Datasets:

c-i-ber
/

Nova

+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0fsKoLM5sjn_"
+      },
+      "outputs": [],
+      "source": [
+        "# Colab Setup Cell\n",
+        "# Install necessary packages\n",
+        "!pip -q install -U datasets==3.1.0 pillow pyarrow matplotlib tqdm huggingface_hub\n",
+        "\n",
+        "from pathlib import Path\n",
+        "from datasets import load_dataset, Image\n",
+        "import matplotlib.pyplot as plt\n",
+        "import matplotlib.patches as patches\n",
+        "from PIL import Image as PILImage\n",
+        "from tqdm.auto import tqdm\n",
+        "from huggingface_hub import hf_hub_url, notebook_login, hf_hub_download # Use hf_hub_download\n",
+        "import os # Necessary for creating folders\n",
+        "\n",
+        "# --- HUGGING FACE AUTHENTICATION (Keep this) ---\n",
+        "print(\"--- Authenticating with Hugging Face ---\")\n",
+        "notebook_login()\n",
+        "\n",
+        "# --- Configuration ---\n",
+        "DATASET_ID = \"c-i-ber/Nova\"\n",
+        "SPLIT_NAME = \"train\"\n",
+        "PARQUET_FILE_URL = f\"hf://datasets/{DATASET_ID}/data/nova-v1.parquet\"\n",
+        "\n",
+        "# --- LOCAL FOLDER CONSTANT ---\n",
+        "LOCAL_IMAGE_DIR = \"images\" # The folder where images will be saved\n",
+        "\n",
+        "MAX_BOXES_TO_DRAW = 5\n",
+        "\n",
+        "# --- Visualization Style Constants (Unchanged) ---\n",
+        "BOX_COLOR = 'cyan'\n",
+        "TEXT_COLOR = 'black'\n",
+        "BOX_LABEL_BG_COLOR = 'white'\n",
+        "TITLE_FONT_SIZE = 12\n",
+        "METADATA_SECTION_TITLE_SIZE = 12\n",
+        "METADATA_TEXT_SIZE = 10"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# --- HUGGING FACE AUTHENTICATION FIX ---\n",
+        "from huggingface_hub import notebook_login\n",
+        "import os\n",
+        "\n",
+        "# 1. Log in to Hugging Face\n",
+        "print(\"--- Authenticating with Hugging Face ---\")\n",
+        "# This command will open an interactive prompt where you paste your token.\n",
+        "# You can get a token from: https://huggingface.co/settings/tokens\n",
+        "notebook_login()"
+      ],
+      "metadata": {
+        "id": "yRAVEjw-28yU"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def load_and_prepare_dataset(parquet_url: str) -> \"datasets.Dataset\":\n",
+        "    \"\"\"\n",
+        "    Loads the dataset structure (metadata) directly from the remote Parquet file.\n",
+        "    \"\"\"\n",
+        "    print(f\"Loading metadata structure from: {parquet_url}\")\n",
+        "    try:\n",
+        "        ds = load_dataset(\"parquet\", data_files=parquet_url, split=SPLIT_NAME)\n",
+        "    except Exception as e:\n",
+        "        print(f\"\\n🚨 Fatal Error: Could not load Parquet file from URL. Details: {e}\")\n",
+        "        return None\n",
+        "\n",
+        "    print(f\"\\nDataset loaded successfully. Total examples: {len(ds)}\")\n",
+        "    if \"bboxes\" in ds.column_names:\n",
+        "        print(\"✅ SUCCESS: All metadata columns loaded correctly.\")\n",
+        "    else:\n",
+        "        print(\"🚨 CRITICAL ERROR: 'bboxes' column is missing.\")\n",
+        "\n",
+        "    return ds\n",
+        "\n",
+        "\n",
+        "def cache_all_images(dataset: \"datasets.Dataset\", dataset_id: str):\n",
+        "    \"\"\"\n",
+        "    Downloads all images from the HF Hub to the local folder and saves them as JPEGs.\n",
+        "    \"\"\"\n",
+        "    Path(LOCAL_IMAGE_DIR).mkdir(exist_ok=True)\n",
+        "    print(f\"\\n--- Downloading and Caching All Images to: {LOCAL_IMAGE_DIR}/ ---\")\n",
