diff --git "a/NetraEmbed_Gradio_Demo_final.ipynb" "b/NetraEmbed_Gradio_Demo_final.ipynb" new file mode 100644--- /dev/null +++ "b/NetraEmbed_Gradio_Demo_final.ipynb" @@ -0,0 +1,6845 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "header" + }, + "source": [ + "# NetraEmbed Gradio Demo\n", + "\n", + "**πŸš€ Universal Multilingual Multimodal Document Retrieval**\n", + "\n", + "This notebook provides a Gradio interface for testing both BiGemma3 and ColGemma3 models with PDF document upload, automatic conversion to images, and query-based retrieval.\n", + "\n", + "**Available Models:**\n", + "- **NetraEmbed (BiGemma3)**: Single-vector embedding with Matryoshka representation - Fast retrieval with cosine similarity\n", + "- **ColNetraEmbed (ColGemma3)**: Multi-vector embedding with late interaction - High-quality retrieval with MaxSim scoring and attention heatmaps\n", + "\n", + "**Links:**\n", + "- πŸ“„ [Paper](https://arxiv.org/abs/2512.03514)\n", + "- πŸ’» [GitHub](https://github.com/adithya-s-k/colpali)\n", + "- πŸ€— [HuggingFace Model](https://huggingface.co/Cognitive-Lab/ColNetraEmbed)\n", + "- πŸ“ [Blog](https://www.cognitivelab.in/blog/introducing-netraembed)\n", + "\n", + "---\n", + "\n", + "**⚠️ GPU Requirements:**\n", + "- **T4 GPU (16GB)**: Can run single model at a time (use smaller batch sizes)\n", + "- **L40S GPU (48GB)**: Can run both models simultaneously\n", + "- **A100 GPU (40-80GB)**: Can run both models simultaneously\n", + "\n", + "Go to `Runtime` β†’ `Change runtime type` β†’ Select GPU" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "setup" + }, + "source": [ + "## 1. Setup and Installation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "install_dependencies", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "0414436f-be47-43f8-da9b-22c9df5c61a4" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m504.9/504.9 kB\u001b[0m \u001b[31m17.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m887.9/887.9 MB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m594.3/594.3 MB\u001b[0m \u001b[31m851.8 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.2/10.2 MB\u001b[0m \u001b[31m115.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.0/88.0 MB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m954.8/954.8 kB\u001b[0m \u001b[31m62.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m193.1/193.1 MB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.2/1.2 MB\u001b[0m \u001b[31m68.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m63.6/63.6 MB\u001b[0m \u001b[31m13.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m267.5/267.5 MB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━���━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m288.2/288.2 MB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m322.4/322.4 MB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.3/39.3 MB\u001b[0m \u001b[31m22.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m90.0/90.0 kB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m155.6/155.6 MB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.6/8.6 MB\u001b[0m \u001b[31m133.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Building wheel for colpali_engine (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\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", + "torchaudio 2.9.0+cu126 requires torch==2.9.0, but you have torch 2.8.0 which is incompatible.\u001b[0m\u001b[31m\n", + "Reading package lists... Done\n", + "Building dependency tree... Done\n", + "Reading state information... Done\n", + "The following NEW packages will be installed:\n", + " poppler-utils\n", + "0 upgraded, 1 newly installed, 0 to remove and 41 not upgraded.\n", + "Need to get 186 kB of archives.\n", + "After this operation, 697 kB of additional disk space will be used.\n", + "Get:1 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 poppler-utils amd64 22.02.0-2ubuntu0.12 [186 kB]\n", + "Fetched 186 kB in 0s (415 kB/s)\n", + "Selecting previously unselected package poppler-utils.\n", + "(Reading database ... 121713 files and directories currently installed.)\n", + "Preparing to unpack .../poppler-utils_22.02.0-2ubuntu0.12_amd64.deb ...\n", + "Unpacking poppler-utils (22.02.0-2ubuntu0.12) ...\n", + "Setting up poppler-utils (22.02.0-2ubuntu0.12) ...\n", + "Processing triggers for man-db (2.10.2-1) ...\n", + "βœ… Installation complete!\n" + ] + } + ], + "source": [ + "# Install required packages\n", + "!pip install -q git+https://github.com/adithya-s-k/colpali.git\n", + "!pip install -q gradio pdf2image Pillow matplotlib seaborn einops numpy\n", + "!apt-get install -y poppler-utils # For pdf2image\n", + "\n", + "print(\"βœ… Installation complete!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "imports", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4d505ca8-6b4c-41f7-c7c6-a5f2f880032a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Device: cuda\n", + "GPU: Tesla T4\n", + "GPU Memory: 15.83 GB\n", + "⚠️ GPU has limited memory (<24GB) - Recommended to run one model at a time\n", + "βœ… Imports complete!