{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [], "gpuType": "T4" }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "id": "xL8y37Y6bORU" }, "outputs": [], "source": [ "%%capture\n", "!pip install gradio spaces transformers accelerate numpy requests\n", "!pip install torch torchvision qwen-vl-utils av hf_xet\n", "!pip install pillow huggingface_hub opencv-python" ] }, { "cell_type": "code", "source": [ "import os\n", "import time\n", "import numpy as np\n", "from threading import Thread\n", "\n", "import gradio as gr\n", "import spaces\n", "import torch\n", "from PIL import Image\n", "import cv2\n", "\n", "from transformers import (\n", " Qwen2VLForConditionalGeneration,\n", " AutoProcessor,\n", " TextIteratorStreamer,\n", ")\n", "\n", "# Constants for text generation\n", "MAX_MAX_NEW_TOKENS = 2048\n", "DEFAULT_MAX_NEW_TOKENS = 1024\n", "# Increase or disable input truncation to avoid token mismatches\n", "MAX_INPUT_TOKEN_LENGTH = int(os.getenv(\"MAX_INPUT_TOKEN_LENGTH\", \"8192\"))\n", "\n", "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", "\n", "MODEL_ID = \"prithivMLmods/Qwen2-VL-OCR-2B-Instruct\"\n", "processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)\n", "model_m = Qwen2VLForConditionalGeneration.from_pretrained(\n", " MODEL_ID,\n", " trust_remote_code=True,\n", " torch_dtype=torch.float16\n", ").to(device).eval()\n", "\n", "def downsample_video(video_path):\n", " \"\"\"\n", " Downsamples the video to evenly spaced frames.\n", " Each frame is returned as a PIL image along with its timestamp.\n", " \"\"\"\n", " vidcap = cv2.VideoCapture(video_path)\n", " total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))\n", " fps = vidcap.get(cv2.CAP_PROP_FPS)\n", " frames = []\n", " frame_indices = np.linspace(0, total_frames - 1, 10, dtype=int)\n", " for i in frame_indices:\n", " vidcap.set(cv2.CAP_PROP_POS_FRAMES, i)\n", " success, image = vidcap.read()\n", " if success:\n", " image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n", " pil_image = Image.fromarray(image)\n", " timestamp = round(i / fps, 2)\n", " frames.append((pil_image, timestamp))\n", " vidcap.release()\n", " return frames\n", "\n", "@spaces.GPU\n", "def generate_image(text: str, image: Image.Image,\n", " max_new_tokens: int = DEFAULT_MAX_NEW_TOKENS,\n", " temperature: float = 0.6,\n", " top_p: float = 0.9,\n", " top_k: int = 50,\n", " repetition_penalty: float = 1.2):\n", "\n", " if image is None:\n", " yield \"Please upload an image.\"\n", " return\n", "\n", " messages = [{\n", " \"role\": \"user\",\n", " \"content\": [\n", " {\"type\": \"image\", \"image\": image},\n", " {\"type\": \"text\", \"text\": text},\n", " ]\n", " }]\n", " prompt_full = processor.apply_chat_template(\n", " messages, tokenize=False, add_generation_prompt=True\n", " )\n", " inputs = processor(\n", " text=[prompt_full],\n", " images=[image],\n", " return_tensors=\"pt\",\n", " padding=True,\n", " truncation=False # Disable truncation to keep image tokens intact\n", " ).to(device)\n", "\n", " streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)\n", " generation_kwargs = {\n", " **inputs,\n", " \"streamer\": streamer,\n", " \"max_new_tokens\": max_new_tokens,\n", " \"do_sample\": True,\n", " \"temperature\": temperature,\n", " \"top_p\": top_p,\n", " \"top_k\": top_k,\n", " \"repetition_penalty\": repetition_penalty,\n", " }\n", " thread = Thread(target=model_m.generate, kwargs=generation_kwargs)\n", " thread.start()\n", " buffer = \"\"\n", " for new_text in streamer:\n", " buffer += new_text.replace(\"<|im_end|>\", \"\")\n", " time.sleep(0.01)\n", " yield buffer\n", "\n", "@spaces.GPU\n", "def generate_video(text: str, video_path: str,\n", " max_new_tokens: int = DEFAULT_MAX_NEW_TOKENS,\n", " temperature: float = 0.6,\n", " top_p: float = 0.9,\n", " top_k: int = 50,\n", " repetition_penalty: float = 1.2):\n", "\n", " if video_path is None:\n", " yield \"Please upload a video.