{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "jKnw99avkQjs", "outputId": "2a28be57-562c-43af-84ed-be153317bda5" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m697.4/697.4 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.7/60.7 MB\u001b[0m \u001b[31m16.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m527.0/527.0 kB\u001b[0m \u001b[31m30.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m48.9/48.9 MB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h" ] } ], "source": [ "!pip install -q \\\n", " \"transformers>=4.38\" \\\n", " \"accelerate>=0.27\" \\\n", " \"trl>=0.10\" \\\n", " \"peft>=0.9\" \\\n", " \"bitsandbytes>=0.42\" \\\n", " \"datasets>=2.17\" \\\n", " \"requests>=2.31\" \\\n", " \"pandas>=2.0\" \\\n", " \"fredapi\" \\\n", " \"world-bank-data\" \\\n", " \"plotly>=5.18\" \\\n", " \"rich>=13.0\" \\\n", " \"tenacity>=8.2\"\n", "\n", "# After install: Runtime → Restart session → run from Cell 2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 109 }, "id": "WEJcP68rlWKS", "outputId": "d91e3c2d-2296-4ad5-f580-2d16cb7aa43c" }, "outputs": [ { "data": { "text/html": [ "
───────────────────────────────────────── CivicAI Advanced — System Ready ─────────────────────────────────────────\n",
              "
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 PyTorch    2.10.0+cpu             \n",
              " Device     CPU                    \n",
              " BF16/FP16  bf16=False  fp16=False \n",
              " Timestamp  2026-04-26 02:19:19    \n",
              "
\n" ], "text/plain": [ " \u001b[36mPyTorch\u001b[0m 2.10.0+cpu \n", " \u001b[36mDevice\u001b[0m CPU \n", " \u001b[36mBF16/FP16\u001b[0m bf16=False fp16=False \n", " \u001b[36mTimestamp\u001b[0m 2026-04-26 02:19:19 \n" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import os, re, json, math, time, random, inspect, warnings, logging\n", "from datetime import datetime\n", "from typing import Dict, List, Optional, Tuple\n", "from pathlib import Path\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import torch\n", "import requests\n", "import plotly.graph_objects as go\n", "import plotly.express as px\n", "from plotly.subplots import make_subplots\n", "from tenacity import retry, stop_after_attempt, wait_exponential\n", "from rich.console import Console\n", "from rich.table import Table\n", "from rich.progress import Progress, SpinnerColumn, TextColumn\n", "from rich import print as rprint\n", "\n", "warnings.filterwarnings(\"ignore\")\n", "logging.basicConfig(level=logging.ERROR)\n", "\n", "console = Console()\n", "\n", "# ── Hardware detection ────────────────────────────────────────────────────────\n", "CUDA_OK = torch.cuda.is_available()\n", "if CUDA_OK:\n", " CAP = torch.cuda.get_device_capability()\n", " USE_BF16 = CAP[0] >= 8\n", " USE_FP16 = not USE_BF16\n", " GPU_NAME = torch.cuda.get_device_name(0)\n", "else:\n", " USE_BF16 = USE_FP16 = False\n", " GPU_NAME = \"CPU\"\n", "\n", "DEVICE = \"cuda\" if CUDA_OK else \"cpu\"\n", "\n", "# ── Paths ─────────────────────────────────────────────────────────────────────\n", "Path(\"assets\").mkdir(exist_ok=True)\n", "Path(\"checkpoints\").mkdir(exist_ok=True)\n", "Path(\"logs\").mkdir(exist_ok=True)\n", "\n", "console.rule(\"[bold cyan]CivicAI Advanced — System Ready\")\n", "table = Table(show_header=False, box=None)\n", "table.add_row(\"[cyan]PyTorch\", torch.__version__)\n", "table.add_row(\"[cyan]Device\", GPU_NAME)\n", "table.add_row(\"[cyan]BF16/FP16\", f\"bf16={USE_BF16} fp16={USE_FP16}\")\n", "table.add_row(\"[cyan]Timestamp\", datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))\n", "console.print(table)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 325, "referenced_widgets": [ "f83489a9561a4b73b8d8eddc4b00c34e", "9895d266524c4a9c817912cdb008677a", "7db3437ccf2b430c9e795364df69595c", "5074b39aac7e43f295767192a8c6dfae", "188f4333d05b4bdb8dbf62f789a45620", "621d9a6d432840a58a554fbc6cfb3eed", "1f2303b42b3c4340a284c7126cf1fd37", "c41c26ae9ba443a5b6b942d69cf323be", "3c361255933741089182c76f833bc2c7", "b38158c0ff0c432893a6e8420cb5f6a9", "3f6ada08370341f89dbda7720a82c05a", "1d8f93d877d247af95b009db2b659a65" ] }, "id": "qbbp3ggXlaVG", "outputId": "6b7e3185-d83c-4d07-9fea-78ab232123c3" }, "outputs": [ { "data": { "text/html": [ "
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       country  inflation_pct  unemployment_pct  health_exp_gdp  gdp_growth\n",
              " United States       2.949525             4.198       16.694149    2.793001\n",
              "         India       4.953036             4.219        3.339440    6.494766\n",
              "United Kingdom       3.271573             4.746       11.133358    1.126423\n",
              "       Germany       2.256498             3.711       12.266553   -0.495852\n",
              "         Japan       2.738537             2.451       10.739795    0.104309\n",
              "        Brazil       4.367464             5.970        9.730927    3.419315\n",
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              "       country  inflation_pct  unemployment_pct  health_exp_gdp  gdp_growth\n",
              " United States       \u001b[1;36m2.949525\u001b[0m             \u001b[1;36m4.198\u001b[0m       \u001b[1;36m16.694149\u001b[0m    \u001b[1;36m2.793001\u001b[0m\n",
              "         India       \u001b[1;36m4.953036\u001b[0m             \u001b[1;36m4.219\u001b[0m        \u001b[1;36m3.339440\u001b[0m    \u001b[1;36m6.494766\u001b[0m\n",
              "United Kingdom       \u001b[1;36m3.271573\u001b[0m             \u001b[1;36m4.746\u001b[0m       \u001b[1;36m11.133358\u001b[0m    \u001b[1;36m1.126423\u001b[0m\n",
              "       Germany       \u001b[1;36m2.256498\u001b[0m             \u001b[1;36m3.711\u001b[0m       \u001b[1;36m12.266553\u001b[0m   \u001b[1;36m-0.495852\u001b[0m\n",
              "         Japan       \u001b[1;36m2.738537\u001b[0m             \u001b[1;36m2.451\u001b[0m       \u001b[1;36m10.739795\u001b[0m    \u001b[1;36m0.104309\u001b[0m\n",
              "        Brazil       \u001b[1;36m4.367464\u001b[0m             \u001b[1;36m5.970\u001b[0m        \u001b[1;36m9.730927\u001b[0m    \u001b[1;36m3.419315\u001b[0m\n"
            ]
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        }
      ],
      "source": [
        "class RealTimeDataFetcher:\n",
        "    \"\"\"\n",
        "    Fetches live economic indicators from:\n",
        "      • World Bank Open API  (no key required)\n",
        "      • FRED / St. Louis Fed (free key via fredapi)\n",
        "      • BLS  (Bureau of Labor Statistics — no key for basic tier)\n",
        "      • REST Countries       (social / governance proxies)\n",
        "    Falls back to realistic historical means if any API is unavailable.\n",
        "    \"\"\"\n",
        "\n",
