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 import cv2 import requests from transformers import ( Qwen2VLForConditionalGeneration, Qwen2_5_VLForConditionalGeneration, AutoProcessor, TextIteratorStreamer, AutoModel, AutoTokenizer, ) from transformers.image_utils import load_image 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 = 2048 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 Cosmos-Reason1-7B MODEL_ID_M = "nvidia/Cosmos-Reason1-7B" 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 DocScope MODEL_ID_X = "prithivMLmods/docscopeOCR-7B-050425-exp" processor_x = AutoProcessor.from_pretrained(MODEL_ID_X, trust_remote_code=True) model_x = Qwen2_5_VLForConditionalGeneration.from_pretrained( MODEL_ID_X, trust_remote_code=True, torch_dtype=torch.float16 ).to(device).eval() # Load Relaxed MODEL_ID_Z = "Ertugrul/Qwen2.5-VL-7B-Captioner-Relaxed" processor_z = AutoProcessor.from_pretrained(MODEL_ID_Z, trust_remote_code=True) model_z = Qwen2_5_VLForConditionalGeneration.from_pretrained( MODEL_ID_Z, trust_remote_code=True, torch_dtype=torch.float16 ).to(device).eval() # Load visionOCR MODEL_ID_V = "prithivMLmods/visionOCR-3B-061125" processor_v = AutoProcessor.from_pretrained(MODEL_ID_V, trust_remote_code=True) model_v = Qwen2_5_VLForConditionalGeneration.from_pretrained( MODEL_ID_V, 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 == "Cosmos-Reason1-7B": processor, model = processor_m, model_m elif model_name == "docscopeOCR-7B-050425-exp": processor, model = processor_x, model_x elif model_name == "Captioner-7B-Qwen2.5VL": processor, model = processor_z, model_z elif model_name == "visionOCR-3B": processor, model = processor_v, model_v 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 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 == "Cosmos-Reason1-7B": processor, model = processor_m, model_m elif model_name == "docscopeOCR-7B-050425-exp": processor, model = processor_x, model_x elif model_name == "Captioner-7B-Qwen2.5VL": processor, model = processor_z, model_z elif model_name == "visionOCR-3B": processor, model = processor_v, model_v 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 time.sleep(0.01) yield buffer, buffer # Define examples for image and video inference image_examples = [ ["Perform OCR on the text in the image.", "images/1.jpg"], ["Explain the scene in detail.", "images/2.jpg"] ] video_examples = [ ["Explain the Ad in Detail", "videos/1.mp4"], ["Identify the main actions in the video", "videos/2.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("# **DocScope R1**", 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") raw_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() model_choice = gr.Radio( choices=["Cosmos-Reason1-7B", "docscopeOCR-7B-050425-exp", "Captioner-7B-Qwen2.5VL", "visionOCR-3B"], label="Select Model", value="Cosmos-Reason1-7B" ) 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, 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=[raw_output, markdown_output] ) if __name__ == "__main__": demo.queue(max_size=30).launch(mcp_server=True, ssr_mode=False, show_error=True)