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from pprint import pprint
from getpass import getpass
from PyPDF2 import PdfReader
import gradio as gr
import os
from transformers import pipeline
# Function to read PDF file content directly
def read_pdf(pdf_path):
content = ""
reader = PdfReader(pdf_path)
for page in reader.pages:
content += page.extract_text()
return content
# Process and retrieve answers
def process_invoice(file, hf_token, questions):
# Read the PDF content directly
pdf_content = read_pdf(file.name)
# Initialize the Hugging Face pipeline
qa_pipeline = pipeline("question-answering", model="mistralai/Mixtral-8x7B-Instruct-v0.1", token=hf_token)
answers = {}
for question in questions.split(','):
result = qa_pipeline(question=question.strip(), context=pdf_content)
answers[question] = result['answer']
return answers
# Gradio interface
def gradio_interface(file, hf_token, questions):
answers = process_invoice(file, hf_token, questions)
return answers
interface = gr.Interface(
fn=gradio_interface,
inputs=[
gr.inputs.File(file_count="single", type="file", label="Upload Invoice (PDF)"),
gr.inputs.Textbox(type="password", label="Enter your Hugging Face Token"),
gr.inputs.Textbox(lines=5, placeholder="Enter your questions separated by commas")
],
outputs="json",
title="Invoice Data Extraction",
description="Upload an invoice PDF, provide your Hugging Face token, and get the extracted data based on your questions."
)
if __name__ == "__main__":
interface.launch()
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