        "    WORLD_BANK_BASE = \"https://api.worldbank.org/v2\"\n",
        "    BLS_BASE        = \"https://api.bls.gov/publicAPI/v1/timeseries/data\"\n",
        "    REST_COUNTRIES  = \"https://restcountries.com/v3.1/alpha\"\n",
        "\n",
        "    # World Bank indicator codes\n",
        "    WB_INDICATORS = {\n",
        "        \"inflation\"   : \"FP.CPI.TOTL.ZG\",   # CPI inflation %\n",
        "        \"unemployment\": \"SL.UEM.TOTL.ZS\",    # Unemployment % of labour force\n",
        "        \"health_exp\"  : \"SH.XPD.CHEX.GD.ZS\",# Health expenditure % of GDP\n",
        "        \"life_expect\" : \"SP.DYN.LE00.IN\",    # Life expectancy at birth\n",
        "        \"gdp_growth\"  : \"NY.GDP.MKTP.KD.ZG\", # GDP growth %\n",
        "        \"homicide\"    : \"VC.IHR.PSRC.P5\",    # Intentional homicides per 100k\n",
        "    }\n",
        "\n",
        "    # Country ISO codes supported\n",
        "    COUNTRIES = {\n",
        "        \"USA\": {\"iso2\": \"US\", \"iso3\": \"USA\", \"name\": \"United States\"},\n",
        "        \"IND\": {\"iso2\": \"IN\", \"iso3\": \"IND\", \"name\": \"India\"},\n",
        "        \"GBR\": {\"iso2\": \"GB\", \"iso3\": \"GBR\", \"name\": \"United Kingdom\"},\n",
        "        \"DEU\": {\"iso2\": \"DE\", \"iso3\": \"DEU\", \"name\": \"Germany\"},\n",
        "        \"JPN\": {\"iso2\": \"JP\", \"iso3\": \"JPN\", \"name\": \"Japan\"},\n",
        "        \"BRA\": {\"iso2\": \"BR\", \"iso3\": \"BRA\", \"name\": \"Brazil\"},\n",
        "    }\n",
        "\n",
        "    # Realistic fallback values (5-year historical means, 2019-2023)\n",
        "    FALLBACKS = {\n",
        "        \"USA\": {\"inflation\":3.8,\"unemployment\":4.8,\"health_exp\":17.2,\"life_expect\":77.5,\"gdp_growth\":2.1,\"homicide\":6.5},\n",
        "        \"IND\": {\"inflation\":5.5,\"unemployment\":7.2,\"health_exp\":3.3, \"life_expect\":69.4,\"gdp_growth\":5.8,\"homicide\":2.8},\n",
        "        \"GBR\": {\"inflation\":3.2,\"unemployment\":4.2,\"health_exp\":10.9,\"life_expect\":80.4,\"gdp_growth\":1.4,\"homicide\":1.2},\n",
        "        \"DEU\": {\"inflation\":2.8,\"unemployment\":3.5,\"health_exp\":12.8,\"life_expect\":80.6,\"gdp_growth\":0.9,\"homicide\":0.9},\n",
        "        \"JPN\": {\"inflation\":1.2,\"unemployment\":2.8,\"health_exp\":10.9,\"life_expect\":84.3,\"gdp_growth\":0.7,\"homicide\":0.2},\n",
        "        \"BRA\": {\"inflation\":6.9,\"unemployment\":11.0,\"health_exp\":9.9,\"life_expect\":75.5,\"gdp_growth\":1.2,\"homicide\":22.4},\n",
        "    }\n",
        "\n",
        "    def __init__(self, cache_ttl_seconds: int = 3600):\n",
        "        self._cache: Dict[str, Tuple[float, dict]] = {}\n",
        "        self.cache_ttl = cache_ttl_seconds\n",
        "        self.session   = requests.Session()\n",
        "        self.session.headers.update({\"User-Agent\": \"CivicAI/2.0\"})\n",
        "\n",
        "    @retry(stop=stop_after_attempt(3), wait=wait_exponential(min=1, max=8))\n",
        "    def _wb_fetch(self, country_iso2: str, indicator: str) -> Optional[float]:\n",
        "        \"\"\"Fetch latest non-null value from World Bank API.\"\"\"\n",
        "        url = (f\"{self.WORLD_BANK_BASE}/country/{country_iso2}/indicator/{indicator}\"\n",
        "               f\"?format=json&mrv=5&per_page=5\")\n",
        "        r   = self.session.get(url, timeout=10)\n",
        "        r.raise_for_status()\n",
        "        data = r.json()\n",
        "        if len(data) < 2 or not data[1]:\n",
        "            return None\n",
        "        for entry in data[1]:\n",
        "            if entry.get(\"value\") is not None:\n",
        "                return float(entry[\"value\"])\n",
        "        return None\n",
        "\n",
        "    def fetch_country(self, country_code: str = \"USA\") -> dict:\n",
        "        \"\"\"\n",
        "        Returns normalised economic state for a country.\n",
        "        Uses cache → World Bank API → fallback in that order.\n",
        "        \"\"\"\n",
        "        cache_key = f\"{country_code}_{int(time.time() // self.cache_ttl)}\"\n",
        "        if cache_key in self._cache:\n",
        "            return self._cache[cache_key]\n",
        "\n",
        "        meta  = self.COUNTRIES.get(country_code, self.COUNTRIES[\"USA\"])\n",
        "        iso2  = meta[\"iso2\"]\n",
        "        raw   = {}\n",
        "\n",
        "        with Progress(SpinnerColumn(), TextColumn(\"[cyan]Fetching {task.description}\"),\n",
        "                      transient=True) as prog:\n",
        "            t = prog.add_task(f\"live data for {meta['name']}\")\n",
        "            for key, indicator in self.WB_INDICATORS.items():\n",
        "                try:\n",
        "                    val = self._wb_fetch(iso2, indicator)\n",
        "                    raw[key] = val if val is not None else self.FALLBACKS[country_code][key]\n",
        "                except Exception:\n",
        "                    raw[key] = self.FALLBACKS[country_code][key]\n",
        "\n",
        "        # ── Normalise to [0, 1] for the RL environment ────────────────────\n",
        "        state = {\n",
        "            # Lower inflation = better; 0 %→1.0, ≥15 %→0.0\n",
        "            \"inflation\"   : max(0.0, min(1.0, 1 - raw[\"inflation\"]   / 15.0)),\n",
        "            # Higher employment = better\n",
        "            \"employment\"  : max(0.0, min(1.0, 1 - raw[\"unemployment\"]/ 25.0)),\n",
        "            # Higher health expenditure + life expectancy = better\n",
        "            \"health\"      : max(0.0, min(1.0, (raw[\"health_exp\"] / 20.0 +\n",
        "                                               raw[\"life_expect\"] / 90.0) / 2)),\n",
        "            # GDP growth proxy for satisfaction\n",
        "            \"satisfaction\": max(0.0, min(1.0, (raw[\"gdp_growth\"] + 5) / 15.0)),\n",
        "            # Lower homicide = better\n",
        "            \"crime\"       : max(0.0, min(1.0, 1 - raw[\"homicide\"]   / 50.0)),\n",
        "        }\n",
        "\n",
        "        # Attach raw for reporting\n",
        "        state[\"_raw\"]    = raw\n",
        "        state[\"_country\"]= meta[\"name\"]\n",
        "        state[\"_fetched\"]= datetime.now().isoformat()\n",
        "\n",
        "        self._cache[cache_key] = state\n",
        "        return state\n",
        "\n",
        "    def fetch_all_countries(self) -> Dict[str, dict]:\n",
        "        results = {}\n",
        "        for code in self.COUNTRIES:\n",
        "            console.log(f\"[dim]→ fetching {code}\")\n",
        "            results[code] = self.fetch_country(code)\n",
        "        return results\n",
        "\n",
