{ "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", "de95da5eaf0d47dfbcf9e7d8e9ea7463", "ccc8b97716b240aead84098242abc7e4", "3fbfb351d3ea495fb266ddb0e3cab6b5", "f291f40c38bc4b3ebce0cee4cd0d4df3", "3c0141ee3b6341aa989edf4009e2fcf2", "2f94768d14c94a6c920768a068411991", "a2b01038320649fbb21221a086319566", "fc0619c2bce0405b8648eaca1d6984b2", "72639caa0cc142f2a3c6e455e3c70eec", "b95d4cdc0c204a54b31c43f44097b49c", "866b4d1beca84ac5a55a3ecf35fae687", "86c6dbf64c7e45bf892ee63a19f394ce", "3b2d64cddcc043cbaea99f281d784c94", "4480c6b6846b4f0b8d56430cb893851f", "514c92f7dbfc4b1abde94bb860a99614", "a6a8f9ce42b8463da7d38a31c79c4375", "08613ab151bf4072b000c83537c941f8", "8a11c89d1e57497fa9d3bd3a83076bc4", "ac8ea416611d4c2db6f7c6d5812f80f7", "5f7d2655d2444bb39abbb21c70294d1f", "0030d0b7b0754dc48a4d658643844317", "3e98529a68a8412e9691f56e53c4c8bf", "d05229a9b69f4603a2d6556f5aa32beb", "be7c299d67584de7a6bdd93fbc08e027", "849121ab21d24a2c8145992665fd2e0b", "e6c4a1e761064aa6a73aa77c8a6f6266", "90ff312a62104e40a3d7eeae98400d89", "bcb8091cea234ac2981a8c89e507d9d5", "5364f388d9074378a27fca506aa2c3e1", 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"cce73f0d6d384f5ca64e20e825b4b384", "905faa2ccbd94808abbb7db054f9386f", "0eb74d061f2045499f1e8868b012b9a6", "e4c1d444a27d4cb3bd040bea91f091d9", "f688726dd7eb404c8898b5e1e6f90be9", "564b589c0abf45e287f6b14aacf3d429", "f7e6dabdbc64415cb45b3986790f2914", "6f6e399875fd4ce2b446b9abb83d91a9", "4d9c48b6189e452cb8f2cb607a8107b5", "16eec3e123e74b5b8b0567ee549cbaf1", "c9006767b4774fe7b6d8853d7a8feda9", "ed970a233e844d34ab543d84d0b5b923", "d660854556aa42559931a05eae4d82c8", "b345020c4fd84f759fe179a059bbe19e", "23e6acc5403e458d9b59fccd3b223f6b", "9cc8d20604c7458b8dbd63445972814e", "5c9ee0ccaf7f4c41b54869549c748dda", "6c55105437144a2e95764a6ad0f28347", "278a2db0361541868af955f53e885fab", "26c92e71e1c64aa6a239e3ee6612701e", "4490365020114b54834b8400fc0846ff", "f307366359c84e4bbfeb442d556d7f0d", "1bfdb6ca17e045f4ad1178156a8070bc", "1bdafb7f21c24becbec1ef8e7641c323", "dbd2a3857c6a40a7bd2e0c2e181be3e5", "776b14b876ee44c5a282667601680656", "0a3a953368b4475cbcdc42b536db0fd3", "c4cda0a119d5447f8e70833158e4c1aa", "30e835fb562840298ac9cd04ab2836b8", "58c7192ae98d45f4b9dc596a7a80c39c", "62d394fab74a4b12ba54be13e2e8518b", "a13525637322433b899079d0dcbc2736", "f00ac8d54ec7499e86e779dd69b4594f", "b1447d49611e406384fb998d67152f97", "f5f749c4f88949ad9674ba2d69d2a27d", "bb4e29cad7f745ff9bafcc85a53386bd", "88d1c1f7280f42b6addf2ba4a05056eb", "da31fdcb8345498295b2ec96f4cf4dfe", "f7aeaa4c687e46f4b47dcbf0716a74c2", "dce71facb7d74915af3dfeccfa03e7c0", "d9e75c41834d4112891c97c4979dca20", "55c99ad84831496e8d37f6c6296a6b63", "d064fbd54ed541c2a3ba2a787000e1d5", "ab4b2f4ca11e4884a61516eaa8d51b19", "8f1b23330de44eecad3db0d38e327116", "c9adf80706024efaa8e45633d6db69f4", "8c74c95dcb164b9594bb42ad0a805798", "0bcce8e8ca894859b2fc86e5008b7fdb", "e3f2ac9d37b44c9e859df4805aa17d2b", "96437de88586496da6e0426134c13839", "b1f745eb38a84de3aca1894b60ae5c4a", "afca67d37eb34759b01ed51597a1e9f1", "d71db55d0dbe44458a9d254dce6495ec", "fae34a0cb5124e19ad7a9a8469001428", "b71ff3ff359343648142b6884349a48d", "9c7f7a5d91f0438c8ab0fd9de4b7e04d", "a845c5a38617407bb494ca93ff42b3ee", "48ae222dc3424dd1a51ccde8036f2416", "d7c9ddcc3b5e4dcea3e738a63a7ce056", "07354ae2c86f4c5e8d00fd59a6ee3541", "89efeb07be184576bd00edca4a1df0a9", "14b20ca07f7b46f4ac621486d0ba9ba3", "125646884f5c4307bdc9570ad8aa1c0e", "d7eaebe01d1d465fbde1857845675d5a", "c808043057cb481fa17e389d39d42e8d", "d124a328e5604294b60c7cf873aaac27", "80417ed3a2de4fdfa32d360eaac956e7" ] }, "outputId": "08cb0075-3d14-4b78-f3e3-7bd998d48f4b" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Colab notebook detected. 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": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "14b83908f48d4b128570fe618d4510a7": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", 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