{ "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", "
\n", " {label}\n", "
\n", "
\n", "
\n", "
\n", "\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 }