diff --git "a/OCR_using_QwenVL_by_PS (1).ipynb" "b/OCR_using_QwenVL_by_PS (1).ipynb"
deleted file mode 100644--- "a/OCR_using_QwenVL_by_PS (1).ipynb"
+++ /dev/null
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- " 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",
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- "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",
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- " Found existing installation: tokenizers 0.19.1\n",
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- " 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": {
- "base_uri": "https://localhost:8080/",
- "height": 1000,
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- "id": "edorQR88KmZJ",
- "outputId": "b383e96c-f358-4892-9058-afce960e717c"
- },
- "execution_count": 4,
- "outputs": [
- {
- "output_type": "stream",
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- "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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- {
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- "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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- "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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- {
- "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'{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