+        "\n",
+        "    for example in tqdm(dataset, desc=\"Downloading and saving images\", unit=\"img\"):\n",
+        "        try:\n",
+        "            # 1. Get the image's remote path and local target path\n",
+        "            image_url_path = example['image_path']\n",
+        "\n",
+        "            # The filename is the part after the last slash\n",
+        "            filename = Path(image_url_path).name\n",
+        "            local_save_path = Path(LOCAL_IMAGE_DIR) / filename\n",
+        "\n",
+        "            # Skip if the file already exists locally\n",
+        "            if local_save_path.exists():\n",
+        "                continue\n",
+        "\n",
+        "            # 2. Download the file's absolute path from HF cache\n",
+        "            # This uses the correct function and full internal path\n",
+        "            repo_file_path = image_url_path\n",
+        "            cached_file_path = hf_hub_download(\n",
+        "                repo_id=dataset_id,\n",
+        "                filename=repo_file_path,\n",
+        "                repo_type=\"dataset\"\n",
+        "            )\n",
+        "\n",
+        "            # 3. Open the downloaded file from the cache and save it to the local folder\n",
+        "            img = PILImage.open(cached_file_path)\n",
+        "            img.save(local_save_path)\n",
+        "\n",
+        "        except Exception as e:\n",
+        "            print(f\"\\nWarning: Could not process image {example.get('filename')}. Error: {e}\")\n",
+        "\n",
+        "    print(\"Caching complete. Demo examples will load instantly from the local folder.\")\n",
+        "\n",
+        "\n",
+        "def load_local_image(filename: str) -> PILImage.Image:\n",
+        "    \"\"\"Loads an image from the local directory using its simple filename.\"\"\"\n",
+        "    local_path = Path(LOCAL_IMAGE_DIR) / filename\n",
+        "    return PILImage.open(local_path)\n",
+        "\n",
+        "\n",
+        "# --- Example Selector Functions (Unchanged) ---\n",
+        "# ... (has_boxes, get_example_by_filename, find_first_example_with_boxes are here) ...\n",
+        "\n",
+        "def has_boxes(example: dict) -> bool:\n",
+        "    bboxes = example.get(\"bboxes\", None)\n",
+        "    return isinstance(bboxes, list) and len(bboxes) > 0\n",
+        "\n",
+        "def get_example_by_filename(dataset: \"datasets.Dataset\", filename: str) -> dict:\n",
+        "    \"\"\"Retrieves an example from the dataset by its 'filename'.\"\"\"\n",
+        "    for i, ex in tqdm(\n",
+        "        enumerate(dataset),\n",
+        "        total=len(dataset),\n",
+        "        desc=f\"Searching for '{filename}'\",\n",
+        "        unit=\"ex\",\n",
+        "        leave=False\n",
+        "    ):\n",
+        "        if ex.get(\"filename\") == filename:\n",
+        "            return dataset[i]\n",
+        "    raise ValueError(f\"Example with filename '{filename}' not found.\")\n",
+        "\n",
+        "def find_first_example_with_boxes(dataset: \"datasets.Dataset\") -> dict:\n",
+        "    \"\"\"Finds the first example in the dataset that contains bounding boxes.\"\"\"\n",
+        "    print(\"Searching for first example with bounding boxes...\")\n",
+        "    for ex in tqdm(dataset, desc=\"Finding example with boxes\", unit=\"ex\"):\n",
+        "        if has_boxes(ex):\n",
+        "            return ex\n",
+        "\n",
+        "    print(\"Warning: No example with bounding boxes found. Displaying index 0 instead.\")\n",
