import os import time import threading 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, Glm4vForConditionalGeneration, AutoProcessor, TextIteratorStreamer, ) from qwen_vl_utils import process_vision_info # Constants for text generation MAX_MAX_NEW_TOKENS = 4096 DEFAULT_MAX_NEW_TOKENS = 3584 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 Camel-Doc-OCR-062825 MODEL_ID_M = "prithivMLmods/Camel-Doc-OCR-062825" 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 Qwen2.5-VL-3B-Instruct-abliterated MODEL_ID_X = "huihui-ai/Qwen2.5-VL-3B-Instruct-abliterated" 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 Megalodon-OCR-Sync-0713 MODEL_ID_T = "prithivMLmods/Megalodon-OCR-Sync-0713" processor_t = AutoProcessor.from_pretrained(MODEL_ID_T, trust_remote_code=True) model_t = Qwen2_5_VLForConditionalGeneration.from_pretrained( MODEL_ID_T, trust_remote_code=True, torch_dtype=torch.float16 ).to(device).eval() # Load GLM-4.1V-9B-Thinking MODEL_ID_S = "zai-org/GLM-4.1V-9B-Thinking" processor_s = AutoProcessor.from_pretrained(MODEL_ID_S, trust_remote_code=True) model_s = Glm4vForConditionalGeneration.from_pretrained( MODEL_ID_S, trust_remote_code=True, torch_dtype=torch.float16 ).to(device).eval() # Load DeepEyes-7B MODEL_ID_Y = "ChenShawn/DeepEyes-7B" processor_y = AutoProcessor.from_pretrained(MODEL_ID_Y, trust_remote_code=True) model_y = Qwen2_5_VLForConditionalGeneration.from_pretrained( MODEL_ID_Y, trust_remote_code=True, torch_dtype=torch.float16 ).to(device).eval() def downsample_video(video_path): """ Downsample a video to evenly spaced frames, returning each as a PIL image 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, 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): """ Generate responses using the selected model for image input. """ if model_name == "Camel-Doc-OCR-062825": processor = processor_m model = model_m elif model_name == "Megalodon-OCR-Sync-0713": processor = processor_t model = model_t elif model_name == "GLM-4.1V-9B-Thinking": processor = processor_s model = model_s elif model_name == "DeepEyes-7B-Thinking": processor = processor_y model = model_y elif model_name == "Qwen2.5-VL-3B-Instruct-abliterated": processor = processor_x model = model_x 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", "image": 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=False, 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 = threading.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): """ Generate responses using the selected model for video input. """ if model_name == "Camel-Doc-OCR-062825": processor = processor_m model = model_m elif model_name == "Megalodon-OCR-Sync-0713": processor = processor_t model = model_t elif model_name == "GLM-4.1V-9B-Thinking": processor = processor_s model = model_s elif model_name == "DeepEyes-7B-Thinking": processor = processor_y model = model_y elif model_name == "Qwen2.5-VL-3B-Instruct-abliterated": processor = processor_x model = model_x 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=False, 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 = threading.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 = [ ["explain the movie shot in detail.", "images/5.jpg"], ["convert this page to doc [text] precisely for markdown.", "images/1.png"], ["convert this page to doc [table] precisely for markdown.", "images/2.png"], ["explain the movie shot in detail.", "images/3.png"], ["fill the correct numbers.", "images/4.png"] ] video_examples = [ ["explain the video in detail.", "videos/b.mp4"], ["explain the ad video in detail.", "videos/a.mp4"] ] # Updated CSS with model choice highlighting css = """ .submit-btn { background-color: #2980b9 !important; color: white !important; } .submit-btn:hover { background-color: #3498db !important; } .canvas-output { border: 2px solid #4682B4; border-radius: 10px; padding: 20px; } """ # Create the Gradio Interface with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo: gr.Markdown("# **[Multimodal VLM OCR](https://huggingface.co/collections/prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0)**") with gr.Row(): with gr.Column(): 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="Image") image_submit = gr.Button("Submit", elem_classes="submit-btn") 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="Video") video_submit = gr.Button("Submit", elem_classes="submit-btn") 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(): with gr.Column(elem_classes="canvas-output"): gr.Markdown("## Output") output = gr.Textbox(label="Raw Output Stream", interactive=False, lines=2) with gr.Accordion("(Result.md)", open=False): markdown_output = gr.Markdown(label="(Result.md)") model_choice = gr.Radio( choices=["Camel-Doc-OCR-062825", "GLM-4.1V-9B-Thinking", "Megalodon-OCR-Sync-0713", "DeepEyes-7B-Thinking", "Qwen2.5-VL-3B-Instruct-abliterated"], label="Select Model", value="Camel-Doc-OCR-062825" ) gr.Markdown("**Model Info đŸ’»** | [Report Bug](https://huggingface.co/spaces/prithivMLmods/Multimodal-OCR-Comparator/discussions)") gr.Markdown("> Camel-Doc-OCR-062825 and Megalodon-OCR-Sync-0713 are both fine-tuned versions of the Qwen2.5-VL series focused on document retrieval, content extraction, analysis recognition, and excelling in OCR and visual document analysis tasks for structured and unstructured content—Camel-Doc-OCR-062825 leveraging the Qwen2.5-VL-7B-Instruct as its base, while Megalodon-OCR-Sync-0713 uses Qwen2.5-VL-3B-Instruct and is especially trained on diverse captioning datasets.") gr.Markdown("> GLM-4.1V-9B-Thinking is a vision-language model (VLM) based on the GLM-4-9B-0414 foundation, with a strong emphasis on advanced reasoning capabilities, chain-of-thought inference, and robust bilingual (Chinese/English) performance on complex multimodal benchmarks.") gr.Markdown("> DeepEyes-7B stands out for its agentic reinforcement learning approach, focusing on thinking with images for better visual reasoning, math problem-solving, and mitigating hallucination using Qwen2.5-VL-7B-Instruct as its foundation. Finally, Qwen2.5-VL-3B-Instruct-abliterated is part of the Qwen2.5-VL family, known for its versatile vision-language understanding and generation, serving as the foundational architecture for several of these fine-tuned vision-language and OCR models.") # Define the submit button actions 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=30).launch(share=True, mcp_server=True, ssr_mode=False, show_error=True)