diff --git "a/tinypress_colab.ipynb" "b/tinypress_colab.ipynb"
--- "a/tinypress_colab.ipynb"
+++ "b/tinypress_colab.ipynb"
@@ -1,336 +1,7606 @@
 {
- "nbformat": 4,
- "nbformat_minor": 5,
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "cell-title",
+      "metadata": {
+        "id": "cell-title"
+      },
+      "source": [
+        "# TinyPress — Prompt Compression Engine\n",
+        "\n",
+        "**HuggingFace Build Small Hackathon · Track: Thousand Token Wood**\n",
+        "\n",
+        "| Layer | Detail |\n",
+        "|-------|--------|\n",
+        "| Compression | `Qwen/Qwen2.5-1.5B-Instruct` (default, switchable) |\n",
+        "| Scoring | `sentence-transformers/all-MiniLM-L6-v2` (default, switchable) |\n",
+        "| UI | Gradio 5 — public share URL |\n",
+        "| Storage | SQLite at `/content/tinypress.db` |\n",
+        "\n",
+        "**Features**\n",
+        "- Compress text to a user-defined token budget\n",
+        "- Live 🔴 / 🟢 compression readiness banner\n",
+        "- Per-token colour highlight panel (toggle on/off)\n",
+        "- Dynamic compression model switching (5 curated <32B models)\n",
+        "- Dynamic scoring embedder switching (6 models, with per-model impact info)\n",
+        "- 👍 / 👎 feedback on every compression result, with optional text comment\n",
+        "- Compression run history persisted to SQLite\n",
+        "- Column picker in History tab — compact default view, expandable to all fields\n",
+        "- Per-row delete in history\n",
+        "- Side-by-side word-level diff viewer with feedback badge and token detail\n",
+        "\n",
+        "> **Recommended runtime:** GPU  →  Runtime → Change runtime type → T4 GPU\n",
+        "\n",
+        "---\n",
+        "\n",
+        "### About the author\n",
+        "\n",
+        "Built by **Sriharsha C R** — AI Engineer, Cloud Native developer, and knowledge sharer.\n",
+        "If this was useful, feel free to connect — always happy to chat about AI, LLMs, or anything in between.\n",
+        "\n",
+        "[![LinkedIn](https://img.shields.io/badge/LinkedIn-sriharsha--cr-0a66c2?logo=linkedin&logoColor=white)](https://www.linkedin.com/in/sriharsha-cr)\n",
+        "[![X / Twitter](https://img.shields.io/badge/X-@sriharsha__cr-000000?logo=x&logoColor=white)](https://x.com/sriharsha_cr)\n",
+        "[![HuggingFace](https://img.shields.io/badge/HuggingFace-sriharsha--cr-ff9d00?logo=huggingface&logoColor=white)](https://huggingface.co/sriharsha-cr)\n",
+        "[![GitHub](https://img.shields.io/badge/GitHub-SriharshaCR-181717?logo=github&logoColor=white)](https://github.com/SriharshaCR)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cell-s1-hdr",
+      "metadata": {
+        "id": "cell-s1-hdr"
+      },
+      "source": [
+        "## Step 1 — Install dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "cell-install",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "collapsed": true,
+        "id": "cell-install",
+        "outputId": "cb76788b-9d5a-4b8a-b107-3d21e06c1bfc"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q \\\n",
+        "    \"gradio==5.0\" \\\n",
+        "    \"transformers>=4.40.0\" \\\n",
+        "    \"sentence-transformers>=3.0.0\" \\\n",
+        "    \"torch>=2.2.0\" \\\n",
+        "    \"numpy>=1.26.0\" \\\n",
+        "    \"pandas>=2.0.0\" \\\n",
+        "    \"accelerate>=0.30.0\" \\\n",
+        "    \"huggingface_hub==0.25.2\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cell-s2-hdr",
+      "metadata": {
+        "id": "cell-s2-hdr"
+      },
+      "source": [
+        "## Step 2 — Runtime check"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "cell-runtime",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cell-runtime",
+        "outputId": "0d5544c5-650d-4aa5-8640-fdd0170f1d6e"
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "\n",
+        "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+        "dtype  = torch.float16 if device == 'cuda' else torch.float32\n",
+        "\n",
+        "print(f'Device : {device}')\n",
+        "if device == 'cuda':\n",
+        "    print(f'GPU    : {torch.cuda.get_device_name(0)}')\n",
+        "    print(f'VRAM   : {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB')\n",
+        "print(f'dtype  : {dtype}')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cell-s3-hdr",
+      "metadata": {
+        "id": "cell-s3-hdr"
+      },
+      "source": [
+        "## Step 3 — Configuration"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "id": "T1fBTJIxdWHP",
+      "metadata": {
+        "id": "T1fBTJIxdWHP"
+      },
+      "outputs": [],
+      "source": [
+        "# Curated <32B open-weight causal LMs for local / Colab inference.\n",
+        "AVAILABLE_MODELS = [\n",
+        "    'Qwen/Qwen2.5-1.5B-Instruct',\n",
+        "    'Qwen/Qwen2.5-0.5B-Instruct',\n",
+        "    'HuggingFaceTB/SmolLM2-1.7B-Instruct',\n",
+        "    'microsoft/Phi-3.5-mini-instruct',\n",
+        "    'meta-llama/Llama-3.2-1B-Instruct',\n",
+        "]\n",
+        "\n",
+        "# Curated sentence-transformer embedding models for quality scoring.\n",
+        "AVAILABLE_EMBEDDER_MODELS = [\n",
+        "    'sentence-transformers/all-MiniLM-L6-v2',\n",
+        "    'sentence-transformers/all-mpnet-base-v2',\n",
+        "    'BAAI/bge-small-en-v1.5',\n",
+        "    'BAAI/bge-base-en-v1.5',\n",
+        "    'mixedbread-ai/mxbai-embed-large-v1',\n",
+        "    'Alibaba-NLP/gte-Qwen2-1.5B-instruct',\n",
+        "]\n",
+        "\n",
+        "EMBEDDER_INFO = {\n",
+        "    'sentence-transformers/all-MiniLM-L6-v2': (\n",
+        "        '⚡ **Fast · 22M params · Default**  \\n'\n",
+        "        'Great baseline. Scores are reliable for typical compression ratios. '\n",
+        "        'Runs comfortably on CPU — minimal overhead.'\n",
+        "    ),\n",
+        "    'sentence-transformers/all-mpnet-base-v2': (\n",
+        "        '⚖️ **Balanced · 110M params**  \\n'\n",
+        "        'Noticeably sharper quality scores than MiniLM, especially on longer texts. '\n",
+        "        'Small speed trade-off; fine on CPU.'\n",
+        "    ),\n",
+        "    'BAAI/bge-small-en-v1.5': (\n",
+        "        '⚡ **Fast · 33M params**  \\n'\n",
+        "        'Strong quality-to-size ratio — often matches MiniLM on accuracy while being '\n",
+        "        'slightly more sensitive to meaning shifts. Good CPU option.'\n",
+        "    ),\n",
+        "    'BAAI/bge-base-en-v1.5': (\n",
+        "        '⚖️ **Balanced · 109M params**  \\n'\n",
+        "        'Consistently strong on semantic similarity benchmarks. '\n",
+        "        'Scores will be more discriminating — small differences in compression quality show up more clearly.'\n",
+        "    ),\n",
+        "    'mixedbread-ai/mxbai-embed-large-v1': (\n",
+        "        '🏆 **High quality · 335M params**  \\n'\n",
+        "        'Top-tier similarity scores. Quality readings will be the most accurate here, '\n",
+        "        'but slower to load and run. GPU recommended.'\n",
+        "    ),\n",
+        "    'Alibaba-NLP/gte-Qwen2-1.5B-instruct': (\n",
+        "        '🔬 **Best quality · 1.5B params**  \\n'\n",
+        "        'Strongest semantic understanding in this list. Scores will reflect subtle meaning loss '\n",
+        "        'that smaller models miss. Requires significant RAM/VRAM — GPU strongly recommended.'\n",
+        "    ),\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "cell-config",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cell-config",
+        "outputId": "f98bce01-b9ec-49dc-8f5a-e58d82e1de69"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "LLM_MODEL      = os.getenv('LLM_MODEL',      AVAILABLE_MODELS[1])\n",
+        "EMBEDDER_MODEL = os.getenv('EMBEDDER_MODEL', AVAILABLE_EMBEDDER_MODELS[0])\n",
+        "DB_PATH        = os.getenv('DB_PATH',        '/content/tinypress.db')\n",
+        "SERVER_PORT    = int(os.getenv('PORT', 7860))\n",
+        "\n",
+        "DEFAULT_TARGET_TOKENS = 500\n",
+        "MAX_NEW_TOKENS        = 1024\n",
+        "APP_TITLE             = 'TinyPress'\n",
+        "\n",
+        "PUBLIC_UI = True\n",
+        "\n",
+        "print(f'LLM      : {LLM_MODEL}')\n",
+        "print(f'Embedder : {EMBEDDER_MODEL}')\n",
+        "print(f'DB       : {DB_PATH}')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cell-s4-hdr",
+      "metadata": {
+        "id": "cell-s4-hdr"
+      },
+      "source": [
+        "## Step 4 — Model loader"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "cell-model-loader",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cell-model-loader",
+        "outputId": "e6c0f401-e6c9-4eee-d577-8831907a7a36"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
+        "from sentence_transformers import SentenceTransformer\n",
+        "import gc\n",
+        "\n",
+        "_llm               = None\n",
+        "_tokenizer         = None\n",
+        "_embedder          = None\n",
+        "_current_model_id    = None\n",
+        "_current_embedder_id = None\n",
+        "\n",
+        "\n",
+        "def get_current_model_id():\n",
+        "    return _current_model_id\n",
+        "\n",
+        "\n",
+        "def get_current_tokenizer_id():\n",
+        "    # Tokenizer is always loaded from the same HF repo as the model.\n",
+        "    return _current_model_id\n",
+        "\n",
+        "\n",
+        "def get_current_embedder_id():\n",
+        "    return _current_embedder_id\n",
+        "\n",
+        "\n",
+        "def get_llm():\n",
+        "    global _llm, _tokenizer\n",
+        "    if _llm is None:\n",
+        "        _load_llm(LLM_MODEL)\n",
+        "    return _llm, _tokenizer\n",
+        "\n",
+        "\n",
+        "def switch_llm(model_id: str) -> str:\n",
+        "    global _current_model_id\n",
+        "    if _current_model_id == model_id:\n",
+        "        return f'Already using {model_id}'\n",
+        "    _unload_llm()\n",
+        "    _load_llm(model_id)\n",
+        "    return f'Loaded: {model_id}'\n",
+        "\n",
+        "\n",
+        "def _load_llm(model_id: str):\n",
+        "    \"\"\"Load model + its paired tokenizer. Both come from the same model_id.\"\"\"\n",
+        "    global _llm, _tokenizer, _current_model_id\n",
+        "    print(f'Loading LLM: {model_id} ...')\n",
+        "    _tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
+        "    _llm = AutoModelForCausalLM.from_pretrained(\n",
+        "        model_id,\n",
+        "        torch_dtype=dtype,\n",
+        "        device_map='auto',\n",
+        "    )\n",
+        "    _llm.eval()\n",
+        "    _current_model_id = model_id\n",
+        "    print(f'LLM ready: {model_id}')\n",
+        "\n",
+        "\n",
+        "def _unload_llm():\n",
+        "    \"\"\"Free GPU/CPU memory before loading a different model.\"\"\"\n",
+        "    global _llm, _tokenizer, _current_model_id\n",
+        "    del _llm, _tokenizer\n",
+        "    _llm = None\n",
+        "    _tokenizer = None\n",
+        "    _current_model_id = None\n",
+        "    gc.collect()\n",
+        "    if torch.cuda.is_available():\n",
+        "        torch.cuda.empty_cache()\n",
+        "\n",
+        "\n",
+        "def get_embedder():\n",
+        "    global _embedder, _current_embedder_id\n",
+        "    if _embedder is None:\n",
+        "        _load_embedder(EMBEDDER_MODEL)\n",
+        "    return _embedder\n",
+        "\n",
+        "\n",
+        "def switch_embedder(model_id: str) -> str:\n",
+        "    global _current_embedder_id\n",
+        "    if _current_embedder_id == model_id:\n",
+        "        return f'Already using {model_id}'\n",
+        "    _unload_embedder()\n",
+        "    _load_embedder(model_id)\n",
+        "    return f'Loaded: {model_id}'\n",
+        "\n",
+        "\n",
+        "def _load_embedder(model_id: str):\n",
+        "    global _embedder, _current_embedder_id\n",
+        "    print(f'Loading embedder: {model_id} ...')\n",
+        "    # Explicitly set device to 'cpu' to avoid ZeroGPU conflicts\n",
+        "    _embedder = SentenceTransformer(model_id, device='cpu')\n",
+        "    _current_embedder_id = model_id\n",
+        "    print(f'Embedder ready: {model_id}')\n",
+        "\n",
+        "\n",
+        "def _unload_embedder():\n",
+        "    global _embedder, _current_embedder_id\n",
+        "    del _embedder\n",
+        "    _embedder = None\n",
+        "    _current_embedder_id = None\n",
+        "    gc.collect()\n",
+        "    if torch.cuda.is_available():\n",
+        "        torch.cuda.empty_cache()\n",
+        "\n",
+        "\n",
+        "print('Model loader defined.')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cell-s5-hdr",
+      "metadata": {
+        "id": "cell-s5-hdr"
+      },
+      "source": [