+        "    return dataset[0]"
+      ],
+      "metadata": {
+        "id": "5u2Ezk2YFh3o"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Execute the loading\n",
+        "ds = load_and_prepare_dataset(PARQUET_FILE_URL)\n",
+        "\n",
+        "if ds is None:\n",
+        "    raise RuntimeError(\"Dataset loading failed. Cannot proceed with demo.\")\n",
+        "\n",
+        "# --- STEP 2: CACHE ALL IMAGES ---\n",
+        "cache_all_images(ds, DATASET_ID)"
+      ],
+      "metadata": {
+        "id": "hPB6_pzXIBup"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def display_nova_example(dataset: \"datasets.Dataset\", selector: str | int = None):\n",
+        "    # --- Selection Logic (Unchanged) ---\n",
+        "    example = None\n",
+        "    try:\n",
+        "        if isinstance(selector, int):\n",
+        "            if 0 <= selector < len(dataset):\n",
+        "                example = dataset[selector]\n",
+        "            else:\n",
+        "                raise IndexError(f\"Index {selector} out of bounds for dataset of size {len(dataset)}.\")\n",
+        "        elif isinstance(selector, str):\n",
+        "            example = get_example_by_filename(dataset, selector)\n",
+        "        elif selector is None:\n",
+        "            example = find_first_example_with_boxes(dataset)\n",
+        "        else:\n",
+        "            raise TypeError(f\"Selector must be an integer index or a string filename, got {type(selector)}.\")\n",
+        "\n",
+        "        if example is None: return\n",
+        "\n",
+        "        filename = example.get('filename', 'N/A')\n",
+        "        total_boxes = len(example.get('bboxes', []))\n",
+        "        print(f\"\\n--- Displaying File: {filename} | Total Boxes: {total_boxes} ---\")\n",
+        "\n",
+        "    except (ValueError, IndexError, TypeError) as e:\n",
+        "        print(f\"Error selecting example: {e}\")\n",
+        "        return\n",
+        "\n",
+        "    # --- IMAGE LOADING (FAST, LOCAL) ---\n",
+        "    try:\n",
+        "        # Load the image using its filename from the local folder\n",
+        "        img = load_local_image(filename)\n",
+        "\n",
+        "    except Exception as e:\n",
+        "        print(f\"\\n🛑 Fatal Error: Image for file {filename} could not be loaded from local path. Error: {e}\")\n",
+        "        return\n",
+        "\n",
+        "    # --- Visualization Setup (Plotting - Unchanged) ---\n",
+        "    fig, (ax_img, ax_meta) = plt.subplots(1, 2, figsize=(14, 7), gridspec_kw={'width_ratios': [1, 1]})\n",
+        "\n",
+        "    # Left Subplot: Image and Bounding Boxes\n",
+        "    ax_img.imshow(img)\n",
+        "    bboxes = example.get(\"bboxes\", [])\n",
+        "\n",
+        "    img_title = f\"Image: {filename} ({total_boxes} Boxes)\"\n",
+        "    ax_img.set_title(img_title, fontsize=TITLE_FONT_SIZE, fontweight='bold')\n",
+        "\n",
+        "    # Draw Bounding Boxes\n",
+        "    for i, b in enumerate(bboxes[:MAX_BOXES_TO_DRAW]):\n",
+        "        x, y, w, h = b[\"x\"], b[\"y\"], b[\"width\"], b[\"height\"]\n",
+        "        source = b.get(\"source\", \"N/A\")\n",
+        "\n",
+        "        rect = patches.Rectangle((x, y), w, h,\n",
+        "                                 linewidth=2,\n",
+        "                                 edgecolor=BOX_COLOR,\n",
+        "                                 linestyle='-',\n",
+        "                                 fill=False)\n",
+        "        ax_img.add_patch(rect)\n",
