import os import torch from transformers import AutoProcessor, PaliGemmaForConditionalGeneration from PIL import Image import io import re HF_TOKEN = os.environ.get("HF_TOKEN") os.environ['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION'] = 'python' device = "cuda:0" if torch.cuda.is_available() else "cpu" model_id = "google/paligemma-3b-mix-224" model = PaliGemmaForConditionalGeneration.from_pretrained(model_id).to(device) processor = AutoProcessor.from_pretrained(model_id) def extract_text_from_image(image_content): image = Image.open(io.BytesIO(image_content)) prompt = "Extract the following details from this invoice: Invoice Number, Total Amount, Invoice Date." inputs = processor(text=prompt, images=image, return_tensors="pt").to(device) input_len = inputs["input_ids"].shape[-1] with torch.inference_mode(): generation = model.generate(**inputs, max_new_tokens=100, do_sample=False) generation = generation[0][input_len:] decoded = processor.decode(generation, skip_special_tokens=True) return decoded def extract_invoice_details(text): details = {} details['Invoice Number'] = re.search(r'Invoice Number: (\S+)', text).group(1) if re.search(r'Invoice Number: (\S+)', text) else 'N/A' details['Amount'] = re.search(r'Total Amount Due: (\S+)', text).group(1) if re.search(r'Total Amount Due: (\S+)', text) else 'N/A' details['Invoice Date'] = re.search(r'Invoice Date: (\S+)', text).group(1) if re.search(r'Invoice Date: (\S+)', text) else 'N/A' return details