\n" + ] + } + ], + "source": [ + "# Import libraries\n", + "import io\n", + "import gc\n", + "import os\n", + "import math\n", + "from typing import Iterator, List, Optional, Tuple\n", + "\n", + "import gradio as gr\n", + "import torch\n", + "from pdf2image import convert_from_path\n", + "from PIL import Image\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import seaborn as sns\n", + "from einops import rearrange\n", + "\n", + "# Import from colpali_engine\n", + "from colpali_engine.models import BiGemma3, BiGemmaProcessor3, ColGemma3, ColGemmaProcessor3\n", + "from colpali_engine.interpretability import get_similarity_maps_from_embeddings\n", + "from colpali_engine.interpretability.similarity_map_utils import normalize_similarity_map\n", + "\n", + "# Check GPU availability and memory\n", + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "gpu_memory_gb = 0.0\n", + "gpu_name = \"\"\n", + "\n", + "print(f\"Device: {device}\")\n", + "if torch.cuda.is_available():\n", + " gpu_name = torch.cuda.get_device_name(0)\n", + " gpu_memory_gb = torch.cuda.get_device_properties(0).total_memory / 1e9\n", + " print(f\"GPU: {gpu_name}\")\n", + " print(f\"GPU Memory: {gpu_memory_gb:.2f} GB\")\n", + "\n", + " # Determine GPU capability\n", + " if gpu_memory_gb >= 40:\n", + " print(\"βœ… GPU has sufficient memory (β‰₯40GB) - Can run both models simultaneously\")\n", + " can_run_both_models = True\n", + " elif gpu_memory_gb >= 24:\n", + " print(\"⚠️ GPU has moderate memory (24-40GB) - Can run both models but may need careful memory management\")\n", + " can_run_both_models = True\n", + " else:\n", + " print(\"⚠️ GPU has limited memory (<24GB) - Recommended to run one model at a time\")\n", + " can_run_both_models = False\n", + "else:\n", + " print(\"⚠️ WARNING: GPU not available! Please change runtime to GPU.\")\n", + " can_run_both_models = False\n", + "\n", + "# Set memory optimization\n", + "os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True,max_split_size_mb:128\"\n", + "\n", + "print(\"βœ… Imports complete!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6xjcHP14pVIf" + }, + "source": [ + "## GPU Memory Requirements & Model Selection\n", + "\n", + "The notebook automatically detects your GPU and adjusts settings:\n", + "\n", + "**GPU Configurations:**\n", + "- **T4 GPU (16GB)**:\n", + " - Can run **one model at a time** (BiGemma3 OR ColGemma3)\n", + " - Batch size: 1 page at a time\n", + " - Recommended for small PDFs (<20 pages)\n", + " \n", + "- **L4 / RTX 4090 (24GB)**:\n", + " - Can run **one model at a time**\n", + " - Batch size: 2 pages at a time\n", + " - Good for medium PDFs (<50 pages)\n", + "\n", + "- **L40S (48GB) / A100 (40-80GB)**:\n", + " - Can run **both models simultaneously**\n", + " - Batch size: 4 pages at a time\n", + " - Best for large PDFs and running comparisons\n", + "\n", + "The interface will automatically show or hide the \"Both\" option based on available GPU memory." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "document_index" + }, + "source": [ + "## 2. Initialize Document Index and Model Management" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "init_document_index", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "fdee0762-24be-4358-c380-b619eba3a90a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "βœ… Document index initialized!\n", + " - Max batch size: 8\n", + " - Embedding batch size: 1\n" + ] + } + ], + "source": [ + "# Global state for models and indexed documents\n", + "class DocumentIndex:\n", + " def __init__(self):\n", + " self.images: List[Image.Image] = []\n", + " self.bigemma_embeddings = None\n", + " self.colgemma_embeddings = None\n", + " self.bigemma_model = None\n", + " self.bigemma_processor = None\n", + " self.colgemma_model = None\n", + " self.colgemma_processor = None\n", + " self.models_loaded = {\"bigemma\": False, \"colgemma\": False}\n", + "\n", + "\n", + "doc_index = DocumentIndex()\n", + "\n", + "# Configuration - Adjust batch size based on GPU memory\n", + "if gpu_memory_gb >= 40:\n", + " MAX_BATCH_SIZE = 32 # Large GPU (A100, L40S)\n", + " EMBEDDING_BATCH_SIZE = 4 # For embedding generation\n", + "elif gpu_memory_gb >= 24:\n", + " MAX_BATCH_SIZE = 16 # Medium GPU (L4, RTX 4090)\n", + " EMBEDDING_BATCH_SIZE = 2\n", + "else:\n", + " MAX_BATCH_SIZE = 8 # Small GPU (T4)\n", + " EMBEDDING_BATCH_SIZE = 1 # Process one page at a time\n", + "\n", + "print(\"βœ… Document index initialized!\")\n", + "print(f\" - Max batch size: {MAX_BATCH_SIZE}\")\n", + "print(f\" - Embedding batch size: {EMBEDDING_BATCH_SIZE}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "helper_functions", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "71b561d7-39f5-40ff-c23f-f8fad0fd0df3" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "βœ… Helper functions defined!\n" + ] + } + ], + "source": [ + "# Helper functions for model management\n", + "def get_loaded_models() -> List[str]:\n", + " \"\"\"Get list of currently loaded models.