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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "m7rU-pjX3Y1O"
},
"outputs": [],
"source": [
"%%capture\n",
"!pip install gradio transformers accelerate numpy\n",
"!pip install torch torchvision av hf_xet spaces\n",
"!pip install pillow huggingface_hub opencv-python"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "dZUVag_jJMck"
},
"outputs": [],
"source": [
"from huggingface_hub import notebook_login, HfApi\n",
"notebook_login()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kW4MjaOs3c9E"
},
"outputs": [],
"source": [
"import gradio as gr\n",
"from transformers import AutoProcessor, TextIteratorStreamer, AutoModelForImageTextToText\n",
"from transformers.image_utils import load_image\n",
"from threading import Thread\n",
"import time\n",
"import torch\n",
"import spaces\n",
"import cv2\n",
"import numpy as np\n",
"from PIL import Image\n",
"\n",
"# Helper: progress bar HTML\n",
"def progress_bar_html(label: str) -> str:\n",
" return f'''\n",
"<div style=\"display: flex; align-items: center;\">\n",
" <span style=\"margin-right: 10px; font-size: 14px;\">{label}</span>\n",
" <div style=\"width: 110px; height: 5px; background-color: #FFB6C1; border-radius: 2px; overflow: hidden;\">\n",
" <div style=\"width: 100%; height: 100%; background-color: #FF69B4; animation: loading 1.5s linear infinite;\"></div>\n",
" </div>\n",
"</div>\n",
"<style>\n",
"@keyframes loading {{\n",
" 0% {{ transform: translateX(-100%); }}\n",
" 100% {{ transform: translateX(100%); }}\n",
"}}\n",
"</style>\n",
" '''\n",
"\n",
"# Aya Vision 8B setup\n",
"AYA_MODEL_ID = \"CohereForAI/aya-vision-8b\"\n",
"aya_processor = AutoProcessor.from_pretrained(AYA_MODEL_ID)\n",
"aya_model = AutoModelForImageTextToText.from_pretrained(\n",
" AYA_MODEL_ID,\n",
" device_map=\"auto\",\n",
" torch_dtype=torch.float16\n",
")\n",
"\n",
"def downsample_video(video_path, num_frames=10):\n",
" \"\"\"\n",
" Extract evenly spaced frames and timestamps from a video file.\n",
" Returns list of (PIL.Image, timestamp_sec).\n",
" \"\"\"\n",
" vidcap = cv2.VideoCapture(video_path)\n",
" total = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
" fps = vidcap.get(cv2.CAP_PROP_FPS) or 30\n",
" indices = np.linspace(0, total-1, num_frames, dtype=int)\n",
" frames = []\n",
" for idx in indices:\n",
" vidcap.set(cv2.CAP_PROP_POS_FRAMES, int(idx))\n",
" ret, frame = vidcap.read()\n",
" if not ret:\n",
" continue\n",
" frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n",
" pil = Image.fromarray(frame)\n",
" timestamp = round(idx / fps, 2)\n",
" frames.append((pil, timestamp))\n",
" vidcap.release()\n",
" return frames\n",
"\n",
"@spaces.GPU\n",
"def process_image(prompt: str, image: Image.Image):\n",
" if image is None:\n",
" yield \"Error: Please upload an image.\"\n",
" return\n",
" if not prompt.strip():\n",
" yield \"Error: Please provide a prompt with the image.\"\n",
" return\n",
" yield progress_bar_html(\"Processing Image with Aya Vision 8B\")\n",
" messages = [{\"role\": \"user\", \"content\": [\n",
" {\"type\": \"image\", \"image\": image},\n",
" {\"type\": \"text\", \"text\": prompt.strip()}\n",
" ]}]\n",
" inputs = aya_processor.apply_chat_template(\n",
" messages, padding=True, add_generation_prompt=True,\n",
" tokenize=True, return_dict=True, return_tensors=\"pt\"\n",
" ).to(aya_model.device)\n",
" streamer = TextIteratorStreamer(aya_processor, skip_prompt=True, skip_special_tokens=True)\n",
" thread = Thread(target=aya_model.generate, kwargs={**inputs, \"streamer\": streamer, \"max_new_tokens\": 1024, \"do_sample\": True, \"temperature\": 0.3})\n",
" thread.start()\n",
" buff = \"\"\n",
" for chunk in streamer:\n",
" buff += chunk.replace(\"<|im_end|>\", \"\")\n",
" time.sleep(0.01)\n",
" yield buff\n",
"\n",
"@spaces.GPU\n",
"def process_video(prompt: str, video_file: str):\n",
" if video_file is None:\n",
" yield \"Error: Please upload a video.\"\n",
" return\n",
" if not prompt.strip():\n",
" yield \"Error: Please provide a prompt with the video.\"\n",
" return\n",
" yield progress_bar_html(\"Processing Video with Aya Vision 8B\")\n",
" frames = downsample_video(video_file)\n",
" # Build chat messages with each frame and timestamp\n",
" content = [{\"type\": \"text\", \"text\": prompt.strip()}]\n",
" for img, ts in frames:\n",
" content.append({\"type\": \"text\", \"text\": f\"Frame at {ts}s:\"})\n",
" content.append({\"type\": \"image\", \"image\": img})\n",
" messages = [{\"role\": \"user\", \"content\": content}]\n",
" inputs = aya_processor.apply_chat_template(\n",
" messages, tokenize=True, add_generation_prompt=True,\n",
" return_dict=True, return_tensors=\"pt\"\n",
" ).to(aya_model.device)\n",
" streamer = TextIteratorStreamer(aya_processor, skip_prompt=True, skip_special_tokens=True)\n",
" thread = Thread(target=aya_model.generate, kwargs={**inputs, \"streamer\": streamer, \"max_new_tokens\": 1024, \"do_sample\": True, \"temperature\": 0.3})\n",
" thread.start()\n",
" buff = \"\"\n",
" for chunk in streamer:\n",
" buff += chunk.replace(\"<|im_end|>\", \"\")\n",
" time.sleep(0.01)\n",
" yield buff\n",
"\n",
"# Build Gradio UI\n",
"demo = gr.Blocks()\n",
"with demo:\n",
" gr.Markdown(\"# **Aya Vision 8B Multimodal: Image & Video**\")\n",
" with gr.Tabs():\n",
" with gr.TabItem(\"Image Inference\"):\n",
" txt_i = gr.Textbox(label=\"Prompt\", placeholder=\"Enter prompt...\")\n",
" img_u = gr.Image(type=\"filepath\", label=\"Image\")\n",
" btn_i = gr.Button(\"Run Image\")\n",
" out_i = gr.Textbox(label=\"Output\", interactive=False)\n",
" btn_i.click(fn=process_image, inputs=[txt_i, img_u], outputs=out_i)\n",
" with gr.TabItem(\"Video Inference\"):\n",
" txt_v = gr.Textbox(label=\"Prompt\", placeholder=\"Enter prompt...\")\n",
" vid_u = gr.Video(label=\"Video\")\n",
" btn_v = gr.Button(\"Run Video\")\n",
" out_v = gr.Textbox(label=\"Output\", interactive=False)\n",
" btn_v.click(fn=process_video, inputs=[txt_v, vid_u], outputs=out_v)\n",
"\n",
"demo.launch(debug=True, share=True)"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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