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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", - 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"Please note that authentication is recommended but still optional to access public models or datasets.\n", - " warnings.warn(\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "adapter_config.json: 0%| | 0.00/752 [00:00 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'{m.group()}', 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": [ - "" - ], - "text/html": [ - "
" - ] - }, - "metadata": {} - } - ] - } - ] -} \ No newline at end of file