OCR_using_QwenVL_by_PS (1).ipynb

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            "\u001b[?25hBuilding wheels for collected packages: gputil\n",
            "  Building wheel for gputil (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gputil: filename=GPUtil-1.4.0-py3-none-any.whl size=7392 sha256=52fe5740048983cdade95717c2944fc79ef3e750716f24c2f81fc2a5e4ee2cd6\n",
            "  Stored in directory: /root/.cache/pip/wheels/a9/8a/bd/81082387151853ab8b6b3ef33426e98f5cbfebc3c397a9d4d0\n",
            "Successfully built gputil\n",
            "Installing collected packages: pydub, ninja, gputil, xxhash, websockets, tomlkit, semantic-version, ruff, pytrec-eval-terrier, python-multipart, pyarrow, pdf2image, orjson, jsonlines, h11, ffmpy, dill, aiofiles, uvicorn, starlette, multiprocess, httpcore, tokenizers, httpx, fastapi, transformers, gradio-client, sentence-transformers, peft, gradio, datasets, mteb, colpali-engine, byaldi\n",
            "  Attempting uninstall: pyarrow\n",
            "    Found existing installation: pyarrow 14.0.2\n",
            "    Uninstalling pyarrow-14.0.2:\n",
            "      Successfully uninstalled pyarrow-14.0.2\n",
            "  Attempting uninstall: tokenizers\n",
            "    Found existing installation: tokenizers 0.19.1\n",
            "    Uninstalling tokenizers-0.19.1:\n",
            "      Successfully uninstalled tokenizers-0.19.1\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.44.2\n",
            "    Uninstalling transformers-4.44.2:\n",
            "      Successfully uninstalled transformers-4.44.2\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",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed aiofiles-23.2.1 byaldi-0.0.4 colpali-engine-0.3.1 datasets-3.0.1 dill-0.3.8 fastapi-0.115.0 ffmpy-0.4.0 gputil-1.4.0 gradio-4.44.0 gradio-client-1.3.0 h11-0.14.0 httpcore-1.0.5 httpx-0.27.2 jsonlines-4.0.0 mteb-1.6.35 multiprocess-0.70.16 ninja-1.11.1.1 orjson-3.10.7 pdf2image-1.17.0 peft-0.11.1 pyarrow-17.0.0 pydub-0.25.1 python-multipart-0.0.11 pytrec-eval-terrier-0.5.6 ruff-0.6.8 semantic-version-2.10.0 sentence-transformers-3.1.1 starlette-0.38.6 tokenizers-0.20.0 tomlkit-0.12.0 transformers-4.45.1 uvicorn-0.31.0 websockets-12.0 xxhash-3.5.0\n"
          ]
        }
      ],
      "source": [
        "!pip install torch torchvision torchaudio transformers opencv-python Pillow byaldi gradio"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import libraries\n",
        "import cv2\n",
        "from PIL import Image\n",
        "from transformers import AutoProcessor, Qwen2VLForConditionalGeneration\n",
        "import torch\n",
        "from byaldi import RAGMultiModalModel\n",
        "from google.colab import files\n",
        "from IPython.display import display, HTML\n",
        "import os\n",
        "import re"
      ],
      "metadata": {
        "id": "QhtvJ4k2KdyS"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# to detect cuda(GPU)\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(\"Using device:\", device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z34CN3VaKfwb",
        "outputId": "66deba79-fc8f-4407-e229-5751680c0adf"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using device: cpu\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#loading models\n",
        "RAG = RAGMultiModalModel.from_pretrained(\"vidore/colpali\", verbose=0)\n",
        "model = Qwen2VLForConditionalGeneration.from_pretrained(\n",
        "    \"Qwen/Qwen2-VL-2B-Instruct\",\n",
        "    torch_dtype=torch.float16,\n",
        "    device_map=\"auto\"\n",
        ")\n",
        "processor = AutoProcessor.from_pretrained(\"Qwen/Qwen2-VL-2B-Instruct\")\n",
        "\n",
        "torch.cuda.empty_cache()\n"
      ],
      "metadata": {
        "colab": {
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          "height": 1000,
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        "id": "edorQR88KmZJ",
        "outputId": "b383e96c-f358-4892-9058-afce960e717c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "`config.hidden_act` is ignored, you should use `config.hidden_activation` instead.\n",
            "Gemma's activation function will be set to `gelu_pytorch_tanh`. Please, use\n",
            "`config.hidden_activation` if you want to override this behaviour.\n",
            "See https://github.com/huggingface/transformers/pull/29402 for more details.\n"
          ]
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        {
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          "text": [
            "No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'\n"
          ]
        },
        {
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          "text": [
            "Unrecognized keys in `rope_scaling` for 'rope_type'='default': {'mrope_section'}\n"
          ]
        },
        {
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          "text": [
            "`Qwen2VLRotaryEmbedding` can now be fully parameterized by passing the model config through the `config` argument. All other arguments will be removed in v4.46\n"
          ]
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a7f3e4d4dd2b440e8e65c8589bdd4986"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Upload image\n",
        "# def upload_image():\n",
        "#     uploaded = files.upload()\n",
        "#     for filename in uploaded.keys():\n",
        "#         print(f'Uploaded file: {filename}')\n",
        "#         return filename\n",
        "\n",
        "# image_path = upload_image()\n"
      ],
      "metadata": {
        "id": "5wYQE2phKsed"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocessing using OpenCV\n",
        "def preprocess_image(image_path):\n",