+        "## Step 5 — Core pipeline"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "cell-core",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cell-core",
+        "outputId": "2092a376-eebe-4b70-e600-4d1f684222b4"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "\n",
+        "# ── tokenizer utils ───────────────────────────────────────────────────────────\n",
+        "\n",
+        "def count_tokens(text: str) -> int:\n",
+        "    _, tokenizer = get_llm()\n",
+        "    return len(tokenizer.encode(text, add_special_tokens=False))\n",
+        "\n",
+        "\n",
+        "def get_token_strings(text: str) -> list:\n",
+        "    \"\"\"Return the decoded surface string for every token in text.\"\"\"\n",
+        "    _, tokenizer = get_llm()\n",
+        "    ids = tokenizer.encode(text, add_special_tokens=False)\n",
+        "    return [tokenizer.decode([i]) for i in ids]\n",
+        "\n",
+        "\n",
+        "# ── compressor ────────────────────────────────────────────────────────────────\n",
+        "\n",
+        "_PROMPT_TEMPLATE = (\n",
+        "    'You are a lossless compression assistant. '\n",
+        "    'Compress the following text to at most {target} tokens.\\n'\n",
+        "    'Preserve all key facts, decisions, and intent. '\n",
+        "    'Do not add commentary. Output only the compressed text.\\n\\n'\n",
+        "    'TEXT:\\n{text}\\n\\nCOMPRESSED:'\n",
+        ")\n",
+        "\n",
+        "\n",
+        "def _generate(prompt: str) -> str:\n",
+        "    model, tokenizer = get_llm()\n",
+        "    inputs = tokenizer(prompt, return_tensors='pt').to(model.device)\n",
+        "    with torch.no_grad():\n",
+        "        output_ids = model.generate(\n",
+        "            **inputs,\n",
+        "            max_new_tokens=MAX_NEW_TOKENS,\n",
+        "            do_sample=False,\n",
+        "            pad_token_id=tokenizer.eos_token_id,\n",
+        "        )\n",
+        "    new_tokens = output_ids[0][inputs['input_ids'].shape[1]:]\n",
+        "    return tokenizer.decode(new_tokens, skip_special_tokens=True).strip()\n",
+        "\n",
+        "\n",
+        "def compress(text: str, target_tokens: int) -> tuple:\n",
+        "    \"\"\"Returns (compressed_text, input_token_count, output_token_count).\"\"\"\n",
+        "    input_tokens = count_tokens(text)\n",
+        "    if input_tokens <= target_tokens:\n",
+        "        return text, input_tokens, input_tokens\n",
+        "\n",
+        "    prompt     = _PROMPT_TEMPLATE.format(target=target_tokens, text=text)\n",
+        "    compressed = _generate(prompt)\n",
+        "\n",
+        "    # Hard-trim if model overshoots.\n",
+        "    _, tokenizer = get_llm()\n",
+        "    ids = tokenizer.encode(compressed, add_special_tokens=False)\n",
+        "    if len(ids) > target_tokens:\n",
+        "        compressed = tokenizer.decode(ids[:target_tokens], skip_special_tokens=True)\n",
+        "\n",
+        "    output_tokens = count_tokens(compressed)\n",
+        "    return compressed, input_tokens, output_tokens\n",
+        "\n",
+        "\n",
+        "# ── scorer ────────────────────────────────────────────────────────────────────\n",
+        "\n",
+        "def semantic_score(original: str, compressed: str) -> float:\n",
+        "    embedder = get_embedder()\n",
+        "    vecs = embedder.encode([original, compressed], convert_to_numpy=True)\n",
+        "    cos  = float(\n",
+        "        np.dot(vecs[0], vecs[1]) / (np.linalg.norm(vecs[0]) * np.linalg.norm(vecs[1]))\n",
+        "    )\n",
+        "    return round(max(0.0, min(1.0, cos)), 4)\n",
+        "\n",
+        "\n",
+        "print('Core pipeline defined.')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cell-s6-hdr",
+      "metadata": {
+        "id": "cell-s6-hdr"
+      },
+      "source": [
+        "## Step 6 — Diff renderer"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "cell-diff",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cell-diff",
+        "outputId": "34230370-85e9-4d36-fd28-1e8542ffa448"
+      },
+      "outputs": [],
+      "source": [
+        "import difflib\n",
+        "import html as _h\n",
+        "\n",
+        "\n",
+        "def _word_diff(original: str, compressed: str) -> tuple:\n",
+        "    \"\"\"\n",
+        "    Word-level SequenceMatcher diff.\n",
+        "    Returns (annotated_original_html, annotated_compressed_html).\n",
+        "    Colour key:\n",
+        "      original  — red strikethrough = dropped\n",
+        "      compressed — amber            = rewritten\n",
+        "      compressed — green            = inserted\n",
+        "      plain                         = unchanged\n",
+        "    \"\"\"\n",
+        "    orig_words = original.split()\n",
+        "    comp_words = compressed.split()\n",
+        "    matcher = difflib.SequenceMatcher(None, orig_words, comp_words, autojunk=False)\n",
+        "\n",
+        "    orig_parts, comp_parts = [], []\n",
+        "\n",
+        "    for tag, i1, i2, j1, j2 in matcher.get_opcodes():\n",
+        "        ow = _h.escape(' '.join(orig_words[i1:i2]))\n",
+        "        cw = _h.escape(' '.join(comp_words[j1:j2]))\n",
+        "\n",
+        "        if tag == 'equal':\n",
+        "            orig_parts.append(ow)\n",
+        "            comp_parts.append(cw)\n",
+        "\n",
+        "        elif tag == 'delete':\n",
+        "            orig_parts.append(\n",
+        "                f'<mark style=\"background:#fee2e2;color:#b91c1c;'\n",
+        "                f'text-decoration:line-through;padding:1px 3px;border-radius:3px\">{ow}</mark>'\n",
+        "            )\n",
+        "\n",
+        "        elif tag == 'insert':\n",
+        "            comp_parts.append(\n",
+        "                f'<mark style=\"background:#dcfce7;color:#15803d;'\n",
+        "                f'padding:1px 3px;border-radius:3px\">{cw}</mark>'\n",
+        "            )\n",
+        "\n",
+        "        elif tag == 'replace':\n",
+        "            orig_parts.append(\n",
+        "                f'<mark style=\"background:#fee2e2;color:#b91c1c;'\n",
+        "                f'text-decoration:line-through;padding:1px 3px;border-radius:3px\">{ow}</mark>'\n",
+        "            )\n",
+        "            comp_parts.append(\n",
+        "                f'<mark style=\"background:#fef9c3;color:#92400e;'\n",
+        "                f'padding:1px 3px;border-radius:3px\">{cw}</mark>'\n",
+        "            )\n",
+        "\n",
+        "    return ' '.join(orig_parts), ' '.join(comp_parts)\n",
+        "\n",
+        "\n",
+        "def render_diff_html(record: dict) -> str:\n",
+        "    \"\"\"Build a self-contained side-by-side diff HTML block for a compression run.\"\"\"\n",
+        "    original   = record.get('input_text', '')\n",
+        "    compressed = record.get('output_text', '')\n",
+        "    if not original or not compressed:\n",
+        "        return ''\n",
+        "\n",
+        "    orig_html, comp_html = _word_diff(original, compressed)\n",
+        "\n",
+        "    model      = _h.escape(record.get('model', '—'))\n",
+        "    tokenizer  = _h.escape(record.get('tokenizer', '—'))\n",
+        "    ts         = _h.escape(record.get('timestamp', '—'))\n",
+        "    in_tok     = record.get('input_tokens',  '—')\n",
+        "    out_tok    = record.get('output_tokens', '—')\n",
+        "    target_tok = record.get('target_tokens', '—')\n",
+        "    ratio      = record.get('compression_ratio', 0)\n",
+        "    quality    = record.get('quality_score', 0)\n",
+        "    duration   = record.get('duration_ms', '—')\n",
+        "    run_id     = record.get('id', '—')\n",
+        "\n",
+        "    feedback_val  = record.get('feedback')\n",
+        "    feedback_note = _h.escape(record.get('feedback_comment') or '')\n",
+        "\n",
+        "    # Build optional feedback block\n",
+        "    if feedback_val is not None:\n",
+        "        badge_bg    = '#f0fdf4' if feedback_val == 1 else '#fef2f2'\n",
+        "        badge_color = '#15803d' if feedback_val == 1 else '#b91c1c'\n",
+        "        badge_text  = '👍 Helpful' if feedback_val == 1 else '👎 Not helpful'\n",
+        "        feedback_block = (\n",
+        "            f'<div style=\"display:flex;flex-wrap:wrap;align-items:center;gap:8px;'\n",
+        "            f'margin-top:10px;padding:8px 12px;border-radius:6px;background:{badge_bg}\">'\n",
+        "            f'<span style=\"font-weight:600;font-size:0.8rem;color:{badge_color}\">{badge_text}</span>'\n",
+        "        )\n",
+        "        if feedback_note:\n",
+        "            feedback_block += (\n",
+        "                f'<span style=\"font-size:0.8rem;color:#374151;font-style:italic\">'\n",
+        "                f'\"{feedback_note}\"</span>'\n",
+        "            )\n",
+        "        feedback_block += '</div>'\n",
+        "    else:\n",
+        "        feedback_block = ''\n",
+        "\n",
+        "    return f\"\"\"\n",
+        "<div style=\"font-family:system-ui,sans-serif;margin-top:4px\">\n",
+        "\n",
+        "  <!-- Primary meta chips -->\n",
+        "  <div style=\"display:flex;flex-wrap:wrap;gap:6px;margin-bottom:6px;font-size:0.78rem\">\n",
+        "    <span style=\"background:#f3f4f6;padding:3px 9px;border-radius:12px;color:#374151\">Run #{run_id}</span>\n",
+        "    <span style=\"background:#f3f4f6;padding:3px 9px;border-radius:12px;color:#374151\">{ts}</span>\n",
+        "    <span style=\"background:#eff6ff;padding:3px 9px;border-radius:12px;color:#1d4ed8\">{model}</span>\n",
+        "    <span style=\"background:#f0fdf4;padding:3px 9px;border-radius:12px;color:#15803d\">Quality {quality:.4f}</span>\n",
+        "    <span style=\"background:#fff7ed;padding:3px 9px;border-radius:12px;color:#c2410c\">Ratio {ratio:.4f}</span>\n",
+        "    <span style=\"background:#faf5ff;padding:3px 9px;border-radius:12px;color:#7e22ce\">&#9201; {duration} ms</span>\n",
+        "  </div>\n",
+        "\n",
+        "  <!-- Secondary meta chips -->\n",
+        "  <div style=\"display:flex;flex-wrap:wrap;gap:6px;margin-bottom:12px;font-size:0.78rem\">\n",
+        "    <span style=\"background:#f3f4f6;padding:3px 9px;border-radius:12px;color:#374151\">{in_tok} in → {out_tok} out (target {target_tok})</span>\n",
+        "    <span style=\"background:#f3f4f6;padding:3px 9px;border-radius:12px;color:#374151\">tokenizer: {tokenizer}</span>\n",
+        "  </div>\n",
+        "\n",
+        "  <!-- Side-by-side panels -->\n",
+        "  <div style=\"display:grid;grid-template-columns:1fr 1fr;gap:12px\">\n",
+        "    <div style=\"border:1px solid #fecaca;border-radius:8px;overflow:hidden\">\n",
+        "      <div style=\"background:#fef2f2;padding:8px 14px;border-bottom:1px solid #fecaca;\n",
+        "                  display:flex;justify-content:space-between;align-items:center\">\n",
+        "        <span style=\"font-weight:700;font-size:0.8rem;color:#b91c1c\">ORIGINAL</span>\n",
+        "        <span style=\"font-size:0.75rem;color:#6b7280\">{in_tok} tokens</span>\n",
+        "      </div>\n",
+        "      <div style=\"padding:14px;line-height:1.8;font-size:0.875rem;color:#1a1a1a;\n",
+        "                  max-height:340px;overflow-y:auto;word-break:break-word\">{orig_html}</div>\n",
+        "    </div>\n",
+        "    <div style=\"border:1px solid #bbf7d0;border-radius:8px;overflow:hidden\">\n",
+        "      <div style=\"background:#f0fdf4;padding:8px 14px;border-bottom:1px solid #bbf7d0;\n",
+        "                  display:flex;justify-content:space-between;align-items:center\">\n",
+        "        <span style=\"font-weight:700;font-size:0.8rem;color:#15803d\">COMPRESSED</span>\n",
+        "        <span style=\"font-size:0.75rem;color:#6b7280\">{out_tok} tokens</span>\n",
+        "      </div>\n",
+        "      <div style=\"padding:14px;line-height:1.8;font-size:0.875rem;color:#1a1a1a;\n",
+        "                  max-height:340px;overflow-y:auto;word-break:break-word\">{comp_html}</div>\n",
+        "    </div>\n",
+        "  </div>\n",
+        "\n",
+        "  {feedback_block}\n",
+        "\n",
+        "  <!-- Legend -->\n",
+        "  <div style=\"display:flex;flex-wrap:wrap;gap:14px;margin-top:10px;font-size:0.75rem;color:#6b7280;align-items:center\">\n",
+        "    <mark style=\"background:#fee2e2;color:#b91c1c;text-decoration:line-through;padding:2px 7px;border-radius:3px\">dropped</mark>\n",
+        "    <mark style=\"background:#fef9c3;color:#92400e;padding:2px 7px;border-radius:3px\">rewritten</mark>\n",
+        "    <mark style=\"background:#dcfce7;color:#15803d;padding:2px 7px;border-radius:3px\">inserted</mark>\n",
+        "    <span>plain = unchanged</span>\n",
+        "  </div>\n",
+        "\n",
+        "</div>\n",
+        "\"\"\"\n",
+        "\n",
+        "\n",
