{
  "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": "m7rU-pjX3Y1O"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install gradio transformers==4.30.2 pillow\n",
        "!pip install torch torchvision hf_xet timm==1.0.10\n",
        "!pip install flash-attn --no-build-isolation"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "import torch\n",
        "from PIL import Image\n",
        "from transformers import AutoProcessor, AutoModelForCausalLM\n",
        "\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "vision_language_model = AutoModelForCausalLM.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True).to(device).eval()\n",
        "vision_language_processor = AutoProcessor.from_pretrained('microsoft/Florence-2-base', trust_remote_code=True)\n",
        "\n",
        "def describe_image(uploaded_image):\n",
        "    \"\"\"\n",
        "    Generates a detailed description of the input image.\n",
        "\n",
        "    Args:\n",
        "        uploaded_image (PIL.Image.Image or numpy.ndarray): The image to describe.\n",
        "\n",
        "    Returns:\n",
        "        str: A detailed textual description of the image.\n",
        "    \"\"\"\n",
        "    if not isinstance(uploaded_image, Image.Image):\n",
        "        uploaded_image = Image.fromarray(uploaded_image)\n",
        "\n",
        "    inputs = vision_language_processor(text=\"<MORE_DETAILED_CAPTION>\", images=uploaded_image, return_tensors=\"pt\").to(device)\n",
        "    with torch.no_grad():\n",
        "        generated_ids = vision_language_model.generate(\n",
        "            input_ids=inputs[\"input_ids\"],\n",
        "            pixel_values=inputs[\"pixel_values\"],\n",
        "            max_new_tokens=1024,\n",
        "            early_stopping=False,\n",
        "            do_sample=False,\n",
        "            num_beams=3,\n",
        "        )\n",
        "    generated_text = vision_language_processor.batch_decode(generated_ids, skip_special_tokens=False)[0]\n",
        "    processed_description = vision_language_processor.post_process_generation(\n",
        "        generated_text,\n",
        "        task=\"<MORE_DETAILED_CAPTION>\",\n",
        "        image_size=(uploaded_image.width, uploaded_image.height)\n",
        "    )\n",
        "    image_description = processed_description[\"<MORE_DETAILED_CAPTION>\"]\n",
        "    print(\"\\nImage description generated!:\", image_description)\n",
        "    return image_description\n",
        "\n",
        "image_description_interface = gr.Interface(\n",
        "    fn=describe_image,\n",
        "    inputs=gr.Image(label=\"Upload Image\"),\n",
        "    outputs=gr.Textbox(label=\"Generated Caption\", lines=4, show_copy_button=True),\n",
        "    live=False,\n",
        ")\n",
        "\n",
        "image_description_interface.launch(debug=True, ssr_mode=False)"
      ],
      "metadata": {
        "id": "kW4MjaOs3c9E"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}