Image_Caption_Generator_Tool.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install sentence-splitter\n",
        "!pip install SentencePiece\n",
        "!pip install open_clip_torch\n",
        "!pip install gradio\n",
        "!pip install torch\n",
        "!pip install transformers"
      ],
      "metadata": {
        "id": "q54BJRt21jbH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1eee08fe-92c1-400a-d391-f6577e17f43b"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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            "\u001b[?25hRequirement already satisfied: regex>=2017.12.12 in /usr/local/lib/python3.10/dist-packages (from sentence-splitter) (2022.10.31)\n",
            "Installing collected packages: sentence-splitter\n",
            "Successfully installed sentence-splitter-1.4\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting SentencePiece\n",
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            "\u001b[?25hInstalling collected packages: SentencePiece\n",
            "Successfully installed SentencePiece-0.1.98\n",
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            "Installing collected packages: ftfy, huggingface-hub, timm, open_clip_torch\n",
            "Successfully installed ftfy-6.1.1 huggingface-hub-0.14.1 open_clip_torch-2.18.0 timm-0.6.13\n",
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#------------------------------------------------- Imports -----------------------------------------\n",
        "import gradio as gr\n",
        "import torch\n",
        "import open_clip\n",
        "from transformers import AutoProcessor, AutoTokenizer, AutoImageProcessor, AutoModelForCausalLM, BlipForConditionalGeneration, Blip2ForConditionalGeneration, VisionEncoderDecoderModel\n",
        "from huggingface_hub import hf_hub_download\n",
        "from transformers import PegasusForConditionalGeneration, PegasusTokenizer\n"
      ],
      "metadata": {
        "id": "VOoM3f0-wH2P"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#------------------------------------ Paraphrasing model -----------------------------------------\n",
        "\n",
        "# importing the PEGASUS Transformer model\n",
        "model_name = 'tuner007/pegasus_paraphrase'\n",
        "torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "tokenizer = PegasusTokenizer.from_pretrained(model_name)\n",
        "pegasus_model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device)\n",
        "\n",
        "# setting up the model\n",
        "def get_response(input_text):\n",
        "  first_recommendation=input_text;\n",
        "  batch = tokenizer.prepare_seq2seq_batch([input_text],truncation=True,padding='longest',max_length=60, return_tensors=\"pt\").to(torch_device)\n",
        "  translated = pegasus_model.generate(**batch,max_length=60,num_beams=10, num_return_sequences=4, temperature=1.5)\n",
        "  tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True)\n",
        "  tgt_text.insert(0,first_recommendation)\n",
        "  return tgt_text"
      ],
      "metadata": {
        "id": "v51FLNfm1jAi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
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    {
      "cell_type": "code",
      "source": [
        "#------------------------------------ Image Captioning model -----------------------------------------\n",
        "\n",
        "\n",
        "blip_processor_base = AutoProcessor.from_pretrained(\"Salesforce/blip-image-captioning-base\")\n",
        "blip_model_base = BlipForConditionalGeneration.from_pretrained(\"Salesforce/blip-image-captioning-base\")\n",
        "\n",
        "# Selecting compute unit type\n",
        "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
        "\n",
        "\n",
        "blip_model_base.to(device)\n",
        "\n",
        "\n",
        "def generate_caption(processor, model, image, tokenizer=None, use_float_16=False):\n",
        "    inputs = processor(images=image, return_tensors=\"pt\").to(device)\n",
        "\n",
        "    if use_float_16:\n",
        "        inputs = inputs.to(torch.float16)\n",
        "    \n",
        "    generated_ids = model.generate(pixel_values=inputs.pixel_values, max_length=50)\n",
        "\n",
        "    if tokenizer is not None:\n",
        "        generated_caption = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]\n",
        "    else:\n",
        "        generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]\n",
        "   \n",
        "    return generated_caption\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "def generate_captions(image):\n",
        "    \n",
        "    caption_blip_base = generate_caption(blip_processor_base, blip_model_base, image)\n",
        "\n",
        "    return caption_blip_base\n"
      ],
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    {
      "cell_type": "code",
      "source": [
        "#------------------------------------------------ UI module ---------------------------------\n",
        "\n",
        "# Output components for caption generator and paraphraser   \n",
        "outputs_caption = [gr.outputs.Textbox(label=\"Caption generated by BLIP - base\")] \n",
        "outputs_paraphraser = [gr.outputs.Textbox(label=\"Caption 1:\"),gr.outputs.Textbox(label=\"Caption 2:\"),gr.outputs.Textbox(label=\"Caption 3:\"),gr.outputs.Textbox(label=\"Caption 4:\"),gr.outputs.Textbox(label=\"Caption 5:\")] \n",
        "\n",
        "title = \"Image Caption Generator\"\n",
        "description = \"Drag and drop images, generate captions on the fly!\"\n",
        "\n",
        "Caption_generator = gr.Interface(fn=generate_captions, \n",
        "                         inputs=gr.inputs.Image(type=\"pil\"),\n",
        "                         outputs=outputs_caption,\n",
        "                         title=title,\n",
        "                         description=description,\n",
        "                         enable_queue=True)\n",
        "Paraphraser = gr.Interface(fn=get_response,\n",
        "                          title=title,\n",
        "                          description=description, \n",
        "                          inputs=[gr.outputs.Textbox(label=\"Caption generated by BLIP - base\")],\n",
        "                          outputs=outputs_paraphraser)\n",
        "\n",
        "# Launching Caption generator and paraphraser interface in series\n",
        "gr.Series(Caption_generator, Paraphraser).launch(share=True)"
      ],
      "metadata": {
        "id": "911MSQN_Zmg8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 760
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        "outputId": "8eb5c2dd-117f-415f-e432-d0e0980a7e7d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gradio/outputs.py:22: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/gradio/inputs.py:257: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
            "  warnings.warn(value)\n",
            "/usr/local/lib/python3.10/dist-packages/gradio/deprecation.py:40: UserWarning: `enable_queue` is deprecated in `Interface()`, please use it within `launch()` instead.\n",
            "  warnings.warn(value)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://4fc4b0d0a7e2e87d86.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
          ]
        },
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}