+        "print('Diff renderer defined.')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cell-s7-hdr",
+      "metadata": {
+        "id": "cell-s7-hdr"
+      },
+      "source": [
+        "## Step 7 — Database"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "cell-db",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cell-db",
+        "outputId": "b88b9cfb-edcc-4c15-e766-34207b320651"
+      },
+      "outputs": [],
+      "source": [
+        "import sqlite3\n",
+        "\n",
+        "_SCHEMA = \"\"\"\n",
+        "CREATE TABLE IF NOT EXISTS compression_runs (\n",
+        "    id                INTEGER PRIMARY KEY AUTOINCREMENT,\n",
+        "    timestamp         TEXT    NOT NULL,\n",
+        "    model             TEXT    NOT NULL,\n",
+        "    tokenizer         TEXT    NOT NULL,\n",
+        "    input_tokens      INTEGER NOT NULL,\n",
+        "    output_tokens     INTEGER NOT NULL,\n",
+        "    target_tokens     INTEGER NOT NULL,\n",
+        "    compression_ratio REAL    NOT NULL,\n",
+        "    quality_score     REAL    NOT NULL,\n",
+        "    duration_ms       REAL    NOT NULL,\n",
+        "    input_text        TEXT    NOT NULL,\n",
+        "    output_text       TEXT    NOT NULL,\n",
+        "    feedback          INTEGER,\n",
+        "    feedback_comment  TEXT\n",
+        ");\n",
+        "\"\"\"\n",
+        "\n",
+        "\n",
+        "def _connect():\n",
+        "    conn = sqlite3.connect(DB_PATH)\n",
+        "    conn.row_factory = sqlite3.Row\n",
+        "    return conn\n",
+        "\n",
+        "\n",
+        "def init_db():\n",
+        "    conn = _connect()\n",
+        "    conn.executescript(_SCHEMA)\n",
+        "    for col, typedef in [\n",
+        "        ('tokenizer',        'TEXT NOT NULL DEFAULT \"\"'),\n",
+        "        ('duration_ms',      'REAL NOT NULL DEFAULT 0'),\n",
+        "        ('feedback',         'INTEGER'),\n",
+        "        ('feedback_comment', 'TEXT'),\n",
+        "    ]:\n",
+        "        try:\n",
+        "            conn.execute(f'ALTER TABLE compression_runs ADD COLUMN {col} {typedef}')\n",
+        "        except sqlite3.OperationalError:\n",
+        "            pass\n",
+        "    conn.commit()\n",
+        "    conn.close()\n",
+        "\n",
+        "\n",
+        "def save_run(record: dict) -> int:\n",
+        "    conn = _connect()\n",
+        "    cursor = conn.execute(\n",
+        "        '''\n",
+        "        INSERT INTO compression_runs\n",
+        "            (timestamp, model, tokenizer, input_tokens, output_tokens, target_tokens,\n",
+        "             compression_ratio, quality_score, duration_ms, input_text, output_text)\n",
+        "        VALUES\n",
+        "            (:timestamp, :model, :tokenizer, :input_tokens, :output_tokens, :target_tokens,\n",
+        "             :compression_ratio, :quality_score, :duration_ms, :input_text, :output_text)\n",
+        "        ''',\n",
+        "        record,\n",
+        "    )\n",
+        "    run_id = cursor.lastrowid\n",
+        "    conn.commit()\n",
+        "    conn.close()\n",
+        "    return run_id\n",
+        "\n",
+        "\n",
+        "def update_feedback(run_id: int, value: int):\n",
+        "    conn = _connect()\n",
+        "    conn.execute('UPDATE compression_runs SET feedback = ? WHERE id = ?', (value, run_id))\n",
+        "    conn.commit()\n",
+        "    conn.close()\n",
+        "\n",
+        "\n",
+        "def update_feedback_comment(run_id: int, comment: str):\n",
+        "    conn = _connect()\n",
+        "    conn.execute('UPDATE compression_runs SET feedback_comment = ? WHERE id = ?', (comment, run_id))\n",
+        "    conn.commit()\n",
+        "    conn.close()\n",
+        "\n",
+        "\n",
+        "def delete_run(run_id: int):\n",
+        "    conn = _connect()\n",
+        "    conn.execute('DELETE FROM compression_runs WHERE id = ?', (run_id,))\n",
+        "    conn.commit()\n",
+        "    conn.close()\n",
+        "\n",
+        "\n",
+        "def get_run(run_id: int):\n",
+        "    conn = _connect()\n",
+        "    row = conn.execute('SELECT * FROM compression_runs WHERE id = ?', (run_id,)).fetchone()\n",
+        "    conn.close()\n",
+        "    return dict(row) if row else None\n",
+        "\n",
+        "\n",
+        "def get_runs(limit: int = 100) -> list:\n",
+        "    conn = _connect()\n",
+        "    rows = conn.execute(\n",
+        "        'SELECT * FROM compression_runs ORDER BY id DESC LIMIT ?', (limit,)\n",
+        "    ).fetchall()\n",
+        "    conn.close()\n",
+        "    return [dict(r) for r in rows]\n",
+        "\n",
+        "\n",
+        "init_db()\n",
+        "print(f'Database ready at {DB_PATH}')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cell-s8-hdr",
+      "metadata": {
+        "id": "cell-s8-hdr"
+      },
+      "source": [
+        "## Step 8 — Load models\n",
+        "\n",
+        "Downloads and caches weights. GPU warm-cache: ~30 s. First run: a few minutes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "cell-load-models",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 697,
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+            "9a18feb0bc4e4dd699fd725a8a7b2d63",
+            "830145c2ea78403291bc4d2e563b357e"
+          ]
+        },
+        "collapsed": true,
+        "id": "cell-load-models",
+        "outputId": "c3dcc479-bd58-427b-cfdd-008d12973b82"
+      },
+      "outputs": [],
+      "source": [
+        "get_llm()\n",
+        "get_embedder()\n",
+        "print('\\nAll models loaded and ready.')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cell-s9-hdr",
+      "metadata": {
+        "id": "cell-s9-hdr"
+      },
+      "source": [
+        "## Step 9 — Launch Gradio UI\n",
+        "\n",
+        "Prints a **public share URL** when ready. All features are live in the UI."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "id": "cell-gradio",
+      "metadata": {
+        "id": "cell-gradio"
+      },
+      "outputs": [],
+      "source": [
+        "import html as _h\n",
+        "import time\n",
+        "from datetime import datetime, timezone\n",
+        "\n",
+        "import gradio as gr\n",
+        "import pandas as pd\n",
+        "\n",
+        "\n",
+        "# ══════════════════════════════════════════════════════════════════════════════\n",
+        "# COMPRESS TAB — handlers\n",
+        "# ══════════════════════════════════════════════════════════════════════════════\n",
+        "\n",
+        "_PALETTE = [\n",
+        "    '#fde68a', '#bbf7d0', '#bfdbfe', '#fecaca', '#e9d5ff',\n",
+        "    '#fed7aa', '#99f6e4', '#e0e7ff', '#fce7f3', '#d1fae5',\n",
+        "]\n",
+        "_BTN_SHOW = '🔍  Show Token Highlights'\n",
+        "_BTN_HIDE = '🙈  Hide Token Highlights'\n",
+        "\n",
+        "\n",
+        "def _render_token_html(text: str) -> str:\n",
+        "    if not text.strip():\n",
+        "        return ''\n",
+        "    tokens = get_token_strings(text)\n",
+        "    if not tokens:\n",
+        "        return ''\n",
+        "    spans = []\n",
+        "    for i, tok in enumerate(tokens):\n",
+        "        color   = _PALETTE[i % len(_PALETTE)]\n",
+        "        display = _h.escape(tok).replace(' ', '<span style=\"opacity:0.35;font-size:0.7em\">·</span>')\n",
+        "        spans.append(\n",
+        "            f'<span title=\"token {i+1}\" '\n",
+        "            f'style=\"background:{color};border-radius:4px;padding:2px 5px;'\n",
+        "            f'font-family:\\'Courier New\\',monospace;font-size:0.8rem;'\n",
+        "            f'line-height:2.2;margin:2px 1px;display:inline-block;'\n",
+        "            f'cursor:default;border:1px solid rgba(0,0,0,0.06)\">{display}</span>'\n",
+        "        )\n",
+        "    return (\n",
+        "        '<div style=\"font-family:system-ui,sans-serif;padding:10px 12px;'\n",
+        "        'border:1px solid #e5e7eb;border-radius:8px;background:#fafafa\">'\n",
+        "        f'<div style=\"font-size:0.75rem;color:#6b7280;margin-bottom:8px;font-weight:500\">'\n",
+        "        f'{len(tokens)} tokens — each chip = one token, hover for index</div>'\n",
+        "        '<div style=\"line-height:2.6;word-break:break-all;max-height:200px;overflow-y:auto\">'\n",
+        "        + ''.join(spans) + '</div></div>'\n",
+        "    )\n",
+        "\n",
+        "\n",
+        "def toggle_token_panel(is_visible: bool, text: str):\n",
+        "    new_visible  = not is_visible\n",
+        "    html_content = _render_token_html(text) if new_visible else ''\n",
+        "    btn_label    = _BTN_HIDE if new_visible else _BTN_SHOW\n",
+        "    return new_visible, html_content, gr.update(value=btn_label)\n",
+        "\n",
+        "\n",
+        "def update_token_panel(text: str, is_visible: bool) -> str:\n",
+        "    return _render_token_html(text) if is_visible else ''\n",
+        "\n",
+        "\n",
+        "_STATUS_EMPTY = '<span></span>'\n",
+        "_STATUS_RED = (\n",
+        "    '<div style=\"background:#fee2e2;border:1px solid #ef4444;color:#b91c1c;'\n",
+        "    'padding:8px 12px;border-radius:6px;font-size:0.9rem;\">'\n",
+        "    '🔴 <strong>Compression not needed</strong> — input ({input_tok} tokens) '\n",
+        "    'is already within the {budget}-token budget.</div>'\n",
+        ")\n",
+        "_STATUS_GREEN = (\n",
+        "    '<div style=\"background:#dcfce7;border:1px solid #22c55e;color:#15803d;'\n",
+        "    'padding:8px 12px;border-radius:6px;font-size:0.9rem;\">'\n",
+        "    '🟢 <strong>Ready to compress</strong> — {input_tok} tokens → {budget} token budget '\n",
+        "    '({delta} tokens to shed).</div>'\n",
+        ")\n",
+        "\n",
+        "\n",
+        "def compression_status(text: str, target_tokens: int) -> str:\n",
+        "    if not text.strip():\n",
+        "        return _STATUS_EMPTY\n",
+        "    n = count_tokens(text)\n",
+        "    if n <= int(target_tokens):\n",
+        "        return _STATUS_RED.format(input_tok=n, budget=int(target_tokens))\n",
+        "    return _STATUS_GREEN.format(input_tok=n, budget=int(target_tokens), delta=n - int(target_tokens))\n",
+        "\n",
+        "\n",
+        "def run_compression(text: str, target_tokens: int):\n",
+        "    _hidden = gr.update(visible=False)\n",
+        "    if not text.strip():\n",
+        "        return ('', 0, 0, 0, 0.0, None,\n",
+        "                _hidden, _hidden, gr.update(value='', visible=False),\n",
+        "                gr.update(value='', visible=False), _hidden, gr.update(value='', visible=False))\n",
+        "\n",
+        "    t0 = time.perf_counter()\n",
+        "    compressed, input_tokens, output_tokens = compress(text, int(target_tokens))\n",
+        "    duration_ms = round((time.perf_counter() - t0) * 1000, 1)\n",
+        "\n",
+        "    ratio   = round(output_tokens / input_tokens, 4) if input_tokens else 0.0\n",
+        "    quality = semantic_score(text, compressed)\n",
+        "\n",
+        "    run_id = save_run({\n",
+        "        'timestamp':         datetime.now(timezone.utc).isoformat(),\n",
+        "        'model':             get_current_model_id() or LLM_MODEL,\n",
+        "        'tokenizer':         get_current_tokenizer_id() or LLM_MODEL,\n",
+        "        'input_tokens':      input_tokens,\n",
+        "        'output_tokens':     output_tokens,\n",
+        "        'target_tokens':     int(target_tokens),\n",
+        "        'compression_ratio': ratio,\n",
+        "        'quality_score':     quality,\n",
+        "        'duration_ms':       duration_ms,\n",
+        "        'input_text':        text,\n",
+        "        'output_text':       compressed,\n",
+        "    })\n",
+        "\n",
+        "    return (\n",
+        "        compressed, input_tokens, output_tokens, ratio, quality,\n",
+        "        run_id,\n",
+        "        gr.update(visible=True), gr.update(visible=True),\n",
+        "        gr.update(value='', visible=True),\n",
+        "        gr.update(value='', visible=False),\n",
+        "        gr.update(visible=False),\n",
+        "        gr.update(value='', visible=False),\n",
+        "    )\n",
+        "\n",
+        "\n",
+        "def load_model(model_id: str) -> str:\n",
+        "    if not model_id:\n",
+        "        return 'No model selected.'\n",
+        "    try:\n",
+        "        return switch_llm(model_id)\n",
+        "    except Exception as exc:\n",
+        "        return f'Error loading {model_id}: {exc}'\n",
+        "\n",
+        "\n",
+        "def load_embedder(model_id: str) -> str:\n",
+        "    if not model_id:\n",
+        "        return 'No model selected.'\n",
+        "    try:\n",
+        "        return switch_embedder(model_id)\n",
+        "    except Exception as exc:\n",
+        "        return f'Error loading {model_id}: {exc}'\n",