        "    def to_dataframe(self, all_data: Dict[str, dict]) -> pd.DataFrame:\n",
        "        rows = []\n",
        "        for code, state in all_data.items():\n",
        "            raw = state.get(\"_raw\", {})\n",
        "            rows.append({\n",
        "                \"country\"     : state.get(\"_country\", code),\n",
        "                \"code\"        : code,\n",
        "                \"inflation_pct\": raw.get(\"inflation\", 0),\n",
        "                \"unemployment_pct\": raw.get(\"unemployment\", 0),\n",
        "                \"health_exp_gdp\": raw.get(\"health_exp\", 0),\n",
        "                \"life_expect\"   : raw.get(\"life_expect\", 0),\n",
        "                \"gdp_growth\"    : raw.get(\"gdp_growth\", 0),\n",
        "                \"homicide_rate\" : raw.get(\"homicide\", 0),\n",
        "                # normalised\n",
        "                \"norm_inflation\"   : state[\"inflation\"],\n",
        "                \"norm_employment\"  : state[\"employment\"],\n",
        "                \"norm_health\"      : state[\"health\"],\n",
        "                \"norm_satisfaction\": state[\"satisfaction\"],\n",
        "                \"norm_crime\"       : state[\"crime\"],\n",
        "                \"fetched_at\"       : state.get(\"_fetched\"),\n",
        "            })\n",
        "        return pd.DataFrame(rows)\n",
        "\n",
        "\n",
        "# Instantiate & fetch\n",
        "fetcher     = RealTimeDataFetcher(cache_ttl_seconds=3600)\n",
        "all_data    = fetcher.fetch_all_countries()\n",
        "df_world    = fetcher.to_dataframe(all_data)\n",
        "\n",
        "console.rule(\"[bold green]Live Data Fetched\")\n",
        "console.print(df_world[[\"country\",\"inflation_pct\",\"unemployment_pct\",\n",
        "                          \"health_exp_gdp\",\"gdp_growth\"]].to_string(index=False))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 647
        },
        "id": "wEIL-B2flmFU",
        "outputId": "9d43d7d0-e372-445c-cc2f-0a9cfe7821ca"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "\n",
              "\n",
              "    
                            \n",
              "                        
                    
\n",
              "\n",
              ""
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "
[02:20:36] ✓ Dashboard saved → assets/global_dashboard.html                                        4082134586.py:34\n",
              "
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            ],
            "text/plain": [
              "\u001b[2;36m[02:20:36]\u001b[0m\u001b[2;36m \u001b[0m\u001b[32m✓ Dashboard saved → assets/global_dashboard.html\u001b[0m                                        \u001b]8;id=756173;file:///tmp/ipykernel_2561/4082134586.py\u001b\\\u001b[2m4082134586.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=566577;file:///tmp/ipykernel_2561/4082134586.py#34\u001b\\\u001b[2m34\u001b[0m\u001b]8;;\u001b\\\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def plot_global_dashboard(df: pd.DataFrame) -> None:\n",
        "    fig = make_subplots(\n",
        "        rows=2, cols=3,\n",
        "        subplot_titles=(\n",
        "            \"Inflation (%)\", \"Unemployment (%)\", \"Health Exp (% GDP)\",\n",
        "            \"Life Expectancy (yrs)\", \"GDP Growth (%)\", \"Homicide Rate (per 100k)\"\n",
        "        ),\n",
        "    )\n",
        "    cols_raw = [\"inflation_pct\",\"unemployment_pct\",\"health_exp_gdp\",\n",
        "                \"life_expect\",\"gdp_growth\",\"homicide_rate\"]\n",
        "    colors   = px.colors.qualitative.Bold\n",
        "\n",
        "    for i, col in enumerate(cols_raw):\n",
        "        r, c = divmod(i, 3)\n",
        "        fig.add_trace(\n",
        "            go.Bar(\n",
        "                x=df[\"country\"], y=df[col],\n",
        "                marker_color=colors,\n",
        "                showlegend=False,\n",
        "                text=df[col].round(1), textposition=\"outside\"\n",
        "            ),\n",
        "            row=r+1, col=c+1\n",
        "        )\n",
        "\n",
        "    fig.update_layout(\n",
        "        title_text=\"🌍 CivivAI — Real-Time Global Economic Dashboard\",\n",
        "        title_font_size=20,\n",
        "        height=600, template=\"plotly_dark\",\n",
        "        paper_bgcolor=\"#0d1117\", plot_bgcolor=\"#0d1117\",\n",
        "        font=dict(color=\"#e6edf3\"),\n",
        "    )\n",
        "    fig.show()\n",
        "    fig.write_html(\"assets/global_dashboard.html\")\n",
        "    console.log(\"[green]✓ Dashboard saved → assets/global_dashboard.html\")\n",
        "\n",
        "plot_global_dashboard(df_world)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "r9AXHYEiluMx",
        "outputId": "f52ebc5f-74e8-444e-8ff0-2d3aa635b0fa"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "
─────────────────────────────────────────── Advanced Environment Ready ────────────────────────────────────────────\n",
              "
\n"
            ],
            "text/plain": [
              "\u001b[92m─────────────────────────────────────────── \u001b[0m\u001b[1;32mAdvanced Environment Ready\u001b[0m\u001b[92m ────────────────────────────────────────────\u001b[0m\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "
Initial state (USA, real data): {'inflation': 0.7922, 'employment': 0.8618, 'health': 0.801, 'satisfaction': 0.543,\n",
              "'crime': 0.8937}\n",
              "
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            ],
            "text/plain": [
              "Initial state \u001b[1m(\u001b[0mUSA, real data\u001b[1m)\u001b[0m: \u001b[1m{\u001b[0m\u001b[32m'inflation'\u001b[0m: \u001b[1;36m0.7922\u001b[0m, \u001b[32m'employment'\u001b[0m: \u001b[1;36m0.8618\u001b[0m, \u001b[32m'health'\u001b[0m: \u001b[1;36m0.801\u001b[0m, \u001b[32m'satisfaction'\u001b[0m: \u001b[1;36m0.543\u001b[0m,\n",
              "\u001b[32m'crime'\u001b[0m: \u001b[1;36m0.8937\u001b[0m\u001b[1m}\u001b[0m\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "class AdvancedCivicAIEnv:\n",
        "    \"\"\"\n",
        "    Production-grade multi-country civic environment.\n",
        "    • Initialises from real World Bank data\n",
        "    • Supports 6 countries and 4 policy tasks\n",
        "    • Action space: 5-dimensional continuous [0,1]\n",
        "    • Observation: 10-dimensional (5 state + 5 delta from last step)\n",
        "    • Reward: weighted multi-objective (Pareto-style)\n",
        "    • Includes shock events (recession, pandemic proxy, crime spike)\n",
        "    \"\"\"\n",
        "\n",
        "    TASKS = {\n",
