Visual_Q_&_A.ipynb

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            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
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            "text/plain": [
              "preprocessor_config.json:   0%|          | 0.00/445 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b04c90d22b6244d6bf87f5c8deccc99b"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Use a pipeline as a high-level helper\n",
        "from transformers import pipeline\n",
        "\n",
        "pipe = pipeline(\"visual-question-answering\", model=\"Salesforce/blip-vqa-base\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "from PIL import Image\n",
        "from transformers import BlipProcessor, BlipForQuestionAnswering\n",
        "\n",
        "processor = BlipProcessor.from_pretrained(\"Salesforce/blip-vqa-base\")\n",
        "model = BlipForQuestionAnswering.from_pretrained(\"Salesforce/blip-vqa-base\").to(\"cuda\")\n",
        "\n",
        "img_url = 'https://gampa.in/assets/images/banner/Gampa-2.jpg'\n",
        "raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')\n",
        "\n",
        "question = \"how many dogs are in image\"\n",
        "inputs = processor(raw_image, question, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "out = model.generate(**inputs)\n",
        "print(processor.decode(out[0], skip_special_tokens=True))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gDPGZAmN5IPq",
        "outputId": "0be42bde-07ce-4525-df85-b40b51e8e350"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1141: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "question = \"what the dress code he wear\"\n",
        "inputs = processor(raw_image, question, return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "out = model.generate(**inputs)\n",
        "print(processor.decode(out[0], skip_special_tokens=True))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N5hasnC0GJkx",
        "outputId": "bfe5378a-b22c-4df2-ba55-29de15a7cfb8"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "business\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# First, install the necessary libraries\n",
        "!pip install gradio transformers torch pillow\n",
        "\n",
        "# Import necessary libraries\n",
        "import gradio as gr\n",
        "from PIL import Image\n",
        "from transformers import BlipProcessor, BlipForQuestionAnswering\n",
        "import torch\n",
        "\n",
        "# Load the processor and model from Hugging Face Hub\n",
        "processor = BlipProcessor.from_pretrained(\"Salesforce/blip-vqa-base\")\n",
        "model = BlipForQuestionAnswering.from_pretrained(\"Salesforce/blip-vqa-base\")\n",
        "\n",
        "# Define a function that uses the model to answer questions about the image\n",
        "def answer_question(image, question):\n",
        "    # Convert the image to RGB\n",
        "    image = image.convert('RGB')\n",
        "\n",
        "    # Prepare inputs\n",
        "    inputs = processor(image, question, return_tensors=\"pt\")\n",
        "\n",
        "    # Send model inputs to CUDA if available\n",
        "    if torch.cuda.is_available():\n",
        "        inputs = inputs.to(\"cuda\")\n",
        "        model.to(\"cuda\")\n",
        "\n",
        "    # Generate an answer\n",
        "    output = model.generate(**inputs)\n",
        "\n",
        "    # Decode the generated tokens to text\n",
        "    answer = processor.decode(output[0], skip_special_tokens=True)\n",
        "    return answer\n",
        "\n",
        "# Create a Gradio interface\n",
        "interface = gr.Interface(\n",
        "    fn=answer_question,\n",
        "    inputs=[\n",
        "        gr.components.Image(type=\"pil\"),\n",
        "        gr.components.Textbox(label=\"Question about the image\")\n",
        "    ],\n",
        "    outputs=gr.components.Textbox(),\n",
        "    title=\"Visual Question Answering\",\n",
        "    description=\"Upload an image and ask a question about it.\"\n",
        ")\n",
        "\n",
        "# Launch the Gradio app\n",
        "interface.launch()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NQZ_H1WKcvvC",
        "outputId": "1ad6147c-5bab-471e-f86e-0463dbe3adbd"
      },
      "execution_count": 4,
      "outputs": [
        {
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          "name": "stdout",
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            "Collecting starlette<0.38.0,>=0.37.2 (from fastapi->gradio)\n",
            "  Downloading starlette-0.37.2-py3-none-any.whl (71 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.9/71.9 kB\u001b[0m \u001b[31m11.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
            "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio) (23.2.0)\n",
            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio) (2023.12.1)\n",
            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio) (0.34.0)\n",
            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio) (0.18.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib~=3.0->gradio) (1.16.0)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer<1.0,>=0.12->gradio) (2.16.1)\n",
            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx>=0.24.1->gradio) (1.2.1)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer<1.0,>=0.12->gradio) (0.1.2)\n",
            "Building wheels for collected packages: ffmpy\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ffmpy: filename=ffmpy-0.3.2-py3-none-any.whl size=5584 sha256=27e9004aa6b62ac589d5ae3818fb8fe8109724b76c20a9e0bc68d90b3cef8116\n",
            "  Stored in directory: /root/.cache/pip/wheels/bd/65/9a/671fc6dcde07d4418df0c592f8df512b26d7a0029c2a23dd81\n",
            "Successfully built ffmpy\n",
            "Installing collected packages: pydub, ffmpy, websockets, tomlkit, shellingham, semantic-version, ruff, python-multipart, orjson, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, h11, aiofiles, uvicorn, starlette, nvidia-cusparse-cu12, nvidia-cudnn-cu12, httpcore, typer, nvidia-cusolver-cu12, httpx, fastapi, gradio-client, gradio\n",
            "  Attempting uninstall: typer\n",
            "    Found existing installation: typer 0.9.4\n",
            "    Uninstalling typer-0.9.4:\n",
            "      Successfully uninstalled typer-0.9.4\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "spacy 3.7.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\n",
            "weasel 0.3.4 requires typer<0.10.0,>=0.3.0, but you have typer 0.12.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed aiofiles-23.2.1 fastapi-0.110.2 ffmpy-0.3.2 gradio-4.27.0 gradio-client-0.15.1 h11-0.14.0 httpcore-1.0.5 httpx-0.27.0 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 orjson-3.10.1 pydub-0.25.1 python-multipart-0.0.9 ruff-0.4.1 semantic-version-2.10.0 shellingham-1.5.4 starlette-0.37.2 tomlkit-0.12.0 typer-0.12.3 uvicorn-0.29.0 websockets-11.0.3\n",
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://cfdfabfd590c8d88d4.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
          ]
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        {
          "output_type": "execute_result",
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}