app.py

import torch
import base64
import urllib.request
import gradio as gr

from io import BytesIO
from PIL import Image
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration

from olmocr.data.renderpdf import render_pdf_to_base64png
from olmocr.prompts import build_finetuning_prompt
from olmocr.prompts.anchor import get_anchor_text

# Initialize the model
model = Qwen2VLForConditionalGeneration.from_pretrained("allenai/olmOCR-7B-0225-preview", torch_dtype=torch.bfloat16).eval()
processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

# Function to process PDF and generate text
def process_pdf(pdf_file):
    pdf_filename = pdf_file.name
    image_base64 = render_pdf_to_base64png(pdf_filename, 1, target_longest_image_dim=1024)
    anchor_text = get_anchor_text(pdf_filename, 1, pdf_engine="pdfreport", target_length=4000)
    prompt = build_finetuning_prompt(anchor_text)
    
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": prompt},
                {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}},
            ],
        }
    ]
    
    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    main_image = Image.open(BytesIO(base64.b64decode(image_base64)))
    
    inputs = processor(
        text=[text],
        images=[main_image],
        padding=True,
        return_tensors="pt",
    )
    inputs = {key: value.to(device) for (key, value) in inputs.items()}
    
    output = model.generate(
        **inputs,
        temperature=0.8,
        max_new_tokens=1500,
        num_return_sequences=1,
        do_sample=True,
    )
    
    prompt_length = inputs["input_ids"].shape[1]
    new_tokens = output[:, prompt_length:]
    text_output = processor.tokenizer.batch_decode(new_tokens, skip_special_tokens=True)
    
    return text_output[0]

# Create Gradio Interface
iface = gr.Interface(
    fn=process_pdf,
    inputs=gr.File(label="Upload PDF"),
    outputs=gr.Textbox(label="Extracted Text"),
    title="PDF Text Extractor",
    description="Upload a PDF file and extract text using Qwen2-VL-7B-Instruct."
)

# Launch the Gradio app
if __name__ == "__main__":
    iface.launch()