspath("breast cancer.pdf"), "What Causes Breast Cancer?"],
[os.path.abspath("v61n3a11.pdf"), "what is MDT?"]
]
# 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= [gr.inputs.File(label="input pdf file"), gr.inputs.Textbox(label="Question:")],
outputs =gr.outputs.Textbox(label="Chatbot Response"),
examples = examples,
)
# Run the interface
interface.launch(enable_queue = True) |