{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 🧪 WFGY Diagnostic Notebook (v0.5 — widget-free)\n",
    "Measure **ΔS** and classify common reasoning failures with one click.\n",
    "No API key • No widgets • Works in any Colab session."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 📐 Core formula\n",
    "$$\\Delta S = 1 - \\cos\\theta\\bigl(I, G\\bigr)$$  \n",
    "*$I$ = current-step embedding, $G$ = ground-truth / prompt embedding.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 🔎 What does ΔS mean?\n",
    "\n",
    "ΔS = 1 − cosθ(I, G)  \n",
    "*I = Prompt intent, G = Generated output*\n",
    "\n",
    "📉 **Low ΔS** → Stable  \n",
    "🔺 **Medium ΔS** → Interpretation Collapse  \n",
    "🚨 **High ΔS** → Hallucination or Chunk Drift"
   ]
  },
  {
   "cell_type": "code",
   "metadata": { "id": "install" },
   "source": [
    "!pip -q install sentence-transformers --upgrade"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": { "id": "imports" },
   "source": [
    "from sentence_transformers import SentenceTransformer, util"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": { "id": "model" },
   "source": [
    "model = SentenceTransformer('all-MiniLM-L6-v2')"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": { "id": "functions" },
   "source": [
    "def delta_s(a_vec, b_vec):\n",
    "    return 1 - util.cos_sim(a_vec, b_vec).item()\n",
    "\n",
    "def classify_failure(dS):\n",
    "    if dS > 0.80:\n",
    "        return ('No.1 Hallucination / Chunk Drift', 'Apply BBMC + boundary check')\n",
    "    if dS > 0.60:\n",
    "        return ('No.2 Interpretation Collapse', 'Insert λ_observe checkpoint')\n",
    "    if dS > 0.40:\n",
    "        return ('No.6 Logic Collapse', 'Trigger BBCR fallback')\n",
    "    if dS > 0.25:\n",
    "        return ('No.9 Entropy Collapse', 'Use BBAM attention modulation')\n",
    "    return ('Stable (ΔS ≤ 0.25)', 'No action — pass')"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ✏️ Edit & run\n",
    "Replace the text in **Prompt** / **Answer**, press ▶️ to diagnose."
   ]
  },
  {
   "cell_type": "code",
   "metadata": { "id": "user_input" },
   "source": [
    "prompt = \"### Product Review\\nThe new phone has a crystal-clear display and fast charging…\"\n",
    "answer = \"The capital of France is Paris. Also, your phone seems great!\"\n",
    "\n",
    "# --- diagnostic ---\n",
    "dS = delta_s(model.encode(prompt, convert_to_tensor=True),\n",
    "             model.encode(answer, convert_to_tensor=True))\n",
    "\n",
    "failure, fix = classify_failure(dS)\n",
    "print(\"ΔS :\", f\"{dS:.3f}\")\n",
    "print(\"Failure detected :\", failure)\n",
    "print(\"Suggested fix    :\", fix)"
   ],
   "execution_count": null,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Next Steps  \n",
    "* Explore **Problem Map 1.0** & **2.0** for full 16 failure modes.  \n",
    "* Read about **λ_observe, E_resonance, BBCR** in the WFGY 1.0 paper.  \n",
    "* Fork & extend this notebook — MIT-licensed.  \n",
    "\n",
    "> Repo → https://github.com/onestardao/WFGY"
   ]
  }
 ],
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