import os import random import uuid import json import time import asyncio from threading import Thread from typing import Iterable import gradio as gr import spaces import torch import numpy as np from PIL import Image, ImageOps import requests from transformers import ( AutoTokenizer, AutoProcessor, TextIteratorStreamer, ) from transformers.image_utils import load_image # The custom model class is imported via trust_remote_code=True from transformers import AutoModelForImageTextToText from gradio.themes import Soft from gradio.themes.utils import colors, fonts, sizes from docling_core.types.doc import DoclingDocument, DocTagsDocument import re import ast import html # --- Theme and CSS Definition --- colors.steel_blue = colors.Color( name="steel_blue", c50="#EBF3F8", c100="#D3E5F0", c200="#A8CCE1", c300="#7DB3D2", c400="#529AC3", c500="#4682B4", # SteelBlue base color c600="#3E72A0", c700="#36638C", c800="#2E5378", c900="#264364", c950="#1E3450", ) class SteelBlueTheme(Soft): def __init__( self, *, primary_hue: colors.Color | str = colors.gray, secondary_hue: colors.Color | str = colors.steel_blue, neutral_hue: colors.Color | str = colors.slate, text_size: sizes.Size | str = sizes.text_lg, font: fonts.Font | str | Iterable[fonts.Font | str] = ( fonts.GoogleFont("Outfit"), "Arial", "sans-serif", ), font_mono: fonts.Font | str | Iterable[fonts.Font | str] = ( fonts.GoogleFont("IBM Plex Mono"), "ui-monospace", "monospace", ), ): super().__init__( primary_hue=primary_hue, secondary_hue=secondary_hue, neutral_hue=neutral_hue, text_size=text_size, font=font, font_mono=font_mono, ) super().set( background_fill_primary="*primary_50", background_fill_primary_dark="*primary_900", body_background_fill="linear-gradient(135deg, *primary_200, *primary_100)", body_background_fill_dark="linear-gradient(135deg, *primary_900, *primary_800)", button_primary_text_color="white", button_primary_text_color_hover="white", button_primary_background_fill="linear-gradient(90deg, *secondary_500, *secondary_600)", button_primary_background_fill_hover="linear-gradient(90deg, *secondary_600, *secondary_700)", button_primary_background_fill_dark="linear-gradient(90deg, *secondary_600, *secondary_700)", button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_600)", slider_color="*secondary_500", slider_color_dark="*secondary_600", block_title_text_weight="600", block_border_width="3px", block_shadow="*shadow_drop_lg", button_primary_shadow="*shadow_drop_lg", button_large_padding="11px", color_accent_soft="*primary_100", block_label_background_fill="*primary_200", ) steel_blue_theme = SteelBlueTheme() css = """ #main-title h1 { font-size: 2.3em !important; } #output-title h2 { font-size: 2.1em !important; } """ # Constants for text generation MAX_MAX_NEW_TOKENS = 5120 DEFAULT_MAX_NEW_TOKENS = 3072 MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096")) # Check for CUDA availability device = "cuda" if torch.cuda.is_available() else "cpu" # Load Nanonets-OCR2-3B MODEL_ID_3B = "nanonets/Nanonets-OCR2-3B" processor_3b = AutoProcessor.from_pretrained(MODEL_ID_3B, trust_remote_code=True) model_3b = AutoModelForImageTextToText.from_pretrained( MODEL_ID_3B, dtype=torch.float16, #device_map="auto", trust_remote_code=True, attn_implementation="flash_attention_2" ).to(device).eval() # Load Nanonets-OCR2-1.5B-exp MODEL_ID_1_5B = "nanonets/Nanonets-OCR2-1.5B-exp" processor_1_5b = AutoProcessor.from_pretrained(MODEL_ID_1_5B, trust_remote_code=True) model_1_5b = AutoModelForImageTextToText.from_pretrained( MODEL_ID_1_5B, dtype=torch.float16, #device_map="auto", trust_remote_code=True, attn_implementation="flash_attention_2" ).to(device).eval() @spaces.GPU def generate_image(model_name: str, text: str, image: Image.Image, max_new_tokens: int = 1024, temperature: float = 0.6, top_p: float = 0.9, top_k: int = 50, repetition_penalty: float = 1.2): """Generation function for image input.""" if model_name == "Nanonets-OCR2-3B": processor, model = processor_3b, model_3b elif model_name == "Nanonets-OCR2-1.5B-exp": processor, model = processor_1_5b, model_1_5b else: yield "Invalid model selected.", "Invalid model selected." return if image is None: yield "Please upload an image.", "Please upload an image." return images = [image] messages = [ { "role": "user", "content": [{"type": "image"}] + [{"type": "text", "text": text}] } ] prompt = processor.apply_chat_template(messages, add_generation_prompt=True) inputs = processor(text=prompt, images=images, return_tensors="pt") streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True) generation_kwargs = { **inputs, "streamer": streamer, "max_new_tokens": max_new_tokens, "temperature": temperature, "top_p": top_p, "top_k": top_k, "repetition_penalty": repetition_penalty, } thread = Thread(target=model.generate, kwargs=generation_kwargs) thread.start() buffer = "" for new_text in streamer: buffer += new_text.replace("<|im_end|>", "") yield buffer, buffer # Define examples for image inference image_examples = [ ["Reconstruct the doc [table] as it is.", "images/0.png"], ["Describe the image!", "images/8.png"], ["OCR the image", "images/2.jpg"], ["Convert this page to docling", "images/1.png"], ["Convert this page to docling", "images/3.png"], ["Convert chart to OTSL.", "images/4.png"], ["Convert code to text", "images/5.jpg"], ["Convert this table to OTSL.", "images/6.jpg"], ["Convert formula to late.", "images/7.jpg"], ] # Create the Gradio Interface with gr.Blocks(css=css, theme=steel_blue_theme) as demo: gr.Markdown("# **Multimodal OCR3**", elem_id="main-title") with gr.Row(): with gr.Column(scale=2): # Image Inference Components image_query = gr.Textbox(label="Query Input", placeholder="Enter your query here...") image_upload = gr.Image(type="pil", label="Upload Image", height=290) image_submit = gr.Button("Submit", variant="primary") gr.Examples(examples=image_examples, inputs=[image_query, image_upload]) with gr.Accordion("Advanced options", open=False): max_new_tokens = gr.Slider(label="Max new tokens", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS) temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=4.0, step=0.1, value=0.6) top_p = gr.Slider(label="Top-p (nucleus sampling)", minimum=0.05, maximum=1.0, step=0.05, value=0.9) top_k = gr.Slider(label="Top-k", minimum=1, maximum=1000, step=1, value=50) repetition_penalty = gr.Slider(label="Repetition penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.2) with gr.Column(scale=3): gr.Markdown("## Output", elem_id="output-title") raw_output = gr.Textbox(label="Raw Output Stream", interactive=False, lines=11, show_copy_button=True) with gr.Accordion("(Result.md)", open=True): formatted_output = gr.Markdown(label="(Result.md)") model_choice = gr.Radio( choices=["Nanonets-OCR2-3B", "Nanonets-OCR2-1.5B-exp"], label="Select Model", value="Nanonets-OCR2-3B" ) image_submit.click( fn=generate_image, inputs=[model_choice, image_query, image_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty], outputs=[raw_output, formatted_output] ) if __name__ == "__main__": demo.queue(max_size=50).launch(ssr_mode=False, show_error=True)