+        "\n",
+        "\n",
+        "def on_embedder_change(model_id: str) -> str:\n",
+        "    return EMBEDDER_INFO.get(model_id, '')\n",
+        "\n",
+        "\n",
+        "def submit_feedback(run_id, value: int):\n",
+        "    if run_id is None:\n",
+        "        return 'Run a compression first.', gr.update(visible=False), gr.update(visible=False), gr.update(value='', visible=False)\n",
+        "    update_feedback(run_id, value)\n",
+        "    msg = '👍 Marked as helpful — thanks!' if value == 1 else '👎 Noted — thanks for the feedback!'\n",
+        "    return msg, gr.update(visible=True), gr.update(visible=True), gr.update(value='', visible=False)\n",
+        "\n",
+        "\n",
+        "def save_comment(run_id, comment: str):\n",
+        "    if run_id is None:\n",
+        "        return gr.update(value='Run a compression first.', visible=True)\n",
+        "    if not comment.strip():\n",
+        "        return gr.update(value='Type a note first.', visible=True)\n",
+        "    update_feedback_comment(run_id, comment.strip())\n",
+        "    return gr.update(value='✓ Note saved.', visible=True)\n",
+        "\n",
+        "\n",
+        "# ══════════════════════════════════════════════════════════════════════════════\n",
+        "# HISTORY TAB — handlers\n",
+        "# ══════════════════════════════════════════════════════════════════════════════\n",
+        "\n",
+        "_DEFAULT_COLS = ['id', 'timestamp', 'model', 'compression_ratio', 'quality_score', 'feedback']\n",
+        "_ALL_COLS = [\n",
+        "    'id', 'timestamp', 'model', 'tokenizer',\n",
+        "    'input_tokens', 'output_tokens', 'target_tokens',\n",
+        "    'compression_ratio', 'quality_score', 'duration_ms',\n",
+        "    'feedback', 'feedback_comment',\n",
+        "]\n",
+        "\n",
+        "\n",
+        "def load_history(selected_cols=None):\n",
+        "    cols = selected_cols if selected_cols else _DEFAULT_COLS\n",
+        "    runs = get_runs(limit=100)\n",
+        "    if not runs:\n",
+        "        return pd.DataFrame(columns=cols), '', '', ''\n",
+        "    df       = pd.DataFrame(runs)\n",
+        "    existing = [c for c in cols if c in df.columns]\n",
+        "    df       = df[existing]\n",
+        "    avg_q = f\"{df['quality_score'].mean():.4f}\" if 'quality_score' in df.columns else '—'\n",
+        "    avg_r = f\"{df['compression_ratio'].mean():.4f}\" if 'compression_ratio' in df.columns else '—'\n",
+        "    return df, avg_q, avg_r, ''\n",
+        "\n",
+        "\n",
+        "def on_row_select(evt: gr.SelectData, df: pd.DataFrame):\n",
+        "    if df is None or df.empty:\n",
+        "        return None, '', 'No rows available.'\n",
+        "    row_idx = evt.index[0]\n",
+        "    run_id  = int(df.iloc[row_idx]['id'])\n",
+        "    record  = get_run(run_id)\n",
+        "    if not record:\n",
+        "        return None, '', f'Row {run_id} not found in database.'\n",
+        "    return run_id, render_diff_html(record), f'Row {run_id} selected — click Delete to remove.'\n",
+        "\n",
+        "\n",
+        "def delete_selected(run_id, selected_cols):\n",
+        "    if run_id is None:\n",
+        "        df, avg_q, avg_r, _ = load_history(selected_cols)\n",
+        "        return df, avg_q, avg_r, None, '', 'No row selected.'\n",
+        "    delete_run(run_id)\n",
+        "    df, avg_q, avg_r, _ = load_history(selected_cols)\n",
+        "    return df, avg_q, avg_r, None, '', f'Row {run_id} deleted.'\n",
+        "\n",
+        "\n",
+        "# ══════════════════════════════════════════════════════════════════════════════\n",
+        "# BUILD APP\n",
+        "# ══════════════════════════════════════════════════════════════════════════════\n",
+        "\n",
+        "def build_app() -> gr.Blocks:\n",
+        "    with gr.Blocks(title=APP_TITLE) as app:\n",
+        "\n",
+        "        # ── Compress tab ──────────────────────────────────────────────────\n",
+        "        with gr.Tab('Compress'):\n",
+        "            gr.Markdown('## TinyPress — Prompt Compression Engine')\n",
+        "            gr.Markdown(\n",
+        "                'Paste any long text. Set your token budget. Get a compressed version '\n",
+        "                'that preserves intent — scored for quality.'\n",
+        "            )\n",
+        "\n",
+        "            with gr.Accordion('Model Settings', open=False):\n",
+        "                gr.Markdown('**Compression Model**')\n",
+        "                model_dropdown = gr.Dropdown(\n",
+        "                    choices=AVAILABLE_MODELS, value=LLM_MODEL,\n",
+        "                    label='Compression Model', allow_custom_value=True,\n",
+        "                )\n",
+        "                load_model_btn = gr.Button('Load Model', variant='secondary')\n",
+        "                model_status   = gr.Textbox(label='Model Status', value=f'Active: {LLM_MODEL}', interactive=False)\n",
+        "\n",
+        "                gr.Markdown(\"---\") # Replaced gr.Divider() with gr.Markdown(\"---\")\n",
+        "\n",
+        "                gr.Markdown('**Scoring Embedder**')\n",
+        "                embedder_dropdown = gr.Dropdown(\n",
+        "                    choices=AVAILABLE_EMBEDDER_MODELS, value=EMBEDDER_MODEL,\n",
+        "                    label='Embedder Model', allow_custom_value=True,\n",
+        "                )\n",
+        "                embedder_info_panel = gr.Markdown(value=EMBEDDER_INFO.get(EMBEDDER_MODEL, ''))\n",
+        "                load_embedder_btn = gr.Button('Load Embedder', variant='secondary')\n",
+        "                embedder_status   = gr.Textbox(label='Embedder Status', value=f'Active: {EMBEDDER_MODEL}', interactive=False)\n",
+        "\n",
+        "            with gr.Row():\n",
+        "                with gr.Column():\n",
+        "                    input_text = gr.Textbox(label='Input Text', lines=12, placeholder='Paste your text here...')\n",
+        "                    token_toggle_btn = gr.Button(_BTN_SHOW, variant='secondary', size='sm')\n",
+        "                    token_panel      = gr.HTML(value='')\n",
+        "                    tokens_visible   = gr.State(value=False)\n",
+        "                    target_slider    = gr.Slider(minimum=100, maximum=1000, value=DEFAULT_TARGET_TOKENS, step=50, label='Target Token Budget')\n",
+        "                    status_banner    = gr.HTML(value=_STATUS_EMPTY)\n",
+        "                    compress_btn     = gr.Button('Compress', variant='primary')\n",
+        "\n",
+        "                with gr.Column():\n",
+        "                    output_text = gr.Textbox(label='Compressed Output', lines=12)\n",
+        "                    with gr.Row():\n",
+        "                        input_tok  = gr.Number(label='Input Tokens',  interactive=False)\n",
+        "                        output_tok = gr.Number(label='Output Tokens', interactive=False)\n",
+        "                    with gr.Row():\n",
+        "                        ratio   = gr.Number(label='Compression Ratio',   interactive=False)\n",
+        "                        quality = gr.Number(label='Quality Score (0–1)', interactive=False)\n",
+        "                    gr.Markdown('**Was this compression helpful?**')\n",
+        "                    with gr.Row():\n",
+        "                        thumbs_up_btn   = gr.Button('👍  Helpful',     variant='secondary', visible=False, scale=1)\n",
+        "                        thumbs_down_btn = gr.Button('👎  Not helpful', variant='secondary', visible=False, scale=1)\n",
+        "                    feedback_status  = gr.Markdown('', visible=False)\n",
+        "                    comment_box      = gr.Textbox(\n",
+        "                        label='Add a note (optional)',\n",
+        "                        placeholder=\"e.g. 'lost key dates', 'too short', 'great summary'\",\n",
+        "                        lines=2, visible=False,\n",
+        "                    )\n",
+        "                    save_comment_btn = gr.Button('Save note', variant='secondary', size='sm', visible=False)\n",
+        "                    comment_saved    = gr.Markdown('', visible=False)\n",
+        "\n",
+        "            last_run_id = gr.State(value=None)\n",
+        "\n",
+        "            token_toggle_btn.click(fn=toggle_token_panel, inputs=[tokens_visible, input_text], outputs=[tokens_visible, token_panel, token_toggle_btn])\n",
+        "            input_text.change(fn=update_token_panel, inputs=[input_text, tokens_visible], outputs=[token_panel])\n",
+        "            _sa = dict(inputs=[input_text, target_slider], outputs=[status_banner])\n",
+        "            input_text.change(fn=compression_status, **_sa)\n",
+        "            target_slider.change(fn=compression_status, **_sa)\n",
+        "            load_model_btn.click(fn=load_model, inputs=[model_dropdown], outputs=[model_status])\n",
+        "            embedder_dropdown.change(fn=on_embedder_change, inputs=[embedder_dropdown], outputs=[embedder_info_panel])\n",
+        "            load_embedder_btn.click(fn=load_embedder, inputs=[embedder_dropdown], outputs=[embedder_status])\n",
+        "            compress_btn.click(\n",
+        "                fn=run_compression,\n",
+        "                inputs=[input_text, target_slider],\n",
+        "                outputs=[output_text, input_tok, output_tok, ratio, quality,\n",
+        "                         last_run_id, thumbs_up_btn, thumbs_down_btn, feedback_status,\n",
+        "                         comment_box, save_comment_btn, comment_saved],\n",
+        "            )\n",
+        "            thumbs_up_btn.click(\n",
+        "                fn=lambda run_id: submit_feedback(run_id, 1),\n",
+        "                inputs=[last_run_id],\n",
+        "                outputs=[feedback_status, comment_box, save_comment_btn, comment_saved],\n",
+        "            )\n",
+        "            thumbs_down_btn.click(\n",
+        "                fn=lambda run_id: submit_feedback(run_id, -1),\n",
+        "                inputs=[last_run_id],\n",
+        "                outputs=[feedback_status, comment_box, save_comment_btn, comment_saved],\n",
+        "            )\n",
+        "            save_comment_btn.click(fn=save_comment, inputs=[last_run_id, comment_box], outputs=[comment_saved])\n",
+        "\n",
+        "        # ── History tab ───────────────────────────────────────────────────\n",
+        "        with gr.Tab('History') as history_tab:\n",
+        "            gr.Markdown('## Compression Run History')\n",
+        "            with gr.Row():\n",
+        "                refresh_btn = gr.Button('Refresh', variant='secondary')\n",
+        "                delete_btn  = gr.Button('Delete Selected Row', variant='stop')\n",
+        "\n",
+        "            with gr.Accordion('Column visibility', open=False):\n",
+        "                col_picker = gr.CheckboxGroup(choices=_ALL_COLS, value=_DEFAULT_COLS, label=None)\n",
+        "\n",
+        "            with gr.Row():\n",
+        "                avg_quality = gr.Textbox(label='Avg Quality Score',     interactive=False)\n",
+        "                avg_ratio   = gr.Textbox(label='Avg Compression Ratio', interactive=False)\n",
+        "            history_table = gr.DataFrame(label='Past Runs — click a row to see its diff', interactive=False)\n",
+        "            delete_status = gr.Textbox(label='Status', value='Click a row to select it.', interactive=False)\n",
+        "            gr.Markdown('### Side-by-side Diff')\n",
+        "            diff_panel  = gr.HTML(value='')\n",
+        "            selected_id = gr.State(value=None)\n",
+        "\n",
+        "            _outputs = [history_table, avg_quality, avg_ratio, diff_panel]\n",
+        "            refresh_btn.click(fn=load_history, inputs=[col_picker], outputs=_outputs)\n",
+        "            history_tab.select(fn=load_history, inputs=[col_picker], outputs=_outputs)\n",
+        "            col_picker.change(fn=load_history, inputs=[col_picker], outputs=_outputs)\n",
+        "            history_table.select(fn=on_row_select, inputs=[history_table], outputs=[selected_id, diff_panel, delete_status])\n",
+        "            delete_btn.click(\n",
+        "                fn=delete_selected,\n",
+        "                inputs=[selected_id, col_picker],\n",
+        "                outputs=[history_table, avg_quality, avg_ratio, selected_id, diff_panel, delete_status],\n",
+        "            )\n",
+        "\n",
+        "    return app"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "co7JKDlXeGqo",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 715
+        },
+        "id": "co7JKDlXeGqo",
+        "outputId": "a62a6ec7-41bb-4fb8-ff02-61a13910f504"