\"\"\"\n", + " loaded = []\n", + " if doc_index.bigemma_model is not None:\n", + " loaded.append(\"BiGemma3\")\n", + " if doc_index.colgemma_model is not None:\n", + " loaded.append(\"ColGemma3\")\n", + " return loaded\n", + "\n", + "\n", + "def get_model_choice_from_loaded() -> str:\n", + " \"\"\"Determine model choice string based on what's loaded.\"\"\"\n", + " loaded = get_loaded_models()\n", + " if \"BiGemma3\" in loaded and \"ColGemma3\" in loaded:\n", + " return \"Both\"\n", + " elif \"BiGemma3\" in loaded:\n", + " return \"NetraEmbed (BiGemma3)\"\n", + " elif \"ColGemma3\" in loaded:\n", + " return \"ColNetraEmbed (ColGemma3)\"\n", + " else:\n", + " return \"\"\n", + "\n", + "\n", + "def load_bigemma_model():\n", + " \"\"\"Load BiGemma3 model and processor.\"\"\"\n", + " if doc_index.bigemma_model is None:\n", + " print(\"Loading BiGemma3 (NetraEmbed)...\")\n", + " try:\n", + " doc_index.bigemma_processor = BiGemmaProcessor3.from_pretrained(\n", + " \"Cognitive-Lab/NetraEmbed\",\n", + " use_fast=True,\n", + " )\n", + " doc_index.bigemma_model = BiGemma3.from_pretrained(\n", + " \"Cognitive-Lab/NetraEmbed\",\n", + " torch_dtype=torch.bfloat16,\n", + " device_map=device,\n", + " )\n", + " doc_index.bigemma_model.eval()\n", + " doc_index.models_loaded[\"bigemma\"] = True\n", + " print(\"βœ“ BiGemma3 loaded successfully\")\n", + " except Exception as e:\n", + " print(f\"❌ Failed to load BiGemma3: {str(e)}\")\n", + " raise\n", + " return doc_index.bigemma_model, doc_index.bigemma_processor\n", + "\n", + "\n", + "def load_colgemma_model():\n", + " \"\"\"Load ColGemma3 model and processor.\"\"\"\n", + " if doc_index.colgemma_model is None:\n", + " print(\"Loading ColGemma3 (ColNetraEmbed)...\")\n", + " try:\n", + " doc_index.colgemma_model = ColGemma3.from_pretrained(\n", + " \"Cognitive-Lab/ColNetraEmbed\",\n", + " dtype=torch.bfloat16,\n", + " device_map=device,\n", + " )\n", + " doc_index.colgemma_model.eval()\n", + " doc_index.colgemma_processor = ColGemmaProcessor3.from_pretrained(\n", + " \"Cognitive-Lab/ColNetraEmbed\",\n", + " use_fast=True,\n", + " )\n", + " doc_index.models_loaded[\"colgemma\"] = True\n", + " print(\"βœ“ ColGemma3 loaded successfully\")\n", + " except Exception as e:\n", + " print(f\"❌ Failed to load ColGemma3: {str(e)}\")\n", + " raise\n", + " return doc_index.colgemma_model, doc_index.colgemma_processor\n", + "\n", + "\n", + "def unload_models():\n", + " \"\"\"Unload models and free GPU memory.\"\"\"\n", + " try:\n", + " if doc_index.bigemma_model is not None:\n", + " del doc_index.bigemma_model\n", + " del doc_index.bigemma_processor\n", + " doc_index.bigemma_model = None\n", + " doc_index.bigemma_processor = None\n", + " doc_index.models_loaded[\"bigemma\"] = False\n", + "\n", + " if doc_index.colgemma_model is not None:\n", + " del doc_index.colgemma_model\n", + " del doc_index.colgemma_processor\n", + " doc_index.colgemma_model = None\n", + " doc_index.colgemma_processor = None\n", + " doc_index.models_loaded[\"colgemma\"] = False\n", + "\n", + " doc_index.bigemma_embeddings = None\n", + " doc_index.colgemma_embeddings = None\n", + " doc_index.images = []\n", + "\n", + " gc.collect()\n", + " if torch.cuda.is_available():\n", + " torch.cuda.empty_cache()\n", + " torch.cuda.synchronize()\n", + "\n", + " return \"βœ… Models unloaded and GPU memory cleared\"\n", + " except Exception as e:\n", + " return f\"❌ Error unloading models: {str(e)}\"\n", + "\n", + "\n", + "def clear_incompatible_embeddings(model_choice: str) -> str:\n", + " \"\"\"Clear embeddings that are incompatible with currently loading models.\"\"\"\n", + " cleared = []\n", + "\n", + " if model_choice == \"NetraEmbed (BiGemma3)\":\n", + " if doc_index.colgemma_embeddings is not None:\n", + " doc_index.colgemma_embeddings = None\n", + " doc_index.images = []\n", + " cleared.append(\"ColGemma3\")\n", + " print(\"Cleared ColGemma3 embeddings\")\n", + "\n", + " elif model_choice == \"ColNetraEmbed (ColGemma3)\":\n", + " if doc_index.bigemma_embeddings is not None:\n", + " doc_index.bigemma_embeddings = None\n", + " doc_index.images = []\n", + " cleared.append(\"BiGemma3\")\n", + " print(\"Cleared BiGemma3 embeddings\")\n", + "\n", + " if cleared:\n", + " return f\"Cleared {', '.join(cleared)} embeddings - please re-index\"\n", + " return \"\"\n", + "\n", + "\n", + "def pdf_to_images(pdf_path: str) -> List[Image.Image]:\n", + " \"\"\"Convert PDF to list of PIL Images with error handling.\"\"\"\n", + " try:\n", + " print(f\"Converting PDF to images: {pdf_path}\")\n", + " images = convert_from_path(pdf_path, dpi=200)\n", + " print(f\"Converted {len(images)} pages\")\n", + " return images\n", + " except Exception as e:\n", + " print(f\"❌ PDF conversion error: {str(e)}\")\n", + " raise Exception(f\"Failed to convert PDF: {str(e)}\")\n", + "\n", + "\n", + "print(\"βœ… Helper functions defined!