\"\n", " return\n", "\n", " frames = downsample_video(video_path)\n", " messages = [\n", " {\"role\": \"system\", \"content\": [{\"type\": \"text\", \"text\": \"You are a helpful assistant.\"}]},\n", " {\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": text}]}\n", " ]\n", " for image, timestamp in frames:\n", " messages[1][\"content\"].extend([\n", " {\"type\": \"text\", \"text\": f\"Frame {timestamp}:\"},\n", " {\"type\": \"image\", \"image\": image}\n", " ])\n", "\n", " # Use chat template with no truncation\n", " inputs = processor.apply_chat_template(\n", " messages,\n", " tokenize=True,\n", " add_generation_prompt=True,\n", " return_dict=True,\n", " return_tensors=\"pt\",\n", " truncation=False\n", " ).to(device)\n", "\n", " streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)\n", " generation_kwargs = {\n", " **inputs,\n", " \"streamer\": streamer,\n", " \"max_new_tokens\": max_new_tokens,\n", " \"do_sample\": True,\n", " \"temperature\": temperature,\n", " \"top_p\": top_p,\n", " \"top_k\": top_k,\n", " \"repetition_penalty\": repetition_penalty,\n", " }\n", " thread = Thread(target=model_m.generate, kwargs=generation_kwargs)\n", " thread.start()\n", " buffer = \"\"\n", " for new_text in streamer:\n", " buffer += new_text.replace(\"<|im_end|>\", \"\")\n", " time.sleep(0.01)\n", " yield buffer\n", "\n", "# Gradio App Style and Layout\n", "css = \"\"\"\n", ".submit-btn {\n", " background-color: #2980b9 !important;\n", " color: white !important;\n", "}\n", ".submit-btn:hover {\n", " background-color: #3498db !important;\n", "}\n", "\"\"\"\n", "\n", "with gr.Blocks(css=css, theme=\"bethecloud/storj_theme\") as demo:\n", " gr.Markdown(\"# **prithivMLmods/Qwen2-VL-OCR-2B-Instruct**\")\n", " with gr.Row():\n", " with gr.Column():\n", " with gr.Tabs():\n", " with gr.TabItem(\"Image Inference\"):\n", " image_query = gr.Textbox(label=\"Query Input\", placeholder=\"Enter your query here...\")\n", " image_upload = gr.Image(type=\"pil\", label=\"Image\")\n", " image_submit = gr.Button(\"Submit\", elem_classes=\"submit-btn\")\n", "\n", " with gr.TabItem(\"Video Inference\"):\n", " video_query = gr.Textbox(label=\"Query Input\", placeholder=\"Enter your query here...\")\n", " video_upload = gr.Video(label=\"Video\")\n", " video_submit = gr.Button(\"Submit\", elem_classes=\"submit-btn\")\n", "\n", " with gr.Accordion(\"Advanced options\", open=False):\n", " max_new_tokens = gr.Slider(label=\"Max new tokens\", minimum=1, maximum=MAX_MAX_NEW_TOKENS, step=1, value=DEFAULT_MAX_NEW_TOKENS)\n", " temperature = gr.Slider(label=\"Temperature\", minimum=0.1, maximum=4.0, step=0.1, value=0.6)\n", " top_p = gr.Slider(label=\"Top-p (nucleus sampling)\", minimum=0.05, maximum=1.0, step=0.05, value=0.9)\n", " top_k = gr.Slider(label=\"Top-k\", minimum=1, maximum=1000, step=1, value=50)\n", " repetition_penalty = gr.Slider(label=\"Repetition penalty\", minimum=1.0, maximum=2.0, step=0.05, value=1.2)\n", " with gr.Column():\n", " output = gr.Textbox(label=\"Output\", interactive=False)\n", "\n", " image_submit.click(\n", " fn=generate_image,\n", " inputs=[image_query, image_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],\n", " outputs=output\n", " )\n", " video_submit.click(\n", " fn=generate_video,\n", " inputs=[video_query, video_upload, max_new_tokens, temperature, top_p, top_k, repetition_penalty],\n", " outputs=output\n", " )\n", "\n", "if __name__ == \"__main__\":\n", " demo.queue(max_size=30).launch(share=True, ssr_mode=False, show_error=True)\n" ], "metadata": { "id": "Y-NTbL1tdL9X" }, "execution_count": null, "outputs": [] } ] }