        "        \"stabilize_economy\"  : {\"inflation_weight\":0.4, \"employment_weight\":0.3, \"health_weight\":0.15, \"satisfaction_weight\":0.1, \"crime_weight\":0.05},\n",
        "        \"improve_health\"     : {\"inflation_weight\":0.1, \"employment_weight\":0.2, \"health_weight\":0.5,  \"satisfaction_weight\":0.15,\"crime_weight\":0.05},\n",
        "        \"reduce_crime\"       : {\"inflation_weight\":0.1, \"employment_weight\":0.2, \"health_weight\":0.2,  \"satisfaction_weight\":0.1, \"crime_weight\":0.4},\n",
        "        \"maximize_wellbeing\" : {\"inflation_weight\":0.2, \"employment_weight\":0.2, \"health_weight\":0.2,  \"satisfaction_weight\":0.2, \"crime_weight\":0.2},\n",
        "    }\n",
        "\n",
        "    SHOCK_EVENTS = [\n",
        "        {\"name\":\"recession\",  \"prob\":0.02, \"effect\":{\"inflation\":+0.15,\"employment\":-0.12,\"satisfaction\":-0.1}},\n",
        "        {\"name\":\"pandemic\",   \"prob\":0.01, \"effect\":{\"health\":-0.2,    \"employment\":-0.1, \"satisfaction\":-0.15}},\n",
        "        {\"name\":\"crime_spike\",\"prob\":0.02, \"effect\":{\"crime\":-0.15,    \"satisfaction\":-0.08}},\n",
        "        {\"name\":\"boom\",       \"prob\":0.02, \"effect\":{\"employment\":+0.1,\"satisfaction\":+0.1,\"inflation\":+0.05}},\n",
        "    ]\n",
        "\n",
        "    def __init__(self, fetcher: RealTimeDataFetcher, default_country: str = \"USA\"):\n",
        "        self.fetcher         = fetcher\n",
        "        self.default_country = default_country\n",
        "        self._prev_state     = None\n",
        "        self.step_count      = 0\n",
        "        self.shock_log       = []\n",
        "        self.state_data      = {}\n",
        "\n",
        "    def reset(self, task_id: str = \"stabilize_economy\", country: str = None) -> dict:\n",
        "        country          = country or self.default_country\n",
        "        self.task_id     = task_id\n",
        "        self.weights     = self.TASKS[task_id]\n",
        "        self.step_count  = 0\n",
        "        self.shock_log   = []\n",
        "\n",
        "        # Load real data as starting state\n",
        "        live             = self.fetcher.fetch_country(country)\n",
        "        self.state_data  = {k: live[k] for k in [\"inflation\",\"employment\",\"health\",\"satisfaction\",\"crime\"]}\n",
        "\n",
        "        # Add small noise so each episode is unique\n",
        "        for k in self.state_data:\n",
        "            self.state_data[k] = float(np.clip(\n",
        "                self.state_data[k] + np.random.normal(0, 0.02), 0.0, 1.0\n",
        "            ))\n",
        "\n",
        "        self._prev_state = dict(self.state_data)\n",
        "        return self._build_obs()\n",
        "\n",
        "    def _build_obs(self) -> dict:\n",
        "        \"\"\"10-dim observation: current state + delta from previous step.\"\"\"\n",
        "        obs = dict(self.state_data)\n",
        "        obs[\"_task\"]  = self.task_id\n",
        "        obs[\"_step\"]  = self.step_count\n",
        "        if self._prev_state:\n",
        "            for k in [\"inflation\",\"employment\",\"health\",\"satisfaction\",\"crime\"]:\n",
        "                obs[f\"d_{k}\"] = self.state_data[k] - self._prev_state[k]\n",
        "        else:\n",
        "            for k in [\"inflation\",\"employment\",\"health\",\"satisfaction\",\"crime\"]:\n",
        "                obs[f\"d_{k}\"] = 0.0\n",
        "        return obs\n",
        "\n",
        "    def _apply_shocks(self):\n",
        "        \"\"\"Stochastic external shock events.\"\"\"\n",
        "        for shock in self.SHOCK_EVENTS:\n",
        "            if np.random.random() < shock[\"prob\"]:\n",
        "                self.shock_log.append({\"step\": self.step_count, \"event\": shock[\"name\"]})\n",
        "                for k, delta in shock[\"effect\"].items():\n",
        "                    if k in self.state_data:\n",
        "                        self.state_data[k] = float(np.clip(self.state_data[k] + delta, 0.0, 1.0))\n",
        "                console.log(f\"[yellow]⚡ Shock event: {shock['name']} at step {self.step_count}\")\n",
        "\n",
        "    def step(self, action: dict) -> Tuple[dict, float, bool, dict]:\n",
        "        \"\"\"\n",
        "        action keys: tax, jobs, healthcare, education, infrastructure\n",
        "        Each in [0, 1] — represents budget allocation intensity.\n",
        "        \"\"\"\n",
        "        self._prev_state = dict(self.state_data)\n",
        "\n",
        "        # Policy effects (with diminishing returns via sqrt)\n",
        "        tax          = action.get(\"tax\",           0.5)\n",
        "        jobs         = action.get(\"jobs\",          0.5)\n",
        "        healthcare   = action.get(\"healthcare\",    0.5)\n",
        "        education    = action.get(\"education\",     0.5)\n",
        "        infra        = action.get(\"infrastructure\",0.5)\n",
        "\n",
        "        self.state_data[\"inflation\"]    = np.clip(\n",
        "            self.state_data[\"inflation\"]    - tax        * 0.08 + jobs * 0.02, 0.0, 1.0)\n",
        "        self.state_data[\"employment\"]   = np.clip(\n",
        "            self.state_data[\"employment\"]   + jobs       * 0.06 + infra * 0.02, 0.0, 1.0)\n",
        "        self.state_data[\"health\"]       = np.clip(\n",
        "            self.state_data[\"health\"]       + healthcare * 0.07 + education * 0.02, 0.0, 1.0)\n",
        "        self.state_data[\"satisfaction\"] = np.clip(\n",
        "            self.state_data[\"satisfaction\"] + education  * 0.05 + infra    * 0.03\n",
        "                                            - tax        * 0.03, 0.0, 1.0)\n",
        "        self.state_data[\"crime\"]        = np.clip(\n",
        "            self.state_data[\"crime\"]        + education  * 0.05 + jobs     * 0.03\n",
        "                                            - infra      * 0.01, 0.0, 1.0)\n",
        "\n",
        "        # Gaussian noise\n",
        "        for k in self.state_data:\n",
        "            self.state_data[k] = float(np.clip(\n",
        "                self.state_data[k] + np.random.normal(0, 0.008), 0.0, 1.0))\n",
        "\n",
        "        self._apply_shocks()\n",
        "        self.step_count += 1\n",
        "\n",
        "        reward  = self._compute_reward()\n",
        "        done    = self.step_count >= 50\n",
        "        info    = {\"shocks\": self.shock_log, \"step\": self.step_count}\n",
        "        return self._build_obs(), float(reward), done, info\n",
        "\n",
        "    def _compute_reward(self) -> float:\n",
        "        s = self.state_data\n",
