diff --git a/app.py b/app.py index 01c788c..07e232d 100644 --- a/app.py +++ b/app.py @@ -6,7 +6,6 @@ import re import time import torch import spaces -import re import ast import html import random @@ -18,18 +17,13 @@ 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): @@ -46,16 +40,13 @@ def normalize_values(text, target_max=500): 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" + # _attn_implementation="flash_attention_2" ).to("cuda") -def model_inference( - input_dict, history -): +def model_inference(input_dict, history): text = input_dict["text"] print(input_dict["files"]) if len(input_dict["files"]) > 1: @@ -63,33 +54,28 @@ def model_inference( 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).") + gr.Error("Please input a text query along with 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} - ] - } - ] + { + "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') @@ -116,12 +102,11 @@ def model_inference( buffer += html.escape(new_text) yield buffer - # After finishing the streamer loop: cleaned_output = full_output.replace("", "").strip() if cleaned_output: yield cleaned_output - # Now, since cleaned_output exists, we can safely use it. + if any(tag in cleaned_output for tag in ["", "", "", "", ""]): doctag_output = cleaned_output if "" in doctag_output: @@ -133,34 +118,27 @@ def model_inference( 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 +) - 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,server_name="0.0.0.0", server_port=7860) \ No newline at end of file +demo.launch(debug=True, server_name="0.0.0.0", server_port=7860)