+        "\n",
+        "        ax_img.text(x, y - 5, f\"Source: {source}\",\n",
+        "                fontsize=8,\n",
+        "                color=TEXT_COLOR,\n",
+        "                bbox=dict(facecolor=BOX_LABEL_BG_COLOR, alpha=0.8, edgecolor='none', pad=2))\n",
+        "\n",
+        "    ax_img.axis(\"off\")\n",
+        "\n",
+        "    # Right Subplot: Structured Metadata\n",
+        "    ax_meta.set_axis_off()\n",
+        "    ax_meta.set_xlim(0, 1)\n",
+        "    ax_meta.set_ylim(0, 1)\n",
+        "\n",
+        "    y_pos = 0.95\n",
+        "    x_pos = 0.05\n",
+        "    line_height = 0.08\n",
+        "\n",
+        "    ax_meta.text(x_pos, y_pos, \"Metadata Details\", fontsize=METADATA_SECTION_TITLE_SIZE + 2,\n",
+        "                 fontweight='bold', transform=ax_meta.transAxes)\n",
+        "    y_pos -= line_height * 1.5\n",
+        "\n",
+        "    # Caption\n",
+        "    caption = example.get(\"caption_text\", \"N/A (No caption available)\")\n",
+        "    ax_meta.text(x_pos, y_pos, \"Caption:\", fontsize=METADATA_SECTION_TITLE_SIZE,\n",
+        "                 fontweight='bold', transform=ax_meta.transAxes)\n",
+        "    y_pos -= line_height\n",
+        "\n",
+        "    wrapped_caption = \"\\n\".join([caption[i:i+60] for i in range(0, len(caption), 60)])\n",
+        "    ax_meta.text(x_pos, y_pos, wrapped_caption, fontsize=METADATA_TEXT_SIZE, transform=ax_meta.transAxes,\n",
+        "                 verticalalignment='top')\n",
+        "    y_pos -= line_height * (wrapped_caption.count('\\n') + 2)\n",
+        "\n",
+        "    # Clinical and Publication Details\n",
+        "    meta = example.get(\"meta\", {})\n",
+        "    ax_meta.text(x_pos, y_pos, \"Clinical & Publication:\", fontsize=METADATA_SECTION_TITLE_SIZE,\n",
+        "                 fontweight='bold', transform=ax_meta.transAxes)\n",
+        "    y_pos -= line_height\n",
+        "\n",
+        "    details = [\n",
+        "        (\"Final Diagnosis:\", meta.get('final_diagnosis', 'N/A')),\n",
+        "        (\"Title:\", meta.get('title', 'N/A')),\n",
+        "        (\"Publication Date:\", meta.get('publication_date', 'N/A')),\n",
+        "        (\"Link:\", meta.get('link', 'N/A'))\n",
+        "    ]\n",
+        "\n",
+        "    for label, value in details:\n",
+        "        ax_meta.text(x_pos, y_pos, f\"{label:<18} {value}\", fontsize=METADATA_TEXT_SIZE, transform=ax_meta.transAxes)\n",
+        "        y_pos -= line_height\n",
+        "\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "id": "-QzYNu9dFtG9"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# --- Demo Examples (Instant after initial caching) ---\n",
+        "\n",
+        "# 1. Default Example: Find the first entry that has bounding boxes\n",
+        "print(\"--- DEMO 1: Displaying first example with boxes ---\")\n",
+        "display_nova_example(ds)\n",
+        "\n",
+        "# 2. Select by Integer Index\n",
+        "print(\"\\n--- DEMO 2: Select by Index (95) ---\")\n",
+        "display_nova_example(ds, selector=543)\n",
+        "\n",
+        "# 3. Select by String Filename\n",
+        "print(\"\\n--- DEMO 3: Select by Filename (Logical ID) ---\")\n",
+        "display_nova_example(ds, selector=ds[0]['filename'])"
+      ],
+      "metadata": {
+        "id": "6O1rffbKwOxV"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n"
+      ],
+      "metadata": {
+        "id": "zZ8r9w7YFa6Y"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "mPsz_1vZLIFM"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}