import gradio as gr from transformers import AutoProcessor, AutoModelForVision2Seq, TextIteratorStreamer from transformers.image_utils import load_image from threading import Thread import re import time import torch import spaces import re import ast import html import random from PIL import Image, ImageOps from docling_core.types.doc import DoclingDocument from docling_core.types.doc.document import DocTagsDocument def add_random_padding(image, min_percent=0.1, max_percent=0.10): image = image.convert("RGB") width, height = image.size pad_w_percent = random.uniform(min_percent, max_percent) pad_h_percent = random.uniform(min_percent, max_percent) pad_w = int(width * pad_w_percent) pad_h = int(height * pad_h_percent) corner_pixel = image.getpixel((0, 0)) # Top-left corner padded_image = ImageOps.expand(image, border=(pad_w, pad_h, pad_w, pad_h), fill=corner_pixel) return padded_image def normalize_values(text, target_max=500): def normalize_list(values): max_value = max(values) if values else 1 return [round((v / max_value) * target_max) for v in values] def process_match(match): num_list = ast.literal_eval(match.group(0)) normalized = normalize_list(num_list) return "".join([f"" for num in normalized]) pattern = r"\[([\d\.\s,]+)\]" normalized_text = re.sub(pattern, process_match, text) return normalized_text processor = AutoProcessor.from_pretrained("ds4sd/SmolDocling-256M-preview") model = AutoModelForVision2Seq.from_pretrained("ds4sd/SmolDocling-256M-preview", torch_dtype=torch.bfloat16, #_attn_implementation="flash_attention_2" ).to("cuda") @spaces.GPU def model_inference( input_dict, history ): text = input_dict["text"] print(input_dict["files"]) if len(input_dict["files"]) > 1: if "OTSL" in text or "code" in text: images = [add_random_padding(load_image(image)) for image in input_dict["files"]] else: images = [load_image(image) for image in input_dict["files"]] elif len(input_dict["files"]) == 1: if "OTSL" in text or "code" in text: images = [add_random_padding(load_image(input_dict["files"][0]))] else: images = [load_image(input_dict["files"][0])] else: images = [] if text == "" and not images: gr.Error("Please input a query and optionally image(s).") if text == "" and images: gr.Error("Please input a text query along the image(s).") if "OCR at text at" in text or "Identify element" in text or "formula" in text: text = normalize_values(text, target_max=500) resulting_messages = [ { "role": "user", "content": [{"type": "image"} for _ in range(len(images))] + [ {"type": "text", "text": text} ] } ] prompt = processor.apply_chat_template(resulting_messages, add_generation_prompt=True) inputs = processor(text=prompt, images=[images], return_tensors="pt").to('cuda') generation_args = { "input_ids": inputs.input_ids, "pixel_values": inputs.pixel_values, "attention_mask": inputs.attention_mask, "num_return_sequences": 1, "max_new_tokens": 8192, } streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=False) generation_args = dict(inputs, streamer=streamer, max_new_tokens=8192) thread = Thread(target=model.generate, kwargs=generation_args) thread.start() yield "..." buffer = "" full_output = "" for new_text in streamer: full_output += new_text buffer += html.escape(new_text) yield buffer cleaned_output = full_output.replace("", "").strip() if cleaned_output: doctag_output = cleaned_output yield cleaned_output if any(tag in doctag_output for tag in ["", "", "", "", ""]): doc = DoclingDocument(name="Document") if "" in doctag_output: doctag_output = doctag_output.replace("", "").replace("", "") doctag_output = re.sub(r'()(?!.*)<[^>]+>', r'\1', doctag_output) doctags_doc = DocTagsDocument.from_doctags_and_image_pairs([doctag_output], images) doc.load_from_doctags(doctags_doc) yield f"**MD Output:**\n\n{doc.export_to_markdown()}" examples=[[{"text": "Convert this page to docling.", "files": ["example_images/2d0fbcc50e88065a040a537b717620e964fb4453314b71d83f3ed3425addcef6.png"]}], [{"text": "Convert this table to OTSL.", "files": ["example_images/image-2.jpg"]}], [{"text": "Convert code to text.", "files": ["example_images/7666.jpg"]}], [{"text": "Convert formula to latex.", "files": ["example_images/2433.jpg"]}], [{"text": "Convert chart to OTSL.", "files": ["example_images/06236926002285.png"]}], [{"text": "OCR the text in location [47, 531, 167, 565]", "files": ["example_images/s2w_example.png"]}], [{"text": "Extract all section header elements on the page.", "files": ["example_images/paper_3.png"]}], [{"text": "Identify element at location [123, 413, 1059, 1061]", "files": ["example_images/redhat.png"]}], [{"text": "Convert this page to docling.", "files": ["example_images/gazette_de_france.jpg"]}], ] demo = gr.ChatInterface(fn=model_inference, title="SmolDocling-256M: Ultra-compact VLM for Document Conversion 💫", description="Play with [ds4sd/SmolDocling-256M-preview](https://huggingface.co/ds4sd/SmolDocling-256M-preview) in this demo. To get started, upload an image and text or try one of the examples. This demo doesn't use history for the chat, so every chat you start is a new conversation.", examples=examples, textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image"], file_count="multiple"), stop_btn="Stop Generation", multimodal=True, cache_examples=False ) demo.launch(debug=True)