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "processing_functions" + }, + "source": [ + "## 3. Document Processing and Query Functions" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "heatmap_function", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "be4b6ae5-c17b-42d1-d9ad-ea78f63a0618" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "βœ… Heatmap function defined!\n" + ] + } + ], + "source": [ + "def generate_colgemma_heatmap(\n", + " image: Image.Image,\n", + " query: str,\n", + " query_embedding: torch.Tensor,\n", + " image_embedding: torch.Tensor,\n", + " model,\n", + " processor,\n", + ") -> Image.Image:\n", + " \"\"\"Generate heatmap overlay for ColGemma3 results.\"\"\"\n", + " try:\n", + " batch_images = processor.process_images([image]).to(device)\n", + "\n", + " if \"input_ids\" in batch_images and hasattr(model.config, \"image_token_id\"):\n", + " image_token_id = model.config.image_token_id\n", + " image_mask = batch_images[\"input_ids\"] == image_token_id\n", + " else:\n", + " image_mask = torch.ones(image_embedding.shape[0], image_embedding.shape[1], dtype=torch.bool, device=device)\n", + "\n", + " num_image_tokens = image_mask.sum().item()\n", + " n_side = int(math.sqrt(num_image_tokens))\n", + "\n", + " if n_side * n_side == num_image_tokens:\n", + " n_patches = (n_side, n_side)\n", + " else:\n", + " n_patches = (16, 16)\n", + "\n", + " similarity_maps_list = get_similarity_maps_from_embeddings(\n", + " image_embeddings=image_embedding,\n", + " query_embeddings=query_embedding,\n", + " n_patches=n_patches,\n", + " image_mask=image_mask,\n", + " )\n", + "\n", + " similarity_map = similarity_maps_list[0]\n", + "\n", + " if similarity_map.dtype == torch.bfloat16:\n", + " similarity_map = similarity_map.float()\n", + " aggregated_map = torch.mean(similarity_map, dim=0)\n", + "\n", + " img_array = np.array(image.convert(\"RGBA\"))\n", + " similarity_map_array = normalize_similarity_map(aggregated_map).to(torch.float32).cpu().numpy()\n", + " similarity_map_array = rearrange(similarity_map_array, \"h w -> w h\")\n", + "\n", + " similarity_map_image = Image.fromarray((similarity_map_array * 255).astype(\"uint8\")).resize(\n", + " image.size, Image.Resampling.BICUBIC\n", + " )\n", + "\n", + " fig, ax = plt.subplots(figsize=(10, 10))\n", + " ax.imshow(img_array)\n", + " ax.imshow(\n", + " similarity_map_image,\n", + " cmap=sns.color_palette(\"mako\", as_cmap=True),\n", + " alpha=0.5,\n", + " )\n", + " ax.set_axis_off()\n", + " plt.tight_layout()\n", + "\n", + " buffer = io.BytesIO()\n", + " plt.savefig(buffer, format=\"png\", dpi=150, bbox_inches=\"tight\", pad_inches=0)\n", + " buffer.seek(0)\n", + " heatmap_image = Image.open(buffer).copy()\n", + " plt.close()\n", + "\n", + " return heatmap_image\n", + "\n", + " except Exception as e:\n", + " print(f\"❌ Heatmap generation error: {str(e)}\")\n", + " return image\n", + "\n", + "\n", + "print(\"βœ… Heatmap function defined!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "index_query_functions", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1f5efe1a-c80b-48f2-9e03-06ce828ffa51" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "βœ… Index and query functions defined!\n" + ] + } + ], + "source": [ + "@torch.inference_mode()\n", + "def index_document(pdf_file, model_choice: str) -> Iterator[str]:\n", + " \"\"\"Upload and index a PDF document with progress updates.\"\"\"\n", + " if pdf_file is None:\n", + " yield \"⚠️ Please upload a PDF document first.\"\n", + " return\n", + "\n", + " try:\n", + " status_messages = []\n", + "\n", + " status_messages.append(\"⏳ Converting PDF to images...\")\n", + " yield \"\\n\".join(status_messages)\n", + "\n", + " doc_index.images = pdf_to_images(pdf_file.name)\n", + " num_pages = len(doc_index.images)\n", + "\n", + " status_messages.append(f\"βœ“ Converted PDF to {num_pages} images\")\n", + "\n", + " if num_pages > MAX_BATCH_SIZE:\n", + " status_messages.append(f\"⚠️ Large PDF ({num_pages} pages). Processing in batches of {MAX_BATCH_SIZE}...\")\n", + " yield \"\\n\".join(status_messages)\n", + "\n", + " if model_choice in [\"NetraEmbed (BiGemma3)\", \"Both\"]:\n", + " if doc_index.bigemma_model is None:\n", + " status_messages.append(\"⏳ Loading BiGemma3 model...\")\n", + " yield \"\\n\".join(status_messages)\n", + " load_bigemma_model()\n", + " status_messages.append(\"βœ“ BiGemma3 loaded\")\n", + " else:\n", + " status_messages.append(\"βœ“ Using cached BiGemma3 model\")\n", + "\n", + " yield \"\\n\".join(status_messages)\n", + "\n", + " model, processor = doc_index.bigemma_model, doc_index.bigemma_processor\n", + "\n", + " status_messages.append(\"⏳ Encoding images with BiGemma3...