        "        w = self.weights\n",
        "        return (\n",
        "            w[\"inflation_weight\"]    * s[\"inflation\"]    +\n",
        "            w[\"employment_weight\"]   * s[\"employment\"]   +\n",
        "            w[\"health_weight\"]       * s[\"health\"]       +\n",
        "            w[\"satisfaction_weight\"] * s[\"satisfaction\"] +\n",
        "            w[\"crime_weight\"]        * s[\"crime\"]\n",
        "        )\n",
        "\n",
        "    def state_report(self) -> dict:\n",
        "        return {k: round(v, 4) for k, v in self.state_data.items()}\n",
        "\n",
        "\n",
        "# Smoke test\n",
        "env_adv = AdvancedCivicAIEnv(fetcher, default_country=\"USA\")\n",
        "obs     = env_adv.reset(\"stabilize_economy\", \"USA\")\n",
        "console.rule(\"[bold green]Advanced Environment Ready\")\n",
        "console.print(f\"Initial state (USA, real data): {env_adv.state_report()}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3i4Olantl2EW"
      },
      "outputs": [],
      "source": [
        "def build_prompt(obs: dict) -> str:\n",
        "    task_desc = {\n",
        "        \"stabilize_economy\"  : \"Your priority is economic stability: control inflation and protect employment.\",\n",
        "        \"improve_health\"     : \"Your priority is public health: maximize health outcomes and life expectancy.\",\n",
        "        \"reduce_crime\"       : \"Your priority is public safety: reduce crime through investment and employment.\",\n",
        "        \"maximize_wellbeing\" : \"Your priority is overall citizen wellbeing across all dimensions.\",\n",
        "    }.get(obs.get(\"_task\",\"\"), \"Optimize all civic outcomes.\")\n",
        "\n",
        "    return (\n",
        "        f\"You are a senior policy advisor.\\n{task_desc}\\n\\n\"\n",
        "        f\"CURRENT STATE (step {obs.get('_step',0)}):\\n\"\n",
        "        f\"  Inflation score   : {obs.get('inflation',0.5):.3f}  (Δ {obs.get('d_inflation',0):+.3f})\\n\"\n",
        "        f\"  Employment score  : {obs.get('employment',0.5):.3f}  (Δ {obs.get('d_employment',0):+.3f})\\n\"\n",
        "        f\"  Health score      : {obs.get('health',0.5):.3f}  (Δ {obs.get('d_health',0):+.3f})\\n\"\n",
        "        f\"  Satisfaction score: {obs.get('satisfaction',0.5):.3f}  (Δ {obs.get('d_satisfaction',0):+.3f})\\n\"\n",
        "        f\"  Crime score       : {obs.get('crime',0.5):.3f}  (Δ {obs.get('d_crime',0):+.3f})\\n\\n\"\n",
        "        \"OUTPUT FORMAT (all values 0.0–1.0, no other text):\\n\"\n",
        "        \"tax: 0.X, jobs: 0.X, healthcare: 0.X, education: 0.X, infrastructure: 0.X\"\n",
        "    )\n",
        "\n",
        "\n",
        "def parse_action(text: str) -> dict:\n",
        "    \"\"\"5-dimensional action parser with robust regex.\"\"\"\n",
        "    keys = [\"tax\", \"jobs\", \"healthcare\", \"education\", \"infrastructure\"]\n",
        "\n",
        "    def extract(key: str) -> float:\n",
        "        m = re.search(rf\"{key}\\s*:\\s*(\\d*\\.?\\d+)\", text)\n",
        "        if m:\n",
        "            try:\n",
        "                return float(np.clip(float(m.group(1)), 0.0, 1.0))\n",
        "            except ValueError:\n",
        "                pass\n",
        "        return 0.5\n",
        "\n",
        "    return {k: extract(k) for k in keys}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585,
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        },
        "id": "mKGs0I_Cl68D",
        "outputId": "cb3aba1d-a83e-4f4b-87b9-8d3d5c251e6f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
            "WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "87261748ca7d4941a4239b76c10fd8f9",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/665 [00:00─────────────────────────────────────────────────── Model Ready ───────────────────────────────────────────────────\n",
              "\n"
            ],
            "text/plain": [
              "\u001b[92m─────────────────────────────────────────────────── \u001b[0m\u001b[1;32mModel Ready\u001b[0m\u001b[92m ───────────────────────────────────────────────────\u001b[0m\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "
[02:22:51] Model      : gpt2 + LoRA (r=8)                                                          3714833569.py:29\n",
              "
\n"
            ],
            "text/plain": [
              "\u001b[2;36m[02:22:51]\u001b[0m\u001b[2;36m \u001b[0m\u001b[36mModel      : gpt2 + LoRA \u001b[0m\u001b[1;36m(\u001b[0m\u001b[36mr\u001b[0m\u001b[36m=\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;36m)\u001b[0m                                                          \u001b]8;id=980795;file:///tmp/ipykernel_2561/3714833569.py\u001b\\\u001b[2m3714833569.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=235903;file:///tmp/ipykernel_2561/3714833569.py#29\u001b\\\u001b[2m29\u001b[0m\u001b]8;;\u001b\\\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "
           Parameters : 124.7M total                                                               3714833569.py:30\n",
              "
\n"
            ],
            "text/plain": [
              "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[36mParameters : \u001b[0m\u001b[1;36m124.\u001b[0m\u001b[36m7M total\u001b[0m                                                               \u001b]8;id=137043;file:///tmp/ipykernel_2561/3714833569.py\u001b\\\u001b[2m3714833569.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=114016;file:///tmp/ipykernel_2561/3714833569.py#30\u001b\\\u001b[2m30\u001b[0m\u001b]8;;\u001b\\\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "from peft         import LoraConfig, get_peft_model, TaskType\n",
        "\n",
        "MODEL_NAME = \"gpt2\"   # swap to \"gpt2-medium\" or \"distilgpt2\" as needed\n",
        "\n",
        "tokenizer            = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
        "tokenizer.pad_token  = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"left\"\n",
        "\n",
        "base_model = AutoModelForCausalLM.from_pretrained(\n",
        "    MODEL_NAME,\n",
        "    torch_dtype = torch.bfloat16 if USE_BF16 else torch.float32,\n",
        ")\n",
        "\n",
        "# ── Attach LoRA adapters (reduces trainable params by ~90%) ──────────────────\n",
        "lora_cfg = LoraConfig(\n",
        "    task_type    = TaskType.CAUSAL_LM,\n",
        "    r            = 8,           # rank\n",
        "    lora_alpha   = 32,\n",
        "    target_modules = [\"c_attn\"],  # GPT-2 attention projection\n",
        "    lora_dropout = 0.05,\n",
        "    bias         = \"none\",\n",
        ")\n",
        "model = get_peft_model(base_model, lora_cfg)\n",
        "model.print_trainable_parameters()\n",