+      },
+      "outputs": [],
+      "source": [
+        "# Launch UI\n",
+        "\n",
+        "app = build_app()\n",
+        "app.launch(share=PUBLIC_UI, server_port=SERVER_PORT, debug = True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "cell-s10-hdr",
+      "metadata": {
+        "id": "cell-s10-hdr"
+      },
+      "source": [
+        "## Step 10 — Programmatic demo (no UI needed)\n",
+        "\n",
+        "Run this cell to compress a sample text directly and inspect all metrics inline."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "cell-demo",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cell-demo",
+        "outputId": "9d8a823f-5995-4164-9dd3-99924d1d2a8e"
+      },
+      "outputs": [],
+      "source": [
+        "SAMPLE_TEXT = \"\"\"\n",
+        "The transformer architecture, introduced in the seminal paper Attention Is All You Need by Vaswani et al.\n",
+        "in 2017, fundamentally changed how we approach sequence modelling tasks in natural language processing.\n",
+        "Prior to transformers, recurrent neural networks (RNNs) and long short-term memory (LSTM) networks were\n",
+        "the dominant architectures for tasks such as machine translation, text summarisation, and question answering.\n",
+        "However, these models suffered from several limitations: they processed tokens sequentially, making\n",
+        "parallelisation difficult; they struggled to capture long-range dependencies due to vanishing gradients;\n",
+        "and training was slow even on modern hardware. The transformer addressed all of these issues through\n",
+        "its self-attention mechanism, which allows every token in a sequence to directly attend to every other\n",
+        "token in a single operation. Multi-head attention further extends this by running several attention\n",
+        "functions in parallel, capturing different types of relationships between tokens simultaneously.\n",
+        "Position encodings are added to token embeddings to give the model a sense of sequence order, since\n",
+        "unlike RNNs the architecture has no inherent notion of position. Feed-forward sub-layers, layer\n",
+        "normalisation, and residual connections complete each transformer block. The result is a model that\n",
+        "trains faster, scales better with data and compute, and generalises more effectively than its\n",
+        "predecessors, setting the stage for large language models like GPT, BERT, and the entire modern\n",
+        "LLM ecosystem.\n",
+        "\"\"\".strip()\n",
+        "\n",
+        "TARGET = 150  # token budget\n",
+        "\n",
+        "input_tok_count = count_tokens(SAMPLE_TEXT)\n",
+        "print(f'Input tokens : {input_tok_count}')\n",
+        "print(f'Target tokens: {TARGET}')\n",
+        "print(f'Status       : {\"ready to compress\" if input_tok_count > TARGET else \"already within budget\"}')\n",
+        "print()\n",
+        "\n",
+        "t0 = time.perf_counter()\n",
+        "compressed, in_tok, out_tok = compress(SAMPLE_TEXT, TARGET)\n",
+        "elapsed = round((time.perf_counter() - t0) * 1000, 1)\n",
+        "\n",
+        "score = semantic_score(SAMPLE_TEXT, compressed)\n",
+        "ratio = round(out_tok / in_tok, 4)\n",
+        "\n",
+        "print('─' * 60)\n",
+        "print(compressed)\n",
+        "print('─' * 60)\n",
+        "print(f'Output tokens    : {out_tok}')\n",
+        "print(f'Compression ratio: {ratio}')\n",
+        "print(f'Quality score    : {score}')\n",
+        "print(f'Duration         : {elapsed} ms')\n",
+        "print(f'Model            : {get_current_model_id()}')\n",
+        "print(f'Tokenizer        : {get_current_tokenizer_id()}')"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
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   },
-  "language_info": {
-   "name": "python",
-   "version": "3.10.0"
-  },
-  "colab": {
-   "provenance": [],
-   "gpuType": "T4"
-  },
-  "accelerator": "GPU"
- },
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "cell-title",
-   "metadata": {},
-   "source": "# TinyPress — Prompt Compression Engine\n\n**HuggingFace Build Small Hackathon · Track: Thousand Token Wood**\n\n| Layer | Detail |\n|-------|--------|\n| Compression | `Qwen/Qwen2.5-1.5B-Instruct` (default, switchable) |\n| Scoring | `sentence-transformers/all-MiniLM-L6-v2` (default, switchable) |\n| UI | Gradio 5 — public share URL |\n| Storage | SQLite at `/content/tinypress.db` |\n\n**Features**\n- Compress text to a user-defined token budget\n- Live 🔴 / 🟢 compression readiness banner\n- Per-token colour highlight panel (toggle on/off)\n- Dynamic compression model switching (5 curated <32B models)\n- Dynamic scoring embedder switching (6 models, with per-model impact info)\n- 👍 / 👎 feedback on every compression result, with optional text comment\n- Compression run history persisted to SQLite\n- Column picker in History tab — compact default view, expandable to all fields\n- Per-row delete in history\n- Side-by-side word-level diff viewer with feedback badge and token detail\n\n> **Recommended runtime:** GPU  →  Runtime → Change runtime type → T4 GPU\n\n---\n\n### About the author\n\nBuilt by **Sriharsha C R** — AI Engineer, Cloud Native developer, and knowledge sharer.\nIf this was useful, feel free to connect — always happy to chat about AI, LLMs, or anything in between.\n\n[![LinkedIn](https://img.shields.io/badge/LinkedIn-sriharsha--cr-0a66c2?logo=linkedin&logoColor=white)](https://www.linkedin.com/in/sriharsha-cr)\n[![X / Twitter](https://img.shields.io/badge/X-@sriharsha__cr-000000?logo=x&logoColor=white)](https://x.com/sriharsha_cr)\n[![HuggingFace](https://img.shields.io/badge/HuggingFace-sriharsha--cr-ff9d00?logo=huggingface&logoColor=white)](https://huggingface.co/sriharsha-cr)\n[![GitHub](https://img.shields.io/badge/GitHub-SriharshaCR-181717?logo=github&logoColor=white)](https://github.com/SriharshaCR)"
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-s1-hdr",
-   "metadata": {},
-   "source": [
-    "## Step 1 — Install dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-install",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!pip install -q \\\n",
-    "    \"gradio==5.0\" \\\n",
-    "    \"transformers>=4.40.0\" \\\n",
-    "    \"sentence-transformers>=3.0.0\" \\\n",
-    "    \"torch>=2.2.0\" \\\n",
-    "    \"numpy>=1.26.0\" \\\n",
-    "    \"pandas>=2.0.0\" \\\n",
-    "    \"accelerate>=0.30.0\" \\\n",
-    "    \"huggingface_hub==0.25.2\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-s2-hdr",
-   "metadata": {},
-   "source": [
-    "## Step 2 — Runtime check"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-runtime",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "\n",
-    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
-    "dtype  = torch.float16 if device == 'cuda' else torch.float32\n",
-    "\n",
-    "print(f'Device : {device}')\n",
-    "if device == 'cuda':\n",
-    "    print(f'GPU    : {torch.cuda.get_device_name(0)}')\n",
-    "    print(f'VRAM   : {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB')\n",
-    "print(f'dtype  : {dtype}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-s3-hdr",
-   "metadata": {},
-   "source": [
-    "## Step 3 — Configuration"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-config",
-   "metadata": {},
-   "outputs": [],
-   "source": "import os\n\nLLM_MODEL      = os.getenv('LLM_MODEL',      'Qwen/Qwen2.5-1.5B-Instruct')\nEMBEDDER_MODEL = os.getenv('EMBEDDER_MODEL', 'sentence-transformers/all-MiniLM-L6-v2')\nDB_PATH        = os.getenv('DB_PATH',        '/content/tinypress.db')\nSERVER_PORT    = int(os.getenv('PORT', 7860))\n\nDEFAULT_TARGET_TOKENS = 500\nMAX_NEW_TOKENS        = 1024\nAPP_TITLE             = 'TinyPress'\n\n# Curated <32B open-weight causal LMs for local / Colab inference.\nAVAILABLE_MODELS = [\n    'Qwen/Qwen2.5-1.5B-Instruct',\n    'Qwen/Qwen2.5-0.5B-Instruct',\n    'HuggingFaceTB/SmolLM2-1.7B-Instruct',\n    'microsoft/Phi-3.5-mini-instruct',\n    'meta-llama/Llama-3.2-1B-Instruct',\n]\n\n# Curated sentence-transformer embedding models for quality scoring.\nAVAILABLE_EMBEDDER_MODELS = [\n    'sentence-transformers/all-MiniLM-L6-v2',\n    'sentence-transformers/all-mpnet-base-v2',\n    'BAAI/bge-small-en-v1.5',\n    'BAAI/bge-base-en-v1.5',\n    'mixedbread-ai/mxbai-embed-large-v1',\n    'Alibaba-NLP/gte-Qwen2-1.5B-instruct',\n]\n\nEMBEDDER_INFO = {\n    'sentence-transformers/all-MiniLM-L6-v2': (\n        '⚡ **Fast · 22M params · Default**  \\n'\n        'Great baseline. Scores are reliable for typical compression ratios. '\n        'Runs comfortably on CPU — minimal overhead.'\n    ),\n    'sentence-transformers/all-mpnet-base-v2': (\n        '⚖️ **Balanced · 110M params**  \\n'\n        'Noticeably sharper quality scores than MiniLM, especially on longer texts. '\n        'Small speed trade-off; fine on CPU.'\n    ),\n    'BAAI/bge-small-en-v1.5': (\n        '⚡ **Fast · 33M params**  \\n'\n        'Strong quality-to-size ratio — often matches MiniLM on accuracy while being '\n        'slightly more sensitive to meaning shifts. Good CPU option.'\n    ),\n    'BAAI/bge-base-en-v1.5': (\n        '⚖️ **Balanced · 109M params**  \\n'\n        'Consistently strong on semantic similarity benchmarks. '\n        'Scores will be more discriminating — small differences in compression quality show up more clearly.'\n    ),\n    'mixedbread-ai/mxbai-embed-large-v1': (\n        '🏆 **High quality · 335M params**  \\n'\n        'Top-tier similarity scores. Quality readings will be the most accurate here, '\n        'but slower to load and run. GPU recommended.'\n    ),\n    'Alibaba-NLP/gte-Qwen2-1.5B-instruct': (\n        '🔬 **Best quality · 1.5B params**  \\n'\n        'Strongest semantic understanding in this list. Scores will reflect subtle meaning loss '\n        'that smaller models miss. Requires significant RAM/VRAM — GPU strongly recommended.'\n    ),\n}\n\nprint(f'LLM      : {LLM_MODEL}')\nprint(f'Embedder : {EMBEDDER_MODEL}')\nprint(f'DB       : {DB_PATH}')"
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-s4-hdr",
-   "metadata": {},
-   "source": [
-    "## Step 4 — Model loader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-model-loader",
-   "metadata": {},
-   "outputs": [],
-   "source": "from transformers import AutoTokenizer, AutoModelForCausalLM\nfrom sentence_transformers import SentenceTransformer\nimport gc\n\n_llm               = None\n_tokenizer         = None\n_embedder          = None\n_current_model_id    = None\n_current_embedder_id = None\n\n\ndef get_current_model_id():\n    return _current_model_id\n\n\ndef get_current_tokenizer_id():\n    # Tokenizer is always loaded from the same HF repo as the model.\n    return _current_model_id\n\n\ndef get_current_embedder_id():\n    return _current_embedder_id\n\n\ndef get_llm():\n    global _llm, _tokenizer\n    if _llm is None:\n        _load_llm(LLM_MODEL)\n    return _llm, _tokenizer\n\n\ndef switch_llm(model_id: str) -> str:\n    global _current_model_id\n    if _current_model_id == model_id:\n        return f'Already using {model_id}'\n    _unload_llm()\n    _load_llm(model_id)\n    return f'Loaded: {model_id}'\n\n\ndef _load_llm(model_id: str):\n    \"\"\"Load model + its paired tokenizer. Both come from the same model_id.\"\"\"\n    global _llm, _tokenizer, _current_model_id\n    print(f'Loading LLM: {model_id} ...')\n    _tokenizer = AutoTokenizer.from_pretrained(model_id)\n    _llm = AutoModelForCausalLM.from_pretrained(\n        model_id,\n        torch_dtype=dtype,\n        device_map='auto',\n    )\n    _llm.eval()\n    _current_model_id = model_id\n    print(f'LLM ready: {model_id}')\n\n\ndef _unload_llm():\n    \"\"\"Free GPU/CPU memory before loading a different model.\"\"\"\n    global _llm, _tokenizer, _current_model_id\n    del _llm, _tokenizer\n    _llm = None\n    _tokenizer = None\n    _current_model_id = None\n    gc.collect()\n    if torch.cuda.is_available():\n        torch.cuda.empty_cache()\n\n\ndef get_embedder():\n    global _embedder, _current_embedder_id\n    if _embedder is None:\n        _load_embedder(EMBEDDER_MODEL)\n    return _embedder\n\n\ndef switch_embedder(model_id: str) -> str:\n    global _current_embedder_id\n    if _current_embedder_id == model_id:\n        return f'Already using {model_id}'\n    _unload_embedder()\n    _load_embedder(model_id)\n    return f'Loaded: {model_id}'\n\n\ndef _load_embedder(model_id: str):\n    global _embedder, _current_embedder_id\n    print(f'Loading embedder: {model_id} ...')\n    _embedder = SentenceTransformer(model_id)\n    _current_embedder_id = model_id\n    print(f'Embedder ready: {model_id}')\n\n\ndef _unload_embedder():\n    global _embedder, _current_embedder_id\n    del _embedder\n    _embedder = None\n    _current_embedder_id = None\n    gc.collect()\n    if torch.cuda.is_available():\n        torch.cuda.empty_cache()\n\n\nprint('Model loader defined.')"