\")\n", + " yield \"\\n\".join(status_messages)\n", + "\n", + " # Process in batches to avoid OOM on smaller GPUs\n", + " bigemma_embeddings_list = []\n", + " for i in range(0, num_pages, EMBEDDING_BATCH_SIZE):\n", + " batch = doc_index.images[i : i + EMBEDDING_BATCH_SIZE]\n", + " batch_images = processor.process_images(batch).to(device)\n", + " batch_embeddings = model(**batch_images, embedding_dim=768)\n", + " bigemma_embeddings_list.append(batch_embeddings.cpu())\n", + "\n", + " # Clear GPU memory\n", + " del batch_images, batch_embeddings\n", + " if torch.cuda.is_available():\n", + " torch.cuda.empty_cache()\n", + "\n", + " if i % (EMBEDDING_BATCH_SIZE * 5) == 0 and i > 0:\n", + " status_messages[-1] = f\"⏳ Encoding images with BiGemma3... ({i}/{num_pages})\"\n", + " yield \"\\n\".join(status_messages)\n", + "\n", + " doc_index.bigemma_embeddings = torch.cat(bigemma_embeddings_list, dim=0).to(device)\n", + "\n", + " status_messages[-1] = \"βœ“ Indexed with BiGemma3 (shape: {})\".format(doc_index.bigemma_embeddings.shape)\n", + " yield \"\\n\".join(status_messages)\n", + "\n", + " if model_choice in [\"ColNetraEmbed (ColGemma3)\", \"Both\"]:\n", + " if doc_index.colgemma_model is None:\n", + " status_messages.append(\"⏳ Loading ColGemma3 model...\")\n", + " yield \"\\n\".join(status_messages)\n", + " load_colgemma_model()\n", + " status_messages.append(\"βœ“ ColGemma3 loaded\")\n", + " else:\n", + " status_messages.append(\"βœ“ Using cached ColGemma3 model\")\n", + "\n", + " yield \"\\n\".join(status_messages)\n", + "\n", + " model, processor = doc_index.colgemma_model, doc_index.colgemma_processor\n", + "\n", + " status_messages.append(\"⏳ Encoding images with ColGemma3...\")\n", + " yield \"\\n\".join(status_messages)\n", + "\n", + " # Process in batches to avoid OOM on smaller GPUs\n", + " colgemma_embeddings_list = []\n", + " for i in range(0, num_pages, EMBEDDING_BATCH_SIZE):\n", + " batch = doc_index.images[i : i + EMBEDDING_BATCH_SIZE]\n", + " batch_images = processor.process_images(batch).to(device)\n", + " batch_embeddings = model(**batch_images)\n", + " colgemma_embeddings_list.append(batch_embeddings.cpu())\n", + "\n", + " # Clear GPU memory\n", + " del batch_images, batch_embeddings\n", + " if torch.cuda.is_available():\n", + " torch.cuda.empty_cache()\n", + "\n", + " if i % (EMBEDDING_BATCH_SIZE * 5) == 0 and i > 0:\n", + " status_messages[-1] = f\"⏳ Encoding images with ColGemma3... ({i}/{num_pages})\"\n", + " yield \"\\n\".join(status_messages)\n", + "\n", + " doc_index.colgemma_embeddings = torch.cat(colgemma_embeddings_list, dim=0).to(device)\n", + "\n", + " status_messages[-1] = \"βœ“ Indexed with ColGemma3 (shape: {})\".format(doc_index.colgemma_embeddings.shape)\n", + " yield \"\\n\".join(status_messages)\n", + "\n", + " final_status = \"\\n\".join(status_messages) + \"\\n\\nβœ… Document ready for querying!\"\n", + " yield final_status\n", + "\n", + " except Exception as e:\n", + " import traceback\n", + "\n", + " error_details = traceback.format_exc()\n", + " print(f\"Indexing error: {error_details}\")\n", + " yield f\"❌ Error indexing document: {str(e)}\"\n", + "\n", + "\n", + "@torch.inference_mode()\n", + "def query_documents(\n", + " query: str, model_choice: str, top_k: int, show_heatmap: bool = False\n", + ") -> Tuple[Optional[str], Optional[str], Optional[List], Optional[List]]:\n", + " \"\"\"Query the indexed documents.\"\"\"\n", + " if not doc_index.images:\n", + " return \"⚠️ Please upload and index a document first.\", None, None, None\n", + "\n", + " if not query.strip():\n", + " return \"⚠️ Please enter a query.\", None, None, None\n", + "\n", + " try:\n", + " results_bi = None\n", + " results_col = None\n", + " gallery_images_bi = []\n", + " gallery_images_col = []\n", + "\n", + " if model_choice in [\"NetraEmbed (BiGemma3)\", \"Both\"]:\n", + " if doc_index.bigemma_embeddings is None:\n", + " return \"⚠️ Please index the document with BiGemma3 first.\", None, None, None\n", + "\n", + " model, processor = doc_index.bigemma_model, doc_index.bigemma_processor\n", + "\n", + " batch_query = processor.process_texts([query]).to(device)\n", + " query_embedding = model(**batch_query, embedding_dim=768)\n", + "\n", + " scores = processor.score(\n", + " qs=query_embedding,\n", + " ps=doc_index.bigemma_embeddings,\n", + " )\n", + "\n", + " top_k_actual = min(top_k, len(doc_index.images))\n", + " top_indices = scores[0].argsort(descending=True)[:top_k_actual]\n", + "\n", + " results_bi = \"### BiGemma3 (NetraEmbed) Results\\n\\n\"\n", + " for rank, idx in enumerate(top_indices):\n", + " score = scores[0, idx].item()\n", + " results_bi += f\"**Rank {rank + 1}:** Page {idx.item() + 1} - Score: {score:.4f}\\n\"\n", + " gallery_images_bi.append(\n", + " (doc_index.images[idx.item()], f\"Rank {rank + 1} - Page {idx.item() + 1} (Score: {score:.4f})\")\n", + " )\n", + "\n", + " if model_choice in [\"ColNetraEmbed (ColGemma3)\", \"Both\"]:\n", + " if doc_index.colgemma_embeddings is None:\n", + " return \"⚠️ Please index the document with ColGemma3 first.