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edf4939 48b201c e4e2cb3 edf4939 48b201c edf4939 e4e2cb3 edf4939 e4e2cb3 edf4939 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 | import gradio as gr
import requests
import torch
from PIL import Image
from transformers import MllamaForConditionalGeneration, AutoProcessor
from PyPDF2 import PdfReader
import tempfile
import os
from pdf2image import convert_from_path
token= os.getenv("HF_TOKEN")
# Model and processor setup
model_id = "meta-llama/Llama-3.2-11B-Vision-Instruct"
# Load the model
model = MllamaForConditionalGeneration.from_pretrained(
model_id,
use_auth_token=token,
torch_dtype=torch.bfloat16,
device_map="auto", # Automatically allocates the model across available devices
)
processor = AutoProcessor.from_pretrained(model_id)
def process_pdf(pdf_file):
"""Extract text from each page of a PDF."""
# Read the PDF using pdf2image to convert pages to images
images = convert_from_path(pdf_file.name)
extracted_text = {}
for i, page_image in enumerate(images):
# Define the instruction for OCR
messages = [
{"role": "user", "content": [
{"type": "image"},
{"type": "text", "text": "Extract all the text from this image:"}
]}
]
# Prepare the input
input_text = processor.apply_chat_template(messages, add_generation_prompt=True)
inputs = processor(
page_image,
input_text,
add_special_tokens=False,
return_tensors="pt"
).to(model.device)
# Generate the output
output = model.generate(**inputs, max_new_tokens=1500)
# Decode the generated text
page_text = processor.decode(output[0])
extracted_text[f"Page {i + 1}"] = page_text
return extracted_text
def display_results(pdf_file):
"""Process the PDF and display results as key-value pairs with checkboxes."""
extracted_text = process_pdf(pdf_file)
checkboxes = {key: False for key in extracted_text.keys()}
return checkboxes, extracted_text
def create_interface():
"""Build the Gradio interface."""
with gr.Blocks() as app:
gr.Markdown("# PDF OCR Extractor with Key-Value Pairs")
with gr.Row():
pdf_input = gr.File(label="Upload PDF", file_types=[".pdf"])
submit_button = gr.Button("Extract Text")
with gr.Row():
checkboxes_output = gr.CheckboxGroup(label="Select Pages", choices=[])
text_output = gr.Textbox(label="Extracted Text", lines=10, interactive=False)
submit_button.click(
display_results,
inputs=[pdf_input],
outputs=[checkboxes_output, text_output]
)
return app
if __name__ == "__main__":
interface = create_interface()
interface.launch()
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