        "    image = cv2.imread(image_path)\n",
        "    if image is None:\n",
        "        raise FileNotFoundError(f\"Image not found at the path: {image_path}\")\n",
        "\n",
        "    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
        "\n",
        "    # Maintain aspect ratio\n",
        "    height, width = gray.shape\n",
        "    if height > width:\n",
        "        new_height = 1024\n",
        "        new_width = int((width / height) * new_height)\n",
        "    else:\n",
        "        new_width = 1024\n",
        "        new_height = int((height / width) * new_width)\n",
        "\n",
        "    resized_image = cv2.resize(gray, (new_width, new_height))\n",
        "\n",
        "    blurred = cv2.GaussianBlur(resized_image, (5, 5), 0)\n",
        "    thresholded = cv2.adaptiveThreshold(blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)\n",
        "    denoised = cv2.fastNlMeansDenoising(thresholded, h=30)\n",
        "    pil_image = Image.fromarray(denoised)\n",
        "\n",
        "    return pil_image\n",
        "\n",
        " # Call the function and store the result\n",
        "# pil_image = preprocess_image(image_path)\n",
        "\n",
        "# display(pil_image) # Now pil_image is accessible here"
      ],
      "metadata": {
        "id": "Wll58UP0K1cL"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#extract the text\n",
        "def extract_text(image_path):\n",
        "    try:\n",
        "        processed_image = preprocess_image(image_path)\n",
        "        messages = [\n",
        "            {\"role\": \"user\", \"content\": [{\"type\": \"image\"}, {\"type\": \"text\", \"text\": \"PLease extract the both hindi and english text as they appear in the image\"}]}\n",
        "        ]\n",
        "        text_prompt = processor.apply_chat_template(messages, add_generation_prompt=True)\n",
        "        inputs = processor(text=[text_prompt], images=[processed_image], padding=True, return_tensors=\"pt\").to(device)\n",
        "        output_ids = model.generate(**inputs, max_new_tokens=1042)\n",
        "        generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]\n",
        "        extracted_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)[0]\n",
        "        return extracted_text\n",
        "    except Exception as e:\n",
        "        return f\"An error occurred during text extraction: {e}\""
      ],
      "metadata": {
        "id": "DNAG_mbMLAku"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#keyword searching\n",
        "def keyword_search(extracted_text, keywords):\n",
        "    if not keywords:\n",
        "        return extracted_text, \"Please enter a keyword to search and highlight.\"\n",
        "    keywords = [keyword.strip() for keyword in keywords.split(\",\") if keyword.strip()]\n",
        "\n",
        "    highlighted_text = \"\"\n",
        "\n",
        "    lines = extracted_text.split('\\n')\n",
        "    for line in lines:\n",
        "        for keyword in keywords:\n",
        "            pattern = re.compile(re.escape(keyword), re.IGNORECASE)\n",
        "            line = pattern.sub(lambda m: f'<span style=\"color: red;\">{m.group()}</span>', line)\n",
        "        highlighted_text += line + '\\n'\n",
        "    return highlighted_text"
      ],
      "metadata": {
        "id": "hyRx7BQXLEOC"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#OCR and keyword search interface\n",
        "def ocr_interface(image):\n",
        "    image_path = \"temp_image.png\"\n",
        "    image.save(image_path)\n",
        "    extracted_text = extract_text(image_path)\n",
        "    os.remove(image_path)\n",
        "\n",
        "    return extracted_text, \"\"\n",
        "def keyword_interface(extracted_text, keywords):\n",
        "    highlighted_text = keyword_search(extracted_text, keywords)\n",
        "    return highlighted_text\n"
      ],
      "metadata": {
        "id": "flB4r1AjLNXX"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to launch the Gradio interface\n",
        "import gradio as gr\n",
        "def launch_gradio():\n",
        "    with gr.Blocks() as interface:\n",
        "        with gr.Row():\n",
        "            with gr.Column():\n",
        "                image_input = gr.Image(type=\"pil\", label=\"Upload Image for OCR\")\n",
        "\n",
        "            with gr.Column():\n",
        "                extracted_text = gr.Textbox(label=\"Extracted Text\", lines=10, interactive=False)\n",
        "\n",
        "        keywords = gr.Textbox(label=\"Enter Keywords (comma-separated)\", interactive=True)\n",
        "        highlighted_text = gr.HTML(label=\"Highlighted Text\")\n",
        "\n",
        "        extract_btn = gr.Button(\"Extract Text\")\n",
        "        extract_btn.click(fn=ocr_interface, inputs=image_input, outputs=[extracted_text, highlighted_text])\n",
        "\n",
        "        keyword_btn = gr.Button(\"Search & Highlight Keywords\")\n",
        "        keyword_btn.click(fn=keyword_interface, inputs=[extracted_text, keywords], outputs=highlighted_text)\n",
        "\n",
        "    interface.launch()\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    launch_gradio()"
      ],
      "metadata": {
        "id": "pxr8ZcaEgrGD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 650
        },
        "outputId": "36e099bd-c7ba-4720-be6d-658bdc5cd9cc"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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://7300c3fa4037252a21.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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://7300c3fa4037252a21.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}