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-s5-hdr",
-   "metadata": {},
-   "source": [
-    "## Step 5 — Core pipeline"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-core",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "# ── tokenizer utils ───────────────────────────────────────────────────────────\n",
-    "\n",
-    "def count_tokens(text: str) -> int:\n",
-    "    _, tokenizer = get_llm()\n",
-    "    return len(tokenizer.encode(text, add_special_tokens=False))\n",
-    "\n",
-    "\n",
-    "def get_token_strings(text: str) -> list:\n",
-    "    \"\"\"Return the decoded surface string for every token in text.\"\"\"\n",
-    "    _, tokenizer = get_llm()\n",
-    "    ids = tokenizer.encode(text, add_special_tokens=False)\n",
-    "    return [tokenizer.decode([i]) for i in ids]\n",
-    "\n",
-    "\n",
-    "# ── compressor ────────────────────────────────────────────────────────────────\n",
-    "\n",
-    "_PROMPT_TEMPLATE = (\n",
-    "    'You are a lossless compression assistant. '\n",
-    "    'Compress the following text to at most {target} tokens.\\n'\n",
-    "    'Preserve all key facts, decisions, and intent. '\n",
-    "    'Do not add commentary. Output only the compressed text.\\n\\n'\n",
-    "    'TEXT:\\n{text}\\n\\nCOMPRESSED:'\n",
-    ")\n",
-    "\n",
-    "\n",
-    "def _generate(prompt: str) -> str:\n",
-    "    model, tokenizer = get_llm()\n",
-    "    inputs = tokenizer(prompt, return_tensors='pt').to(model.device)\n",
-    "    with torch.no_grad():\n",
-    "        output_ids = model.generate(\n",
-    "            **inputs,\n",
-    "            max_new_tokens=MAX_NEW_TOKENS,\n",
-    "            do_sample=False,\n",
-    "            pad_token_id=tokenizer.eos_token_id,\n",
-    "        )\n",
-    "    new_tokens = output_ids[0][inputs['input_ids'].shape[1]:]\n",
-    "    return tokenizer.decode(new_tokens, skip_special_tokens=True).strip()\n",
-    "\n",
-    "\n",
-    "def compress(text: str, target_tokens: int) -> tuple:\n",
-    "    \"\"\"Returns (compressed_text, input_token_count, output_token_count).\"\"\"\n",
-    "    input_tokens = count_tokens(text)\n",
-    "    if input_tokens <= target_tokens:\n",
-    "        return text, input_tokens, input_tokens\n",
-    "\n",
-    "    prompt     = _PROMPT_TEMPLATE.format(target=target_tokens, text=text)\n",
-    "    compressed = _generate(prompt)\n",
-    "\n",
-    "    # Hard-trim if model overshoots.\n",
-    "    _, tokenizer = get_llm()\n",
-    "    ids = tokenizer.encode(compressed, add_special_tokens=False)\n",
-    "    if len(ids) > target_tokens:\n",
-    "        compressed = tokenizer.decode(ids[:target_tokens], skip_special_tokens=True)\n",
-    "\n",
-    "    output_tokens = count_tokens(compressed)\n",
-    "    return compressed, input_tokens, output_tokens\n",
-    "\n",
-    "\n",
-    "# ── scorer ────────────────────────────────────────────────────────────────────\n",
-    "\n",
-    "def semantic_score(original: str, compressed: str) -> float:\n",
-    "    embedder = get_embedder()\n",
-    "    vecs = embedder.encode([original, compressed], convert_to_numpy=True)\n",
-    "    cos  = float(\n",
-    "        np.dot(vecs[0], vecs[1]) / (np.linalg.norm(vecs[0]) * np.linalg.norm(vecs[1]))\n",
-    "    )\n",
-    "    return round(max(0.0, min(1.0, cos)), 4)\n",
-    "\n",
-    "\n",
-    "print('Core pipeline defined.')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-s6-hdr",
-   "metadata": {},
-   "source": [
-    "## Step 6 — Diff renderer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-diff",
-   "metadata": {},
-   "outputs": [],
-   "source": "import difflib\nimport html as _h\n\n\ndef _word_diff(original: str, compressed: str) -> tuple:\n    \"\"\"\n    Word-level SequenceMatcher diff.\n    Returns (annotated_original_html, annotated_compressed_html).\n    Colour key:\n      original  — red strikethrough = dropped\n      compressed — amber            = rewritten\n      compressed — green            = inserted\n      plain                         = unchanged\n    \"\"\"\n    orig_words = original.split()\n    comp_words = compressed.split()\n    matcher = difflib.SequenceMatcher(None, orig_words, comp_words, autojunk=False)\n\n    orig_parts, comp_parts = [], []\n\n    for tag, i1, i2, j1, j2 in matcher.get_opcodes():\n        ow = _h.escape(' '.join(orig_words[i1:i2]))\n        cw = _h.escape(' '.join(comp_words[j1:j2]))\n\n        if tag == 'equal':\n            orig_parts.append(ow)\n            comp_parts.append(cw)\n\n        elif tag == 'delete':\n            orig_parts.append(\n                f'<mark style=\"background:#fee2e2;color:#b91c1c;'\n                f'text-decoration:line-through;padding:1px 3px;border-radius:3px\">{ow}</mark>'\n            )\n\n        elif tag == 'insert':\n            comp_parts.append(\n                f'<mark style=\"background:#dcfce7;color:#15803d;'\n                f'padding:1px 3px;border-radius:3px\">{cw}</mark>'\n            )\n\n        elif tag == 'replace':\n            orig_parts.append(\n                f'<mark style=\"background:#fee2e2;color:#b91c1c;'\n                f'text-decoration:line-through;padding:1px 3px;border-radius:3px\">{ow}</mark>'\n            )\n            comp_parts.append(\n                f'<mark style=\"background:#fef9c3;color:#92400e;'\n                f'padding:1px 3px;border-radius:3px\">{cw}</mark>'\n            )\n\n    return ' '.join(orig_parts), ' '.join(comp_parts)\n\n\ndef render_diff_html(record: dict) -> str:\n    \"\"\"Build a self-contained side-by-side diff HTML block for a compression run.\"\"\"\n    original   = record.get('input_text', '')\n    compressed = record.get('output_text', '')\n    if not original or not compressed:\n        return ''\n\n    orig_html, comp_html = _word_diff(original, compressed)\n\n    model      = _h.escape(record.get('model', '—'))\n    tokenizer  = _h.escape(record.get('tokenizer', '—'))\n    ts         = _h.escape(record.get('timestamp', '—'))\n    in_tok     = record.get('input_tokens',  '—')\n    out_tok    = record.get('output_tokens', '—')\n    target_tok = record.get('target_tokens', '—')\n    ratio      = record.get('compression_ratio', 0)\n    quality    = record.get('quality_score', 0)\n    duration   = record.get('duration_ms', '—')\n    run_id     = record.get('id', '—')\n\n    feedback_val  = record.get('feedback')\n    feedback_note = _h.escape(record.get('feedback_comment') or '')\n\n    # Build optional feedback block\n    if feedback_val is not None:\n        badge_bg    = '#f0fdf4' if feedback_val == 1 else '#fef2f2'\n        badge_color = '#15803d' if feedback_val == 1 else '#b91c1c'\n        badge_text  = '👍 Helpful' if feedback_val == 1 else '👎 Not helpful'\n        feedback_block = (\n            f'<div style=\"display:flex;flex-wrap:wrap;align-items:center;gap:8px;'\n            f'margin-top:10px;padding:8px 12px;border-radius:6px;background:{badge_bg}\">'\n            f'<span style=\"font-weight:600;font-size:0.8rem;color:{badge_color}\">{badge_text}</span>'\n        )\n        if feedback_note:\n            feedback_block += (\n                f'<span style=\"font-size:0.8rem;color:#374151;font-style:italic\">'\n                f'\"{feedback_note}\"</span>'\n            )\n        feedback_block += '</div>'\n    else:\n        feedback_block = ''\n\n    return f\"\"\"\n<div style=\"font-family:system-ui,sans-serif;margin-top:4px\">\n\n  <!-- Primary meta chips -->\n  <div style=\"display:flex;flex-wrap:wrap;gap:6px;margin-bottom:6px;font-size:0.78rem\">\n    <span style=\"background:#f3f4f6;padding:3px 9px;border-radius:12px;color:#374151\">Run #{run_id}</span>\n    <span style=\"background:#f3f4f6;padding:3px 9px;border-radius:12px;color:#374151\">{ts}</span>\n    <span style=\"background:#eff6ff;padding:3px 9px;border-radius:12px;color:#1d4ed8\">{model}</span>\n    <span style=\"background:#f0fdf4;padding:3px 9px;border-radius:12px;color:#15803d\">Quality {quality:.4f}</span>\n    <span style=\"background:#fff7ed;padding:3px 9px;border-radius:12px;color:#c2410c\">Ratio {ratio:.4f}</span>\n    <span style=\"background:#faf5ff;padding:3px 9px;border-radius:12px;color:#7e22ce\">&#9201; {duration} ms</span>\n  </div>\n\n  <!-- Secondary meta chips -->\n  <div style=\"display:flex;flex-wrap:wrap;gap:6px;margin-bottom:12px;font-size:0.78rem\">\n    <span style=\"background:#f3f4f6;padding:3px 9px;border-radius:12px;color:#374151\">{in_tok} in → {out_tok} out (target {target_tok})</span>\n    <span style=\"background:#f3f4f6;padding:3px 9px;border-radius:12px;color:#374151\">tokenizer: {tokenizer}</span>\n  </div>\n\n  <!-- Side-by-side panels -->\n  <div style=\"display:grid;grid-template-columns:1fr 1fr;gap:12px\">\n    <div style=\"border:1px solid #fecaca;border-radius:8px;overflow:hidden\">\n      <div style=\"background:#fef2f2;padding:8px 14px;border-bottom:1px solid #fecaca;\n                  display:flex;justify-content:space-between;align-items:center\">\n        <span style=\"font-weight:700;font-size:0.8rem;color:#b91c1c\">ORIGINAL</span>\n        <span style=\"font-size:0.75rem;color:#6b7280\">{in_tok} tokens</span>\n      </div>\n      <div style=\"padding:14px;line-height:1.8;font-size:0.875rem;color:#1a1a1a;\n                  max-height:340px;overflow-y:auto;word-break:break-word\">{orig_html}</div>\n    </div>\n    <div style=\"border:1px solid #bbf7d0;border-radius:8px;overflow:hidden\">\n      <div style=\"background:#f0fdf4;padding:8px 14px;border-bottom:1px solid #bbf7d0;\n                  display:flex;justify-content:space-between;align-items:center\">\n        <span style=\"font-weight:700;font-size:0.8rem;color:#15803d\">COMPRESSED</span>\n        <span style=\"font-size:0.75rem;color:#6b7280\">{out_tok} tokens</span>\n      </div>\n      <div style=\"padding:14px;line-height:1.8;font-size:0.875rem;color:#1a1a1a;\n                  max-height:340px;overflow-y:auto;word-break:break-word\">{comp_html}</div>\n    </div>\n  </div>\n\n  {feedback_block}\n\n  <!-- Legend -->\n  <div style=\"display:flex;flex-wrap:wrap;gap:14px;margin-top:10px;font-size:0.75rem;color:#6b7280;align-items:center\">\n    <mark style=\"background:#fee2e2;color:#b91c1c;text-decoration:line-through;padding:2px 7px;border-radius:3px\">dropped</mark>\n    <mark style=\"background:#fef9c3;color:#92400e;padding:2px 7px;border-radius:3px\">rewritten</mark>\n    <mark style=\"background:#dcfce7;color:#15803d;padding:2px 7px;border-radius:3px\">inserted</mark>\n    <span>plain = unchanged</span>\n  </div>\n\n</div>\n\"\"\"\n\n\nprint('Diff renderer defined.')"
