152 lines
6.2 KiB
Python
152 lines
6.2 KiB
Python
import gradio as gr
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from transformers import AutoProcessor, AutoModelForVision2Seq, TextIteratorStreamer
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from transformers.image_utils import load_image
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from threading import Thread
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import re
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import time
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import torch
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import spaces
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import re
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import ast
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import html
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import random
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from PIL import Image, ImageOps
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from docling_core.types.doc import DoclingDocument
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from docling_core.types.doc.document import DocTagsDocument
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def add_random_padding(image, min_percent=0.1, max_percent=0.10):
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image = image.convert("RGB")
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width, height = image.size
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pad_w_percent = random.uniform(min_percent, max_percent)
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pad_h_percent = random.uniform(min_percent, max_percent)
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pad_w = int(width * pad_w_percent)
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pad_h = int(height * pad_h_percent)
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corner_pixel = image.getpixel((0, 0)) # Top-left corner
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padded_image = ImageOps.expand(image, border=(pad_w, pad_h, pad_w, pad_h), fill=corner_pixel)
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return padded_image
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def normalize_values(text, target_max=500):
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def normalize_list(values):
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max_value = max(values) if values else 1
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return [round((v / max_value) * target_max) for v in values]
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def process_match(match):
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num_list = ast.literal_eval(match.group(0))
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normalized = normalize_list(num_list)
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return "".join([f"<loc_{num}>" for num in normalized])
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pattern = r"\[([\d\.\s,]+)\]"
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normalized_text = re.sub(pattern, process_match, text)
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return normalized_text
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processor = AutoProcessor.from_pretrained("ds4sd/SmolDocling-256M-preview")
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model = AutoModelForVision2Seq.from_pretrained("ds4sd/SmolDocling-256M-preview",
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torch_dtype=torch.bfloat16,
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#_attn_implementation="flash_attention_2"
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).to("cuda")
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@spaces.GPU
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def model_inference(
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input_dict, history
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):
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text = input_dict["text"]
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print(input_dict["files"])
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if len(input_dict["files"]) > 1:
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if "OTSL" in text or "code" in text:
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images = [add_random_padding(load_image(image)) for image in input_dict["files"]]
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else:
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images = [load_image(image) for image in input_dict["files"]]
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elif len(input_dict["files"]) == 1:
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if "OTSL" in text or "code" in text:
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images = [add_random_padding(load_image(input_dict["files"][0]))]
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else:
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images = [load_image(input_dict["files"][0])]
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else:
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images = []
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if text == "" and not images:
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gr.Error("Please input a query and optionally image(s).")
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if text == "" and images:
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gr.Error("Please input a text query along the image(s).")
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if "OCR at text at" in text or "Identify element" in text or "formula" in text:
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text = normalize_values(text, target_max=500)
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resulting_messages = [
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{
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"role": "user",
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"content": [{"type": "image"} for _ in range(len(images))] + [
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{"type": "text", "text": text}
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]
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}
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]
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prompt = processor.apply_chat_template(resulting_messages, add_generation_prompt=True)
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inputs = processor(text=prompt, images=[images], return_tensors="pt").to('cuda')
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generation_args = {
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"input_ids": inputs.input_ids,
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"pixel_values": inputs.pixel_values,
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"attention_mask": inputs.attention_mask,
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"num_return_sequences": 1,
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"no_repeat_ngram_size": 10,
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"max_new_tokens": 8192,
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}
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streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=False)
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generation_args = dict(inputs, streamer=streamer, max_new_tokens=8192)
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thread = Thread(target=model.generate, kwargs=generation_args)
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thread.start()
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yield "..."
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buffer = ""
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doctag_output = ""
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for new_text in streamer:
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if new_text != "<end_of_utterance>":
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buffer += html.escape(new_text)
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doctag_output += new_text
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yield buffer
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if any(tag in doctag_output for tag in ["<doctag>", "<otsl>", "<code>", "<formula>", "<chart>"]):
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# final_output = buffer
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# cleaned_output = final_output[len(inputs.input_ids):] if len(final_output) > prompt_length else final_output
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doc = DoclingDocument(name="Document")
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if "<chart>" in doctag_output:
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doctag_output = doctag_output.replace("<chart>", "<otsl>").replace("</chart>", "</otsl>")
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doctag_output = re.sub(r'(<loc_500>)(?!.*<loc_500>)<[^>]+>', r'\1', doctag_output)
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doctags_doc = DocTagsDocument.from_doctags_and_image_pairs([doctag_output], images)
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doc.load_from_doctags(doctags_doc)
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yield f"**MD Output:**\n\n{doc.export_to_markdown()}"
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examples=[[{"text": "Convert this page to docling.", "files": ["example_images/2d0fbcc50e88065a040a537b717620e964fb4453314b71d83f3ed3425addcef6.png"]}],
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[{"text": "Convert this table to OTSL.", "files": ["example_images/image-2.jpg"]}],
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[{"text": "Convert code to text.", "files": ["example_images/7666.jpg"]}],
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[{"text": "Convert formula to latex.", "files": ["example_images/2433.jpg"]}],
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[{"text": "Convert chart to OTSL.", "files": ["example_images/06236926002285.png"]}],
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[{"text": "OCR the text in location [47, 531, 167, 565]", "files": ["example_images/s2w_example.png"]}],
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[{"text": "Extract all section header elements on the page.", "files": ["example_images/paper_3.png"]}],
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[{"text": "Identify element at location [123, 413, 1059, 1061]", "files": ["example_images/redhat.png"]}],
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[{"text": "Convert this page to docling.", "files": ["example_images/gazette_de_france.jpg"]}],
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]
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demo = gr.ChatInterface(fn=model_inference, title="SmolDocling-256M: Ultra-compact VLM for Document Conversion 💫",
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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.",
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examples=examples,
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textbox=gr.MultimodalTextbox(label="Query Input", file_types=["image"], file_count="multiple"), stop_btn="Stop Generation", multimodal=True,
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cache_examples=False
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)
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demo.launch(debug=True, share=True) |