\", None, None, None\n", + "\n", + " model, processor = doc_index.colgemma_model, doc_index.colgemma_processor\n", + "\n", + " batch_query = processor.process_queries([query]).to(device)\n", + " query_embedding = model(**batch_query)\n", + "\n", + " scores = processor.score_multi_vector(\n", + " qs=query_embedding,\n", + " ps=doc_index.colgemma_embeddings,\n", + " )\n", + "\n", + " top_k_actual = min(top_k, len(doc_index.images))\n", + " top_indices = scores[0].argsort(descending=True)[:top_k_actual]\n", + "\n", + " results_col = \"### ColGemma3 (ColNetraEmbed) Results\\n\\n\"\n", + " for rank, idx in enumerate(top_indices):\n", + " score = scores[0, idx].item()\n", + " results_col += f\"**Rank {rank + 1}:** Page {idx.item() + 1} - Score: {score:.2f}\\n\"\n", + "\n", + " if show_heatmap:\n", + " heatmap_image = generate_colgemma_heatmap(\n", + " image=doc_index.images[idx.item()],\n", + " query=query,\n", + " query_embedding=query_embedding,\n", + " image_embedding=doc_index.colgemma_embeddings[idx.item()].unsqueeze(0),\n", + " model=model,\n", + " processor=processor,\n", + " )\n", + " gallery_images_col.append(\n", + " (heatmap_image, f\"Rank {rank + 1} - Page {idx.item() + 1} (Score: {score:.2f})\")\n", + " )\n", + " else:\n", + " gallery_images_col.append(\n", + " (\n", + " doc_index.images[idx.item()],\n", + " f\"Rank {rank + 1} - Page {idx.item() + 1} (Score: {score:.2f})\",\n", + " )\n", + " )\n", + "\n", + " if model_choice == \"NetraEmbed (BiGemma3)\":\n", + " return results_bi, None, gallery_images_bi, None\n", + " elif model_choice == \"ColNetraEmbed (ColGemma3)\":\n", + " return results_col, None, None, gallery_images_col\n", + " else:\n", + " return results_bi, results_col, gallery_images_bi, gallery_images_col\n", + "\n", + " except Exception as e:\n", + " import traceback\n", + "\n", + " error_details = traceback.format_exc()\n", + " print(f\"Query error: {error_details}\")\n", + " return f\"❌ Error during query: {str(e)}\", None, None, None\n", + "\n", + "\n", + "print(\"βœ… Index and query functions defined!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gradio_ui" + }, + "source": [ + "## 4. Create and Launch Gradio Interface" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "gradio_interface", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "835b75c1-70b4-4a06-e270-3746886ab707" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "βœ… UI helper functions defined!\n" + ] + } + ], + "source": [ + "def load_models_with_progress(model_choice: str) -> Iterator[Tuple]:\n", + " \"\"\"Load models with progress updates.\"\"\"\n", + " if not model_choice:\n", + " yield (\n", + " \"❌ Please select a model first.\",\n", + " gr.update(visible=True),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(value=\"Load model first\"),\n", + " )\n", + " return\n", + "\n", + " # Validate GPU memory for \"Both\" option\n", + " if model_choice == \"Both\" and not can_run_both_models:\n", + " yield (\n", + " f\"❌ Insufficient GPU memory ({gpu_memory_gb:.1f}GB) to run both models.\\nPlease select one model at a time.\",\n", + " gr.update(visible=True),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(value=\"Load model first\"),\n", + " )\n", + " return\n", + "\n", + " try:\n", + " status_messages = []\n", + " clear_msg = clear_incompatible_embeddings(model_choice)\n", + " if clear_msg:\n", + " status_messages.append(f\"⚠️ {clear_msg}\")\n", + "\n", + " if model_choice in [\"NetraEmbed (BiGemma3)\", \"Both\"]:\n", + " status_messages.append(\"⏳ Loading BiGemma3 (NetraEmbed)...\")\n", + " yield (\n", + " \"\\n\".join(status_messages),\n", + " gr.update(visible=True),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(value=\"Loading models...\"),\n", + " )\n", + "\n", + " load_bigemma_model()\n", + " status_messages[-1] = \"βœ… BiGemma3 loaded successfully\"\n", + " yield (\n", + " \"\\n\".join(status_messages),\n", + " gr.update(visible=True),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(value=\"Loading models...\"),\n", + " )\n", + "\n", + " if model_choice in [\"ColNetraEmbed (ColGemma3)\", \"Both\"]:\n", + " status_messages.append(\"⏳ Loading ColGemma3 (ColNetraEmbed)...\")\n", + " yield (\n", + " \"\\n\".join(status_messages),\n", + " gr.update(visible=True),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(value=\"Loading models...\"),\n", + " )\n", + "\n", + " load_colgemma_model()\n", + " status_messages[-1] = \"βœ… ColGemma3 loaded successfully\"\n", + " yield (\n", + " \"\\n\".join(status_messages),\n", + " gr.update(visible=True),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(value=\"Loading models...\"),\n", + " )\n", + "\n", + " show_bigemma = model_choice in [\"NetraEmbed (BiGemma3)\", \"Both\"]\n", + " show_colgemma = model_choice in [\"ColNetraEmbed (ColGemma3)\", \"Both\"]\n", + " show_heatmap_checkbox = model_choice in [\"ColNetraEmbed (ColGemma3)\", \"Both\"]\n", + "\n", + " final_status = \"\\n\".join(status_messages) + \"\\n\\nβœ… Ready!\"\n", + " yield (\n", + " final_status,\n", + " gr.update(visible=False),\n", + " gr.update(visible=True),\n", + " gr.update(visible=show_bigemma),\n", + " gr.update(visible=show_colgemma),\n", + " gr.update(visible=show_heatmap_checkbox),\n", + " gr.update(interactive=True),\n", + " gr.update(interactive=True),\n", + " gr.update(interactive=True),\n", + " gr.update(interactive=True),\n", + " gr.update(interactive=True),\n", + " gr.update(value=\"Ready to index\"),\n", + " )\n", + "\n", + " except Exception as e:\n", + " import traceback\n", + "\n", + " error_details = traceback.format_exc()\n", + " print(f\"Model loading error: {error_details}\")\n", + " yield (\n", + " f\"❌ Failed to load models: {str(e)}\",\n", + " gr.update(visible=True),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(value=\"Load model first\"),\n", + " )\n", + "\n", + "\n", + "def unload_models_and_hide_ui():\n", + " \"\"\"Unload models and hide main UI.