        "model = model.to(DEVICE)\n",
        "\n",
        "console.rule(\"[bold green]Model Ready\")\n",
        "console.log(f\"[cyan]Model      : {MODEL_NAME} + LoRA (r=8)\")\n",
        "console.log(f\"[cyan]Parameters : {sum(p.numel() for p in model.parameters())/1e6:.1f}M total\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
        },
        "id": "vhKuP2-zmNmm",
        "outputId": "4bf042e7-24ef-466d-a0f8-49152921fee6"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "
[02:23:14] ✓ Dataset: 300 prompts across 4 tasks × 6 countries                                      401992419.py:20\n",
              "
\n"
            ],
            "text/plain": [
              "\u001b[2;36m[02:23:14]\u001b[0m\u001b[2;36m \u001b[0m\u001b[32m✓ Dataset: \u001b[0m\u001b[1;32m300\u001b[0m\u001b[32m prompts across \u001b[0m\u001b[1;32m4\u001b[0m\u001b[32m tasks × \u001b[0m\u001b[1;32m6\u001b[0m\u001b[32m countries\u001b[0m                                      \u001b]8;id=296563;file:///tmp/ipykernel_2561/401992419.py\u001b\\\u001b[2m401992419.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=472526;file:///tmp/ipykernel_2561/401992419.py#20\u001b\\\u001b[2m20\u001b[0m\u001b]8;;\u001b\\\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "
             Sample:                                                                                401992419.py:22\n",
              "           You are a senior policy advisor.                                                                        \n",
              "           Your priority is economic stability: control inflation and protect employment.                          \n",
              "                                                                                                                   \n",
              "           CURRENT STATE (step 0):                                                                                 \n",
              "             Inflation score   : 0.777  (Δ +0.000)                                                                 \n",
              "             Employment score  : 0.836  (Δ +0.000)                                                                 \n",
              "             Health score      : 0.870  (Δ +0.000)                                                                 \n",
              "             Satisfaction score: 0.529  (Δ +0.000)                                                                 \n",
              "             C...                                                                                                  \n",
              "
\n"
            ],
            "text/plain": [
              "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m  Sample:                                                                                \u001b]8;id=110834;file:///tmp/ipykernel_2561/401992419.py\u001b\\\u001b[2m401992419.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=230086;file:///tmp/ipykernel_2561/401992419.py#22\u001b\\\u001b[2m22\u001b[0m\u001b]8;;\u001b\\\n",
              "\u001b[2;36m           \u001b[0mYou are a senior policy advisor.                                                         \u001b[2m               \u001b[0m\n",
              "\u001b[2;36m           \u001b[0mYour priority is economic stability: control inflation and protect employment.           \u001b[2m               \u001b[0m\n",
              "\u001b[2;36m           \u001b[0m                                                                                         \u001b[2m               \u001b[0m\n",
              "\u001b[2;36m           \u001b[0mCURRENT STATE \u001b[1m(\u001b[0mstep \u001b[1;36m0\u001b[0m\u001b[1m)\u001b[0m:                                                                  \u001b[2m               \u001b[0m\n",
              "\u001b[2;36m           \u001b[0m  Inflation score   : \u001b[1;36m0.777\u001b[0m  \u001b[1m(\u001b[0mΔ +\u001b[1;36m0.000\u001b[0m\u001b[1m)\u001b[0m                                                  \u001b[2m               \u001b[0m\n",
              "\u001b[2;36m           \u001b[0m  Employment score  : \u001b[1;36m0.836\u001b[0m  \u001b[1m(\u001b[0mΔ +\u001b[1;36m0.000\u001b[0m\u001b[1m)\u001b[0m                                                  \u001b[2m               \u001b[0m\n",
              "\u001b[2;36m           \u001b[0m  Health score      : \u001b[1;36m0.870\u001b[0m  \u001b[1m(\u001b[0mΔ +\u001b[1;36m0.000\u001b[0m\u001b[1m)\u001b[0m                                                  \u001b[2m               \u001b[0m\n",
              "\u001b[2;36m           \u001b[0m  Satisfaction score: \u001b[1;36m0.529\u001b[0m  \u001b[1m(\u001b[0mΔ +\u001b[1;36m0.000\u001b[0m\u001b[1m)\u001b[0m                                                  \u001b[2m               \u001b[0m\n",
              "\u001b[2;36m           \u001b[0m  C\u001b[33m...\u001b[0m                                                                                   \u001b[2m               \u001b[0m\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from datasets import Dataset\n",
        "\n",
        "NUM_SAMPLES = 300\n",
        "records     = []\n",
        "env_tmp     = AdvancedCivicAIEnv(fetcher)\n",
        "task_list   = list(AdvancedCivicAIEnv.TASKS.keys())\n",
        "country_list= list(RealTimeDataFetcher.COUNTRIES.keys())\n",
        "\n",
        "for i in range(NUM_SAMPLES):\n",
        "    task    = task_list[i % len(task_list)]\n",
        "    country = country_list[i % len(country_list)]\n",
        "    obs     = env_tmp.reset(task, country)\n",
        "    records.append({\n",
        "        \"prompt\"  : build_prompt(obs),\n",
        "        \"task\"    : task,\n",
        "        \"country\" : country,\n",
        "    })\n",
        "\n",
        "train_dataset = Dataset.from_list(records)\n",
        "console.log(f\"[green]✓ Dataset: {len(train_dataset)} prompts across \"\n",
        "            f\"{len(task_list)} tasks × {len(country_list)} countries\")\n",
        "console.log(f\"  Sample:\\n{train_dataset[0]['prompt'][:300]}...\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3luuRr42mTFr"
      },
      "outputs": [],
      "source": [
        "def civic_reward_advanced(prompts, completions, task=None, country=None, **kwargs) -> List[float]:\n",
        "    \"\"\"\n",
        "    Multi-objective GRPO reward function.\n",
        "    Scores: environment reward + format compliance + consistency bonus.\n",
        "    \"\"\"\n",
        "    rewards    = []\n",
        "    env_r      = AdvancedCivicAIEnv(fetcher)\n",
        "    task_list_ = task   if isinstance(task,   list) else [task]   * len(prompts)\n",
        "    country_   = country if isinstance(country,list) else [country]* len(prompts)\n",
        "\n",
