import os import random import uuid import json import requests 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 import cv2 from transformers import ( Qwen2_5_VLForConditionalGeneration, Qwen2VLForConditionalGeneration, AutoProcessor, AutoTokenizer, TextIteratorStreamer, ) from gradio.themes import Soft from gradio.themes.utils import colors, fonts, sizes # --- 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_800)", button_primary_background_fill_hover_dark="linear-gradient(90deg, *secondary_500, *secondary_500)", 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() # Constants for text generation MAX_MAX_NEW_TOKENS = 4096 DEFAULT_MAX_NEW_TOKENS = 1024 MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096")) device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") # Load docscopeOCR-7B-050425-exp MODEL_ID_M = "prithivMLmods/docscopeOCR-7B-050425-exp" processor_m = AutoProcessor.from_pretrained(MODEL_ID_M, trust_remote_code=True) model_m = Qwen2_5_VLForConditionalGeneration.from_pretrained( MODEL_ID_M, trust_remote_code=True, torch_dtype=torch.float16 ).to(device).eval() # Load coreOCR-7B-050325-preview MODEL_ID_X = "prithivMLmods/coreOCR-7B-050325-preview" processor_x = AutoProcessor.from_pretrained(MODEL_ID_X, trust_remote_code=True) model_x = Qwen2VLForConditionalGeneration.from_pretrained( MODEL_ID_X, trust_remote_code=True, torch_dtype=torch.float16 ).to(device).eval() #-----------------------------subfolder-----------------------------# # Load MonkeyOCR MODEL_ID_G = "echo840/MonkeyOCR" SUBFOLDER = "Recognition" processor_g = AutoProcessor.from_pretrained( MODEL_ID_G, trust_remote_code=True, subfolder=SUBFOLDER ) model_g = Qwen2_5_VLForConditionalGeneration.from_pretrained( MODEL_ID_G, trust_remote_code=True, subfolder=SUBFOLDER, torch_dtype=torch.float16 ).to(device).eval() #-----------------------------subfolder-----------------------------# # Load Camel-Doc-OCR-080125 MODEL_ID_O = "prithivMLmods/Camel-Doc-OCR-080125" processor_o = AutoProcessor.from_pretrained(MODEL_ID_O, trust_remote_code=True) model_o = Qwen2_5_VLForConditionalGeneration.from_pretrained( MODEL_ID_O, trust_remote_code=True, torch_dtype=torch.float16 ).to(device).eval() def downsample_video(video_path): """ Downsamples the video to evenly spaced frames. Each frame is returned as a PIL image along with its timestamp. """ vidcap = cv2.VideoCapture(video_path) total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT)) fps = vidcap.get(cv2.CAP_PROP_FPS) frames = [] frame_indices = np.linspace(0, total_frames - 1, min(total_frames, 10), dtype=int) for i in frame_indices: vidcap.set(cv2.CAP_PROP_POS_FRAMES, i) success, image = vidcap.read() if success: image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) pil_image = Image.fromarray(image) timestamp = round(i / fps, 2) frames.append((pil_image, timestamp)) vidcap.release() return frames @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): """ Generates responses using the selected model for image input. Yields raw text and Markdown-formatted text. """ if model_name == "docscopeOCR-7B-050425-exp": processor, model = processor_m, model_m elif model_name == "coreOCR-7B-050325-preview": processor, model = processor_x, model_x elif model_name == "MonkeyOCR-Recognition": processor, model = processor_g, model_g elif model_name == "Camel-Doc-OCR-080125(v2)": processor, model = processor_o, model_o else: yield "Invalid model selected.", "Invalid model selected." return if image is None: yield "Please upload an image.", "Please upload an image." return messages = [{ "role": "user", "content": [ {"type": "image"}, {"type": "text", "text": text}, ] }] prompt_full = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = processor( text=[prompt_full], images=[image], return_tensors="pt", padding=True, truncation=True, max_length=MAX_INPUT_TOKEN_LENGTH ).to(device) streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True) generation_kwargs = {**inputs, "streamer": streamer, "max_new_tokens": max_new_tokens} thread = Thread(target=model.generate, kwargs=generation_kwargs) thread.start() buffer = "" for new_text in streamer: buffer += new_text buffer = buffer.replace("<|im_end|>", "") time.sleep(0.01) yield buffer, buffer @spaces.GPU def generate_video(model_name: str, text: str, video_path: str, max_new_tokens: int = 1024, temperature: float = 0.6, top_p: float = 0.9, top_k: int = 50, repetition_penalty: float = 1.2): """ Generates responses using the selected model for video input. Yields raw text and Markdown-formatted text. """ if model_name == "docscopeOCR-7B-050425-exp": processor, model = processor_m, model_m elif model_name == "coreOCR-7B-050325-preview": processor, model = processor_x, model_x elif model_name == "MonkeyOCR-Recognition": processor, model = processor_g, model_g elif model_name == "Camel-Doc-OCR-080125(v2)": processor, model = processor_o, model_o else: yield "Invalid model selected.", "Invalid model selected." return if video_path is None: yield "Please upload a video.", "Please upload a video." return frames = downsample_video(video_path) messages = [ {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]}, {"role": "user", "content": [{"type": "text", "text": text}]} ] for frame in frames: image, timestamp = frame messages[1]["content"].append({"type": "text", "text": f"Frame {timestamp}:"}) messages[1]["content"].append({"type": "image", "image": image}) inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt", truncation=True, max_length=MAX_INPUT_TOKEN_LENGTH ).to(device) streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True) generation_kwargs = { **inputs, "streamer": streamer, "max_new_tokens": max_new_tokens, "do_sample": True, "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 buffer = buffer.replace("<|im_end|>", "") time.sleep(0.01) yield buffer, buffer # Define examples for image and video inference image_examples = [ ["Reconstruct the content [table] as it is.", "images/doc.jpg"], ["Reconstruct the doc [table] as it is.", "images/zh.png"], ["Explain the doc[table] in detail.", "images/0.png"], ["Fill the correct numbers", "images/image3.png"], ["Explain the scene", "images/image2.jpg"], ["OCR the image", "images/image1.png"] ] video_examples = [ ["Explain the video in detail", "videos/2.mp4"], ["Explain the video in detail", "videos/1.mp4"] ] css = """ #main-title h1 { font-size: 2.3em !important; } #output-title h2 { font-size: 2.1em !important; } """ # Create the Gradio Interface with gr.Blocks(css=css, theme=steel_blue_theme) as demo: gr.Markdown("# **core OCR**", elem_id="main-title") with gr.Row(): with gr.Column(scale=2): with gr.Tabs(): with gr.TabItem("Image Inference"): 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.TabItem("Video Inference"): video_query = gr.Textbox(label="Query Input", placeholder="Enter your query here...") video_upload = gr.Video(label="Upload Video", height=290) video_submit = gr.Button("Submit", variant="primary") gr.Examples(examples=video_examples, inputs=[video_query, video_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") output = gr.Textbox(label="Raw Output Stream", interactive=False, lines=11, show_copy_button=True) with gr.Accordion("(Result.md)", open=False): markdown_output = gr.Markdown(label="(Result.Md)") model_choice = gr.Radio( choices=["Camel-Doc-OCR-080125(v2)", "docscopeOCR-7B-050425-exp", "MonkeyOCR-Recognition", "coreOCR-7B-050325-preview"], label="Select Model", value="Camel-Doc-OCR-080125(v2)" ) image_submit.click( fn=generate_image, inputs=[model_choice, image_query, image_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty], outputs=[output, markdown_output] ) video_submit.click( fn=generate_video, inputs=[model_choice, video_query, video_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty], outputs=[output, markdown_output] ) if __name__ == "__main__": demo.queue(max_size=50).launch(mcp_server=True, ssr_mode=False, show_error=True)