\"\"\"\n", + " status = unload_models()\n", + " return (\n", + " status,\n", + " gr.update(visible=True),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(interactive=False),\n", + " gr.update(value=\"Load model first\"),\n", + " )\n", + "\n", + "\n", + "def index_with_current_models(pdf_file):\n", + " \"\"\"Index document with currently loaded models.\"\"\"\n", + " if pdf_file is None:\n", + " yield \"⚠️ Please upload a PDF document first.\"\n", + " return\n", + "\n", + " model_choice = get_model_choice_from_loaded()\n", + " if not model_choice:\n", + " yield \"⚠️ No models loaded. Please load a model first.\"\n", + " return\n", + "\n", + " for status in index_document(pdf_file, model_choice):\n", + " yield status\n", + "\n", + "\n", + "def query_with_current_models(query, top_k, show_heatmap):\n", + " \"\"\"Query with currently loaded models.\"\"\"\n", + " model_choice = get_model_choice_from_loaded()\n", + " if not model_choice:\n", + " return \"⚠️ No models loaded. Please load a model first.\", None, None, None\n", + "\n", + " return query_documents(query, model_choice, top_k, show_heatmap)\n", + "\n", + "\n", + "print(\"βœ… UI helper functions defined!\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "launch_gradio", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "14b83908f48d4b128570fe618d4510a7", + "c52e4f27f4f041e2a4356872907c8223", + "d0ead8bec8fc4866a04070c6245912e3", + "e8d83e1ceced4cb4b97463bc8f5fcff1", + "f64385bfadb2415ba186a8759bfc3a45", + "77756585589341489f4b6e85e26582b8", + "9bef3328c56040fdb6412ae1239cbc88", + "b210784d67b147be87f4b7f9ef0c7cc1", + "353a426b0ad44685b07033e8819fff5a", + "1313dd9a697e4d53b59ab9cc847ef0a4", + "9f2a54cd81084f819cf09352c723b15e", + "4beececf1f0e4109888783633c979baa", + "49a03642b3e74064be41fedbb9acfd7f", + "eab0f32f28634d168f3ffbe760e7a116", + "b27dfd99cefd486ca89c150cd717951d", + 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This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n", + "* Running on public URL: https://ba6ec03eaef003496b.gradio.live\n", + "\n", + "This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
" + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Loading BiGemma3 (NetraEmbed)...\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: 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": [ + "preprocessor_config.json: 0%| | 0.00/570 [00:00 https://ba6ec03eaef003496b.gradio.live\n", + "\n", + "================================================================================\n", + "πŸš€ NetraEmbed Gradio Demo is now running!\n", + "================================================================================\n" + ] + } + ], + "source": [ + "# Create Gradio interface\n", + "with gr.Blocks(title=\"NetraEmbed Demo\") as demo:\n", + " # Header section\n", + " with gr.Row():\n", + " with gr.Column(scale=1):\n", + " gr.Markdown(\"# NetraEmbed\")\n", + " gr.HTML(\n", + " \"\"\"\n", + "
\n", + " \n", + " \"Paper\"\n", + " \n", + " \n", + " \"GitHub\"\n", + " \n", + " \n", + " \"Model\"\n", + " \n", + " \n", + " \"Blog\"\n", + " \n", + "
\n", + " \"\"\"\n", + " )\n", + " gr.Markdown(\n", + " \"\"\"\n", + " **πŸš€ Universal Multilingual Multimodal Document Retrieval**\n", + "\n", + " Upload a PDF document, select your model(s), and query using semantic search.\n", + "\n", + " **Available Models:**\n", + " - **NetraEmbed (BiGemma3)**: Single-vector embedding - Fast retrieval\n", + " - **ColNetraEmbed (ColGemma3)**: Multi-vector embedding - High-quality retrieval with heatmaps\n", + " \"\"\"\n", + " )\n", + "\n", + " with gr.Column(scale=1):\n", + " gr.HTML(\n", + " \"\"\"\n", + "
\n", + " \"NetraEmbed\n", + "
\n", + " \"\"\"\n", + " )\n", + "\n", + " gr.Markdown(\"---\")\n", + "\n", + " # Compact 3-column layout\n", + " with gr.Row():\n", + " # Column 1: Model Management\n", + " with gr.Column(scale=1):\n", + " gr.Markdown(\"### πŸ€– Model Management\")\n", + "\n", + " # Conditionally show model options based on GPU memory\n", + " if can_run_both_models:\n", + " model_choices = [\"NetraEmbed (BiGemma3)\", \"ColNetraEmbed (ColGemma3)\", \"Both\"]\n", + " default_choice = \"Both\"\n", + " gpu_info_text = f\"βœ… GPU: {gpu_name} ({gpu_memory_gb:.0f}GB) - Can run both models\"\n", + " else:\n", + " model_choices = [\"NetraEmbed (BiGemma3)\", \"ColNetraEmbed (ColGemma3)\"]\n", + " default_choice = \"NetraEmbed (BiGemma3)\"\n", + " gpu_info_text = f\"⚠️ GPU: {gpu_name} ({gpu_memory_gb:.0f}GB) - Run one model at a time\"\n", + "\n", + " gr.Markdown(f\"**{gpu_info_text}**\")\n", + "\n", + " model_select = gr.Radio(\n", + " choices=model_choices,\n", + " value=default_choice,\n", + " label=\"Select Model(s)\",\n", + " )\n", + "\n", + " load_model_btn = gr.Button(\"πŸ”„ Load Model\", variant=\"primary\", size=\"sm\")\n", + " unload_model_btn = gr.Button(\"πŸ—‘οΈ Unload\", variant=\"secondary\", size=\"sm\")\n", + "\n", + " model_status = gr.Textbox(\n", + " label=\"Status\",\n", + " lines=6,\n", + " interactive=False,\n", + " value=\"Select and load a model\",\n", + " )\n", + "\n", + " loading_info = gr.Markdown(\n", + " f\"\"\"\n", + " **GPU Memory:** {gpu_memory_gb:.1f} GB\n", + " **First load:** 2-3 min\n", + " **Cached:** ~30 sec\n", + " **Batch size:** {EMBEDDING_BATCH_SIZE} pages\n", + " \"\"\",\n", + " visible=True,\n", + " )\n", + "\n", + " # Column 2: Document Upload & Indexing\n", + " with gr.Column(scale=1):\n", + " gr.Markdown(\"### πŸ“„ Upload & Index\")\n", + " pdf_upload = gr.File(label=\"Upload PDF\", file_types=[\".pdf\"], interactive=False)\n", + " index_btn = gr.Button(\"πŸ“₯ Index Document\", variant=\"primary\", size=\"sm\", interactive=False)\n", + "\n", + " index_status = gr.Textbox(\n", + " label=\"Indexing Status\",\n", + " lines=6,\n", + " interactive=False,\n", + " value=\"Load model first\",\n", + " )\n", + "\n", + " # Column 3: Query\n", + " with gr.Column(scale=1):\n", + " gr.Markdown(\"### πŸ”Ž Query Document\")\n", + " query_input = gr.Textbox(\n", + " label=\"Enter Query\",\n", + " placeholder=\"e.g., financial report, organizational structure...\",\n", + " lines=2,\n", + " interactive=False,\n", + " )\n", + "\n", + " with gr.Row():\n", + " top_k_slider = gr.Slider(\n", + " minimum=1,\n", + " maximum=10,\n", + " value=5,\n", + " step=1,\n", + " label=\"Top K\",\n", + " scale=2,\n", + " interactive=False,\n", + " )\n", + " heatmap_checkbox = gr.Checkbox(\n", + " label=\"Heatmaps\",\n", + " value=False,\n", + " visible=False,\n", + " scale=1,\n", + " )\n", + "\n", + " query_btn = gr.Button(\"πŸ” Search\", variant=\"primary\", size=\"sm\", interactive=False)\n", + "\n", + " gr.Markdown(\"---\")\n", + "\n", + " # Results section\n", + " with gr.Column(visible=False) as main_interface:\n", + " gr.Markdown(\"### πŸ“Š Results\")\n", + "\n", + " with gr.Row(equal_height=True):\n", + " with gr.Column(scale=1, visible=False) as bigemma_column:\n", + " bigemma_results = gr.Markdown(\n", + " value=\"*BiGemma3 results will appear here...*\",\n", + " )\n", + " bigemma_gallery = gr.Gallery(\n", + " label=\"BiGemma3 - Top Retrieved Pages\",\n", + " show_label=True,\n", + " columns=2,\n", + " height=\"auto\",\n", + " object_fit=\"contain\",\n", + " )\n", + " with gr.Column(scale=1, visible=False) as colgemma_column:\n", + " colgemma_results = gr.Markdown(\n", + " value=\"*ColGemma3 results will appear here...*\",\n", + " )\n", + " colgemma_gallery = gr.Gallery(\n", + " label=\"ColGemma3 - Top Retrieved Pages\",\n", + " show_label=True,\n", + " columns=2,\n", + " height=\"auto\",\n", + " object_fit=\"contain\",\n", + " )\n", + "\n", + " # Tips\n", + " with gr.Accordion(\"πŸ’‘ Tips\", open=False):\n", + " gr.Markdown(\n", + " \"\"\"\n", + " - **Both models**: Compare results side-by-side\n", + " - **Scores**: BiGemma3 uses cosine similarity (-1 to 1), ColGemma3 uses MaxSim (higher is better)\n", + " - **Heatmaps**: Enable to visualize ColGemma3 attention patterns (brighter = higher attention)\n", + " \"\"\"\n", + " )\n", + "\n", + " # Event handlers\n", + " load_model_btn.click(\n", + " fn=load_models_with_progress,\n", + " inputs=[model_select],\n", + " outputs=[\n", + " model_status,\n", + " loading_info,\n", + " main_interface,\n", + " bigemma_column,\n", + " colgemma_column,\n", + " heatmap_checkbox,\n", + " pdf_upload,\n", + " index_btn,\n", + " query_input,\n", + " top_k_slider,\n", + " query_btn,\n", + " index_status,\n", + " ],\n", + " )\n", + "\n", + " unload_model_btn.click(\n", + " fn=unload_models_and_hide_ui,\n", + " outputs=[\n", + " model_status,\n", + " loading_info,\n", + " main_interface,\n", + " bigemma_column,\n", + " colgemma_column,\n", + " heatmap_checkbox,\n", + " pdf_upload,\n", + " index_btn,\n", + " query_input,\n", + " top_k_slider,\n", + " query_btn,\n", + " index_status,\n", + " ],\n", + " )\n", + "\n", + " index_btn.click(\n", + " fn=index_with_current_models,\n", + " inputs=[pdf_upload],\n", + " outputs=[index_status],\n", + " )\n", + "\n", + " query_btn.click(\n", + " fn=query_with_current_models,\n", + " inputs=[query_input, top_k_slider, heatmap_checkbox],\n", + " outputs=[bigemma_results, colgemma_results, bigemma_gallery, colgemma_gallery],\n", + " )\n", + "\n", + "# Launch the demo\n", + "demo.queue(max_size=20)\n", + "demo.launch(debug=True, share=True)\n", + "\n", + "print(\"\\n\" + \"=\" * 80)\n", + "print(\"πŸš€ NetraEmbed Gradio Demo is now running!\")\n", + "print(\"=\" * 80)" + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "VgVFvDfpqccA" + }, + "execution_count": 8, + "outputs": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + 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