        "    for i, (prompt, completion) in enumerate(zip(prompts, completions)):\n",
        "        # Extract text\n",
        "        if isinstance(completion, list) and len(completion) > 0:\n",
        "            text = completion[0].get(\"content\", \"\")\n",
        "        else:\n",
        "            text = str(completion)\n",
        "\n",
        "        action = parse_action(text)\n",
        "\n",
        "        # Environment reward\n",
        "        t = (task_list_[i] if task_list_[i] else \"maximize_wellbeing\")\n",
        "        c = (country_[i]   if country_[i]   else \"USA\")\n",
        "        env_r.reset(t, c)\n",
        "        _, env_rew, _, _ = env_r.step(action)\n",
        "\n",
        "        # Format reward: all 5 keys present\n",
        "        keys_found = sum(\n",
        "            1 for k in [\"tax\",\"jobs\",\"healthcare\",\"education\",\"infrastructure\"]\n",
        "            if re.search(rf\"{k}\\s*:\\s*\\d\", text)\n",
        "        )\n",
        "        fmt_bonus  = (keys_found / 5.0) * 0.15    # up to +0.15\n",
        "\n",
        "        # Diversity bonus: penalise all-same values (lazy policy)\n",
        "        vals = list(action.values())\n",
        "        div_bonus  = float(np.std(vals)) * 0.1     # up to ~+0.05\n",
        "\n",
        "        total = float(env_rew) + fmt_bonus + div_bonus\n",
        "        rewards.append(round(total, 5))\n",
        "\n",
        "    return rewards"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 93
        },
        "id": "R0PzbuWamgEd",
        "outputId": "2756301b-b345-4d37-c532-6a014d3c069c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "
[02:24:46] Skipped unsupported GRPOConfig args: {'max_prompt_length'}                              1096250958.py:30\n",
              "
\n"
            ],
            "text/plain": [
              "\u001b[2;36m[02:24:46]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33mSkipped unsupported GRPOConfig args: \u001b[0m\u001b[1;33m{\u001b[0m\u001b[33m'max_prompt_length'\u001b[0m\u001b[1;33m}\u001b[0m                              \u001b]8;id=505951;file:///tmp/ipykernel_2561/1096250958.py\u001b\\\u001b[2m1096250958.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=648775;file:///tmp/ipykernel_2561/1096250958.py#30\u001b\\\u001b[2m30\u001b[0m\u001b]8;;\u001b\\\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead.\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "
           ✓ GRPOTrainer initialised with LoRA + multi-objective reward                            1096250958.py:41\n",
              "
\n"
            ],
            "text/plain": [
              "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[32m✓ GRPOTrainer initialised with LoRA + multi-objective reward\u001b[0m                            \u001b]8;id=256787;file:///tmp/ipykernel_2561/1096250958.py\u001b\\\u001b[2m1096250958.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=234053;file:///tmp/ipykernel_2561/1096250958.py#41\u001b\\\u001b[2m41\u001b[0m\u001b]8;;\u001b\\\n"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from trl import GRPOConfig, GRPOTrainer\n",
        "\n",
        "valid_params = set(inspect.signature(GRPOConfig.__init__).parameters)\n",
        "\n",
        "all_kwargs = {\n",
        "    \"output_dir\"                  : \"checkpoints/civicai-grpo\",\n",
        "    \"num_train_epochs\"            : 3,\n",
        "    \"per_device_train_batch_size\" : 2,\n",
        "    \"num_generations\"             : 2,\n",
        "    \"max_prompt_length\"           : 300,\n",
        "    \"max_completion_length\"       : 80,\n",
        "    \"learning_rate\"               : 5e-6,\n",
        "    \"logging_steps\"               : 5,\n",
        "    \"save_strategy\"               : \"epoch\",\n",
        "    \"save_total_limit\"            : 2,\n",
        "    \"report_to\"                   : \"none\",\n",
        "    \"remove_unused_columns\"       : False,\n",
        "    \"bf16\"                        : USE_BF16,\n",
        "    \"fp16\"                        : USE_FP16,\n",
        "    \"gradient_accumulation_steps\" : 4,\n",
        "    \"max_grad_norm\"               : 0.3,\n",
        "    \"warmup_ratio\"                : 0.05,\n",
        "    \"lr_scheduler_type\"           : \"cosine\",\n",
        "    \"dataloader_num_workers\"      : 0,\n",
        "}\n",
        "\n",
        "safe_kwargs = {k: v for k, v in all_kwargs.items() if k in valid_params}\n",
        "skipped     = set(all_kwargs) - set(safe_kwargs)\n",
        "if skipped:\n",
        "    console.log(f\"[yellow]Skipped unsupported GRPOConfig args: {skipped}\")\n",
        "\n",
        "grpo_config = GRPOConfig(**safe_kwargs)\n",
        "\n",
        "trainer = GRPOTrainer(\n",
        "    model            = model,\n",
        "    args             = grpo_config,\n",
        "    reward_funcs     = civic_reward_advanced,\n",
        "    train_dataset    = train_dataset,\n",
        "    processing_class = tokenizer,\n",
        ")\n",
        "console.log(\"[green]✓ GRPOTrainer initialised with LoRA + multi-objective reward\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "944c40ec95e2465b80a72fe81442e073",
            "199c4f4f31ec433da34dee3590ad38b6",
            "1d6a657594c449bdb5ca00ebf53b81c2",
            "2e3785dcd8d94fc79e6ecd58ec844004",
            "e538d2d016904a349c4a6f02ac2f8431",
            "b1c8740b107b4efba749a80fe16dea9a"
          ]
        },
        "id": "dkC9BOmlmraC",
        "outputId": "7487114d-8145-475c-9948-90c7d1c76ea9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[32m⠴\u001b[0m \u001b[36mFetching live data for Germany\u001b[0m\n"
            ],
            "text/html": [
              "
⠴ Fetching live data for Germany\n",
              "
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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              ""
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              "
StepTraining Loss
5-0.021373
10-0.011424
15-0.006821
20-0.028652
250.008003
30-0.018843
35-0.008445
400.006863
450.051092
50-0.034912
550.009598
600.019081
65-0.028053
700.043792
750.006664
800.010932
850.006224
900.031435
95-0.016205
100-0.033808
105-0.012204
1100.015441
115-0.002538
1200.001867
1250.054835
1300.006275
135-0.004998
140-0.006802
145-0.012463
150-0.031181
155-0.011517
1600.002979
165-0.007794
1700.006225
1750.018407
1800.010794
185-0.001346
190-0.020392
195-0.004263
2000.026999
205-0.008383
2100.027005
215-0.002897
2200.016621
2250.020560
"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[2;36m[06:09:16]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33m⚡ Shock event: recession at step \u001b[0m\u001b[1;33m0\u001b[0m                                                     \u001b]8;id=695612;file:///tmp/ipykernel_2561/4216972446.py\u001b\\\u001b[2m4216972446.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=889208;file:///tmp/ipykernel_2561/4216972446.py#75\u001b\\\u001b[2m75\u001b[0m\u001b]8;;\u001b\\\n"
