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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": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "e5b90bab37c942caad7ff7502d2d6764", + "3838034d9cf84d7d88ec98f26b8c379b", + "7c51276df5fe4c128f1dc72083fc30e1", + "f420148aa7d14cbb9a17a9cdc319804e", + "5ff9b3c0d4d143ce9beb5f65a4bc5187", + "a5ab9a9a1b5145efbcc1192d0b00c728", + "1315aabac78045c7808449b9fe6f934a", + "2746b66c34ef4a2bbe13da9e9f4a3386", + "604370f450f2499dacc159b446a9974f", + "5cb87b82c33843f68262785073fabc1a", + "0366635c31b9437e9671d50c7883b7da", + "d318c88446374406a30d74f81ad602d3", + "b34242a7bddf46969c06ed9a25643b2c", + 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+ "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" + ] + }, + { + "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