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-s7-hdr",
-   "metadata": {},
-   "source": [
-    "## Step 7 — Database"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-db",
-   "metadata": {},
-   "outputs": [],
-   "source": "import sqlite3\n\n_SCHEMA = \"\"\"\nCREATE TABLE IF NOT EXISTS compression_runs (\n    id                INTEGER PRIMARY KEY AUTOINCREMENT,\n    timestamp         TEXT    NOT NULL,\n    model             TEXT    NOT NULL,\n    tokenizer         TEXT    NOT NULL,\n    input_tokens      INTEGER NOT NULL,\n    output_tokens     INTEGER NOT NULL,\n    target_tokens     INTEGER NOT NULL,\n    compression_ratio REAL    NOT NULL,\n    quality_score     REAL    NOT NULL,\n    duration_ms       REAL    NOT NULL,\n    input_text        TEXT    NOT NULL,\n    output_text       TEXT    NOT NULL,\n    feedback          INTEGER,\n    feedback_comment  TEXT\n);\n\"\"\"\n\n\ndef _connect():\n    conn = sqlite3.connect(DB_PATH)\n    conn.row_factory = sqlite3.Row\n    return conn\n\n\ndef init_db():\n    conn = _connect()\n    conn.executescript(_SCHEMA)\n    for col, typedef in [\n        ('tokenizer',        'TEXT NOT NULL DEFAULT \"\"'),\n        ('duration_ms',      'REAL NOT NULL DEFAULT 0'),\n        ('feedback',         'INTEGER'),\n        ('feedback_comment', 'TEXT'),\n    ]:\n        try:\n            conn.execute(f'ALTER TABLE compression_runs ADD COLUMN {col} {typedef}')\n        except sqlite3.OperationalError:\n            pass\n    conn.commit()\n    conn.close()\n\n\ndef save_run(record: dict) -> int:\n    conn = _connect()\n    cursor = conn.execute(\n        '''\n        INSERT INTO compression_runs\n            (timestamp, model, tokenizer, input_tokens, output_tokens, target_tokens,\n             compression_ratio, quality_score, duration_ms, input_text, output_text)\n        VALUES\n            (:timestamp, :model, :tokenizer, :input_tokens, :output_tokens, :target_tokens,\n             :compression_ratio, :quality_score, :duration_ms, :input_text, :output_text)\n        ''',\n        record,\n    )\n    run_id = cursor.lastrowid\n    conn.commit()\n    conn.close()\n    return run_id\n\n\ndef update_feedback(run_id: int, value: int):\n    conn = _connect()\n    conn.execute('UPDATE compression_runs SET feedback = ? WHERE id = ?', (value, run_id))\n    conn.commit()\n    conn.close()\n\n\ndef update_feedback_comment(run_id: int, comment: str):\n    conn = _connect()\n    conn.execute('UPDATE compression_runs SET feedback_comment = ? WHERE id = ?', (comment, run_id))\n    conn.commit()\n    conn.close()\n\n\ndef delete_run(run_id: int):\n    conn = _connect()\n    conn.execute('DELETE FROM compression_runs WHERE id = ?', (run_id,))\n    conn.commit()\n    conn.close()\n\n\ndef get_run(run_id: int):\n    conn = _connect()\n    row = conn.execute('SELECT * FROM compression_runs WHERE id = ?', (run_id,)).fetchone()\n    conn.close()\n    return dict(row) if row else None\n\n\ndef get_runs(limit: int = 100) -> list:\n    conn = _connect()\n    rows = conn.execute(\n        'SELECT * FROM compression_runs ORDER BY id DESC LIMIT ?', (limit,)\n    ).fetchall()\n    conn.close()\n    return [dict(r) for r in rows]\n\n\ninit_db()\nprint(f'Database ready at {DB_PATH}')"
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-s8-hdr",
-   "metadata": {},
-   "source": [
-    "## Step 8 — Load models\n",
-    "\n",
-    "Downloads and caches weights. GPU warm-cache: ~30 s. First run: a few minutes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-load-models",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "get_llm()\n",
-    "get_embedder()\n",
-    "print('\\nAll models loaded and ready.')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-s9-hdr",
-   "metadata": {},
-   "source": [
-    "## Step 9 — Launch Gradio UI\n",
-    "\n",
-    "Prints a **public share URL** when ready. All features are live in the UI."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-gradio",
-   "metadata": {},
-   "outputs": [],
-   "source": "import html as _h\nimport time\nfrom datetime import datetime, timezone\n\nimport gradio as gr\nimport pandas as pd\n\n\n# ══════════════════════════════════════════════════════════════════════════════\n# COMPRESS TAB — handlers\n# ══════════════════════════════════════════════════════════════════════════════\n\n_PALETTE = [\n    '#fde68a', '#bbf7d0', '#bfdbfe', '#fecaca', '#e9d5ff',\n    '#fed7aa', '#99f6e4', '#e0e7ff', '#fce7f3', '#d1fae5',\n]\n_BTN_SHOW = '🔍  Show Token Highlights'\n_BTN_HIDE = '🙈  Hide Token Highlights'\n\n\ndef _render_token_html(text: str) -> str:\n    if not text.strip():\n        return ''\n    tokens = get_token_strings(text)\n    if not tokens:\n        return ''\n    spans = []\n    for i, tok in enumerate(tokens):\n        color   = _PALETTE[i % len(_PALETTE)]\n        display = _h.escape(tok).replace(' ', '<span style=\"opacity:0.35;font-size:0.7em\">·</span>')\n        spans.append(\n            f'<span title=\"token {i+1}\" '\n            f'style=\"background:{color};border-radius:4px;padding:2px 5px;'\n            f'font-family:\\'Courier New\\',monospace;font-size:0.8rem;'\n            f'line-height:2.2;margin:2px 1px;display:inline-block;'\n            f'cursor:default;border:1px solid rgba(0,0,0,0.06)\">{display}</span>'\n        )\n    return (\n        '<div style=\"font-family:system-ui,sans-serif;padding:10px 12px;'\n        'border:1px solid #e5e7eb;border-radius:8px;background:#fafafa\">'\n        f'<div style=\"font-size:0.75rem;color:#6b7280;margin-bottom:8px;font-weight:500\">'\n        f'{len(tokens)} tokens — each chip = one token, hover for index</div>'\n        '<div style=\"line-height:2.6;word-break:break-all;max-height:200px;overflow-y:auto\">'\n        + ''.join(spans) + '</div></div>'\n    )\n\n\ndef toggle_token_panel(is_visible: bool, text: str):\n    new_visible  = not is_visible\n    html_content = _render_token_html(text) if new_visible else ''\n    btn_label    = _BTN_HIDE if new_visible else _BTN_SHOW\n    return new_visible, html_content, gr.update(value=btn_label)\n\n\ndef update_token_panel(text: str, is_visible: bool) -> str:\n    return _render_token_html(text) if is_visible else ''\n\n\n_STATUS_EMPTY = '<span></span>'\n_STATUS_RED = (\n    '<div style=\"background:#fee2e2;border:1px solid #ef4444;color:#b91c1c;'\n    'padding:8px 12px;border-radius:6px;font-size:0.9rem;\">'\n    '🔴 <strong>Compression not needed</strong> — input ({input_tok} tokens) '\n    'is already within the {budget}-token budget.</div>'\n)\n_STATUS_GREEN = (\n    '<div style=\"background:#dcfce7;border:1px solid #22c55e;color:#15803d;'\n    'padding:8px 12px;border-radius:6px;font-size:0.9rem;\">'\n    '🟢 <strong>Ready to compress</strong> — {input_tok} tokens → {budget} token budget '\n    '({delta} tokens to shed).</div>'\n)\n\n\ndef compression_status(text: str, target_tokens: int) -> str:\n    if not text.strip():\n        return _STATUS_EMPTY\n    n = count_tokens(text)\n    if n <= int(target_tokens):\n        return _STATUS_RED.format(input_tok=n, budget=int(target_tokens))\n    return _STATUS_GREEN.format(input_tok=n, budget=int(target_tokens), delta=n - int(target_tokens))\n\n\ndef run_compression(text: str, target_tokens: int):\n    _hidden = gr.update(visible=False)\n    if not text.strip():\n        return ('', 0, 0, 0, 0.0, None,\n                _hidden, _hidden, gr.update(value='', visible=False),\n                gr.update(value='', visible=False), _hidden, gr.update(value='', visible=False))\n\n    t0 = time.perf_counter()\n    compressed, input_tokens, output_tokens = compress(text, int(target_tokens))\n    duration_ms = round((time.perf_counter() - t0) * 1000, 1)\n\n    ratio   = round(output_tokens / input_tokens, 4) if input_tokens else 0.0\n    quality = semantic_score(text, compressed)\n\n    run_id = save_run({\n        'timestamp':         datetime.now(timezone.utc).isoformat(),\n        'model':             get_current_model_id() or LLM_MODEL,\n        'tokenizer':         get_current_tokenizer_id() or LLM_MODEL,\n        'input_tokens':      input_tokens,\n        'output_tokens':     output_tokens,\n        'target_tokens':     int(target_tokens),\n        'compression_ratio': ratio,\n        'quality_score':     quality,\n        'duration_ms':       duration_ms,\n        'input_text':        text,\n        'output_text':       compressed,\n    })\n\n    return (\n        compressed, input_tokens, output_tokens, ratio, quality,\n        run_id,\n        gr.update(visible=True), gr.update(visible=True),\n        gr.update(value='', visible=True),\n        gr.update(value='', visible=False),\n        gr.update(visible=False),\n        gr.update(value='', visible=False),\n    )\n\n\ndef load_model(model_id: str) -> str:\n    if not model_id:\n        return 'No model selected.'\n    try:\n        return switch_llm(model_id)\n    except Exception as exc:\n        return f'Error loading {model_id}: {exc}'\n\n\ndef load_embedder(model_id: str) -> str:\n    if not model_id:\n        return 'No model selected.'\n    try:\n        return switch_embedder(model_id)\n    except Exception as exc:\n        return f'Error loading {model_id}: {exc}'\n\n\ndef on_embedder_change(model_id: str) -> str:\n    return EMBEDDER_INFO.get(model_id, '')\n\n\ndef submit_feedback(run_id, value: int):\n    if run_id is None:\n        return 'Run a compression first.', gr.update(visible=False), gr.update(visible=False), gr.update(value='', visible=False)\n    update_feedback(run_id, value)\n    msg = '👍 Marked as helpful — thanks!' if value == 1 else '👎 Noted — thanks for the feedback!'\n    return msg, gr.update(visible=True), gr.update(visible=True), gr.update(value='', visible=False)\n\n\ndef save_comment(run_id, comment: str):\n    if run_id is None:\n        return gr.update(value='Run a compression first.', visible=True)\n    if not comment.strip():\n        return gr.update(value='Type a note first.', visible=True)\n    update_feedback_comment(run_id, comment.strip())\n    return gr.update(value='✓ Note saved.', visible=True)\n\n\n# ══════════════════════════════════════════════════════════════════════════════\n# HISTORY TAB — handlers\n# ══════════════════════════════════════════════════════════════════════════════\n\n_DEFAULT_COLS = ['id', 'timestamp', 'model', 'compression_ratio', 'quality_score', 'feedback']\n_ALL_COLS = [\n    'id', 'timestamp', 'model', 'tokenizer',\n    'input_tokens', 'output_tokens', 'target_tokens',\n    'compression_ratio', 'quality_score', 'duration_ms',\n    'feedback', 'feedback_comment',\n]\n\n\ndef load_history(selected_cols=None):\n    cols = selected_cols if selected_cols else _DEFAULT_COLS\n    runs = get_runs(limit=100)\n    if not runs:\n        return pd.DataFrame(columns=cols), '', '', ''\n    df       = pd.DataFrame(runs)\n    existing = [c for c in cols if c in df.columns]\n    df       = df[existing]\n    avg_q = f\"{df['quality_score'].mean():.4f}\" if 'quality_score' in df.columns else '—'\n    avg_r = f\"{df['compression_ratio'].mean():.4f}\" if 'compression_ratio' in df.columns else '—'\n    return df, avg_q, avg_r, ''\n\n\ndef on_row_select(evt: gr.SelectData, df: pd.DataFrame):\n    if df is None or df.empty:\n        return None, '', 'No rows available.'\n    row_idx = evt.index[0]\n    run_id  = int(df.iloc[row_idx]['id'])\n    record  = get_run(run_id)\n    if not record:\n        return None, '', f'Row {run_id} not found in database.'\n    return run_id, render_diff_html(record), f'Row {run_id} selected — click Delete to remove.'\n\n\ndef delete_selected(run_id, selected_cols):\n    if run_id is None:\n        df, avg_q, avg_r, _ = load_history(selected_cols)\n        return df, avg_q, avg_r, None, '', 'No row selected.'\n    delete_run(run_id)\n    df, avg_q, avg_r, _ = load_history(selected_cols)\n    return df, avg_q, avg_r, None, '', f'Row {run_id} deleted.'\n\n\n# ══════════════════════════════════════════════════════════════════════════════\n# BUILD APP\n# ══════════════════════════════════════════════════════════════════════════════\n\ndef build_app() -> gr.Blocks:\n    with gr.Blocks(title=APP_TITLE) as app:\n\n        # ── Compress tab ──────────────────────────────────────────────────\n        with gr.Tab('Compress'):\n            gr.Markdown('## TinyPress — Prompt Compression Engine')\n            gr.Markdown(\n                'Paste any long text. Set your token budget. Get a compressed version '\n                'that preserves intent — scored for quality.'\n            )\n\n            with gr.Accordion('Model Settings', open=False):\n                gr.Markdown('**Compression Model**')\n                model_dropdown = gr.Dropdown(\n                    choices=AVAILABLE_MODELS, value=LLM_MODEL,\n                    label='Compression Model', allow_custom_value=True,\n                )\n                load_model_btn = gr.Button('Load Model', variant='secondary')\n                model_status   = gr.Textbox(label='Model Status', value=f'Active: {LLM_MODEL}', interactive=False)\n\n                gr.Divider()\n\n                gr.Markdown('**Scoring Embedder**')\n                embedder_dropdown = gr.Dropdown(\n                    choices=AVAILABLE_EMBEDDER_MODELS, value=EMBEDDER_MODEL,\n                    label='Embedder Model', allow_custom_value=True,\n                )\n                embedder_info_panel = gr.Markdown(value=EMBEDDER_INFO.get(EMBEDDER_MODEL, ''))\n                load_embedder_btn = gr.Button('Load Embedder', variant='secondary')\n                embedder_status   = gr.Textbox(label='Embedder Status', value=f'Active: {EMBEDDER_MODEL}', interactive=False)\n\n            with gr.Row():\n                with gr.Column():\n                    input_text = gr.Textbox(label='Input Text', lines=12, placeholder='Paste your text here...')\n                    token_toggle_btn = gr.Button(_BTN_SHOW, variant='secondary', size='sm')\n                    token_panel      = gr.HTML(value='')\n                    tokens_visible   = gr.State(value=False)\n                    target_slider    = gr.Slider(minimum=100, maximum=1000, value=DEFAULT_TARGET_TOKENS, step=50, label='Target Token Budget')\n                    status_banner    = gr.HTML(value=_STATUS_EMPTY)\n                    compress_btn     = gr.Button('Compress', variant='primary')\n\n                with gr.Column():\n                    output_text = gr.Textbox(label='Compressed Output', lines=12)\n                    with gr.Row():\n                        input_tok  = gr.Number(label='Input Tokens',  interactive=False)\n                        output_tok = gr.Number(label='Output Tokens', interactive=False)\n                    with gr.Row():\n                        ratio   = gr.Number(label='Compression Ratio',   interactive=False)\n                        quality = gr.Number(label='Quality Score (0–1)', interactive=False)\n                    gr.Markdown('**Was this compression helpful?**')\n                    with gr.Row():\n                        thumbs_up_btn   = gr.Button('👍  Helpful',     variant='secondary', visible=False, scale=1)\n                        thumbs_down_btn = gr.Button('👎  Not helpful', variant='secondary', visible=False, scale=1)\n                    feedback_status  = gr.Markdown('', visible=False)\n                    comment_box      = gr.Textbox(\n                        label='Add a note (optional)',\n                        placeholder=\"e.g. 'lost key dates', 'too short', 'great summary'\",\n                        lines=2, visible=False,\n                    )\n                    save_comment_btn = gr.Button('Save note', variant='secondary', size='sm', visible=False)\n                    comment_saved    = gr.Markdown('', visible=False)\n\n            last_run_id = gr.State(value=None)\n\n            token_toggle_btn.click(fn=toggle_token_panel, inputs=[tokens_visible, input_text], outputs=[tokens_visible, token_panel, token_toggle_btn])\n            input_text.change(fn=update_token_panel, inputs=[input_text, tokens_visible], outputs=[token_panel])\n            _sa = dict(inputs=[input_text, target_slider], outputs=[status_banner])\n            input_text.change(fn=compression_status, **_sa)\n            target_slider.change(fn=compression_status, **_sa)\n            load_model_btn.click(fn=load_model, inputs=[model_dropdown], outputs=[model_status])\n            embedder_dropdown.change(fn=on_embedder_change, inputs=[embedder_dropdown], outputs=[embedder_info_panel])\n            load_embedder_btn.click(fn=load_embedder, inputs=[embedder_dropdown], outputs=[embedder_status])\n            compress_btn.click(\n                fn=run_compression,\n                inputs=[input_text, target_slider],\n                outputs=[output_text, input_tok, output_tok, ratio, quality,\n                         last_run_id, thumbs_up_btn, thumbs_down_btn, feedback_status,\n                         comment_box, save_comment_btn, comment_saved],\n            )\n            thumbs_up_btn.click(\n                fn=lambda run_id: submit_feedback(run_id, 1),\n                inputs=[last_run_id],\n                outputs=[feedback_status, comment_box, save_comment_btn, comment_saved],\n            )\n            thumbs_down_btn.click(\n                fn=lambda run_id: submit_feedback(run_id, -1),\n                inputs=[last_run_id],\n                outputs=[feedback_status, comment_box, save_comment_btn, comment_saved],\n            )\n            save_comment_btn.click(fn=save_comment, inputs=[last_run_id, comment_box], outputs=[comment_saved])\n\n        # ── History tab ───────────────────────────────────────────────────\n        with gr.Tab('History') as history_tab:\n            gr.Markdown('## Compression Run History')\n            with gr.Row():\n                refresh_btn = gr.Button('Refresh', variant='secondary')\n                delete_btn  = gr.Button('Delete Selected Row', variant='stop')\n\n            with gr.Accordion('Column visibility', open=False):\n                col_picker = gr.CheckboxGroup(choices=_ALL_COLS, value=_DEFAULT_COLS, label=None)\n\n            with gr.Row():\n                avg_quality = gr.Textbox(label='Avg Quality Score',     interactive=False)\n                avg_ratio   = gr.Textbox(label='Avg Compression Ratio', interactive=False)\n            history_table = gr.DataFrame(label='Past Runs — click a row to see its diff', interactive=False)\n            delete_status = gr.Textbox(label='Status', value='Click a row to select it.', interactive=False)\n            gr.Markdown('### Side-by-side Diff')\n            diff_panel  = gr.HTML(value='')\n            selected_id = gr.State(value=None)\n\n            _outputs = [history_table, avg_quality, avg_ratio, diff_panel]\n            refresh_btn.click(fn=load_history, inputs=[col_picker], outputs=_outputs)\n            history_tab.select(fn=load_history, inputs=[col_picker], outputs=_outputs)\n            col_picker.change(fn=load_history, inputs=[col_picker], outputs=_outputs)\n            history_table.select(fn=on_row_select, inputs=[history_table], outputs=[selected_id, diff_panel, delete_status])\n            delete_btn.click(\n                fn=delete_selected,\n                inputs=[selected_id, col_picker],\n                outputs=[history_table, avg_quality, avg_ratio, selected_id, diff_panel, delete_status],\n            )\n\n    return app\n\n\napp = build_app()\napp.launch(share=True, server_port=SERVER_PORT)"
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cell-s10-hdr",
-   "metadata": {},
-   "source": [
-    "## Step 10 — Programmatic demo (no UI needed)\n",
-    "\n",
-    "Run this cell to compress a sample text directly and inspect all metrics inline."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cell-demo",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "SAMPLE_TEXT = \"\"\"\n",
-    "The transformer architecture, introduced in the seminal paper Attention Is All You Need by Vaswani et al.\n",
-    "in 2017, fundamentally changed how we approach sequence modelling tasks in natural language processing.\n",
-    "Prior to transformers, recurrent neural networks (RNNs) and long short-term memory (LSTM) networks were\n",
-    "the dominant architectures for tasks such as machine translation, text summarisation, and question answering.\n",
-    "However, these models suffered from several limitations: they processed tokens sequentially, making\n",
-    "parallelisation difficult; they struggled to capture long-range dependencies due to vanishing gradients;\n",
-    "and training was slow even on modern hardware. The transformer addressed all of these issues through\n",
-    "its self-attention mechanism, which allows every token in a sequence to directly attend to every other\n",
-    "token in a single operation. Multi-head attention further extends this by running several attention\n",
-    "functions in parallel, capturing different types of relationships between tokens simultaneously.\n",
-    "Position encodings are added to token embeddings to give the model a sense of sequence order, since\n",
-    "unlike RNNs the architecture has no inherent notion of position. Feed-forward sub-layers, layer\n",
-    "normalisation, and residual connections complete each transformer block. The result is a model that\n",
-    "trains faster, scales better with data and compute, and generalises more effectively than its\n",
-    "predecessors, setting the stage for large language models like GPT, BERT, and the entire modern\n",
-    "LLM ecosystem.\n",
-    "\"\"\".strip()\n",
-    "\n",
-    "TARGET = 150  # token budget\n",
-    "\n",
-    "input_tok_count = count_tokens(SAMPLE_TEXT)\n",
-    "print(f'Input tokens : {input_tok_count}')\n",
-    "print(f'Target tokens: {TARGET}')\n",
-    "print(f'Status       : {\"ready to compress\" if input_tok_count > TARGET else \"already within budget\"}')\n",
-    "print()\n",
-    "\n",
-    "t0 = time.perf_counter()\n",
-    "compressed, in_tok, out_tok = compress(SAMPLE_TEXT, TARGET)\n",
-    "elapsed = round((time.perf_counter() - t0) * 1000, 1)\n",
-    "\n",
-    "score = semantic_score(SAMPLE_TEXT, compressed)\n",
-    "ratio = round(out_tok / in_tok, 4)\n",
-    "\n",
-    "print('─' * 60)\n",
-    "print(compressed)\n",
-    "print('─' * 60)\n",
-    "print(f'Output tokens    : {out_tok}')\n",
-    "print(f'Compression ratio: {ratio}')\n",
-    "print(f'Quality score    : {score}')\n",
-    "print(f'Duration         : {elapsed} ms')\n",
-    "print(f'Model            : {get_current_model_id()}')\n",
-    "print(f'Tokenizer        : {get_current_tokenizer_id()}')"
-   ]
-  }
- ]
-}
\ No newline at end of file
+  "nbformat": 4,
+  "nbformat_minor": 5
+}