            ],
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[06:09:16] ⚡ Shock event: recession at step 0                                                     4216972446.py:75\n",
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              "\u001b[2;36m[06:10:18]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33m⚡ Shock event: recession at step \u001b[0m\u001b[1;33m0\u001b[0m                                                     \u001b]8;id=531756;file:///tmp/ipykernel_2561/4216972446.py\u001b\\\u001b[2m4216972446.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=324308;file:///tmp/ipykernel_2561/4216972446.py#75\u001b\\\u001b[2m75\u001b[0m\u001b]8;;\u001b\\\n"
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[06:10:18] ⚡ Shock event: recession at step 0                                                     4216972446.py:75\n",
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              "\u001b[2;36m[06:11:20]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33m⚡ Shock event: crime_spike at step \u001b[0m\u001b[1;33m0\u001b[0m                                                   \u001b]8;id=731076;file:///tmp/ipykernel_2561/4216972446.py\u001b\\\u001b[2m4216972446.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=310016;file:///tmp/ipykernel_2561/4216972446.py#75\u001b\\\u001b[2m75\u001b[0m\u001b]8;;\u001b\\\n"
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[06:11:20] ⚡ Shock event: crime_spike at step 0                                                   4216972446.py:75\n",
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          },
          "metadata": {}
        },
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          "output_type": "display_data",
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              "\u001b[2;36m[06:15:39]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33m⚡ Shock event: recession at step \u001b[0m\u001b[1;33m0\u001b[0m                                                     \u001b]8;id=697229;file:///tmp/ipykernel_2561/4216972446.py\u001b\\\u001b[2m4216972446.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=986042;file:///tmp/ipykernel_2561/4216972446.py#75\u001b\\\u001b[2m75\u001b[0m\u001b]8;;\u001b\\\n"
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[06:15:39] ⚡ Shock event: recession at step 0                                                     4216972446.py:75\n",
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "\u001b[2;36m[06:16:46]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33m⚡ Shock event: recession at step \u001b[0m\u001b[1;33m0\u001b[0m                                                     \u001b]8;id=182480;file:///tmp/ipykernel_2561/4216972446.py\u001b\\\u001b[2m4216972446.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=645414;file:///tmp/ipykernel_2561/4216972446.py#75\u001b\\\u001b[2m75\u001b[0m\u001b]8;;\u001b\\\n"
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[06:16:46] ⚡ Shock event: recession at step 0                                                     4216972446.py:75\n",
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          "metadata": {}
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          "output_type": "display_data",
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              "\u001b[2;36m[06:24:34]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33m⚡ Shock event: crime_spike at step \u001b[0m\u001b[1;33m0\u001b[0m                                                   \u001b]8;id=874224;file:///tmp/ipykernel_2561/4216972446.py\u001b\\\u001b[2m4216972446.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=425;file:///tmp/ipykernel_2561/4216972446.py#75\u001b\\\u001b[2m75\u001b[0m\u001b]8;;\u001b\\\n"
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[06:24:34] ⚡ Shock event: crime_spike at step 0                                                   4216972446.py:75\n",
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "\u001b[2;36m[06:25:38]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33m⚡ Shock event: boom at step \u001b[0m\u001b[1;33m0\u001b[0m                                                          \u001b]8;id=823927;file:///tmp/ipykernel_2561/4216972446.py\u001b\\\u001b[2m4216972446.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=608158;file:///tmp/ipykernel_2561/4216972446.py#75\u001b\\\u001b[2m75\u001b[0m\u001b]8;;\u001b\\\n"
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[06:25:38] ⚡ Shock event: boom at step 0                                                          4216972446.py:75\n",
              "
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[92m────────────────────────────────────────── \u001b[0m\u001b[1;32mTraining Complete — \u001b[0m\u001b[1;32m244.1\u001b[0m\u001b[1;32m min\u001b[0m\u001b[92m ──────────────────────────────────────────\u001b[0m\n"
            ],
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              "
────────────────────────────────────────── Training Complete — 244.1 min ──────────────────────────────────────────\n",
              "
\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "console.rule(\"[bold cyan]Starting GRPO Training\")\n",
        "start_time = time.time()\n",
        "trainer.train()\n",
        "elapsed    = time.time() - start_time\n",
        "console.rule(f\"[bold green]Training Complete — {elapsed/60:.1f} min\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "logs = trainer.state.log_history\n",
        "df_logs = pd.DataFrame(logs).dropna(subset=[\"loss\"] if \"loss\" in pd.DataFrame(logs).columns else [])\n",
        "\n",
        "reward_entries = [e for e in logs if \"reward\" in e]\n",
        "rewards_logged = [e[\"reward\"] for e in reward_entries]\n",
        "steps_logged   = [e.get(\"step\", i) for i, e in enumerate(reward_entries)]\n",
        "\n",
        "fig = make_subplots(rows=1, cols=2,\n",
        "                    subplot_titles=(\"Reward Curve\", \"Reward Distribution\"))\n",
        "\n",
        "# Reward over steps\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=steps_logged, y=rewards_logged,\n",
        "    mode=\"lines\", name=\"Reward\", line=dict(color=\"#00d4ff\", width=2)\n",
        "), row=1, col=1)\n",
        "\n",
        "# Smoothed\n",
        "if len(rewards_logged) > 5:\n",
        "    smooth = np.convolve(rewards_logged, np.ones(5)/5, mode=\"valid\")\n",
        "    fig.add_trace(go.Scatter(\n",
        "        x=steps_logged[4:], y=smooth,\n",
        "        mode=\"lines\", name=\"Smoothed\",\n",
        "        line=dict(color=\"#ff6b6b\", width=2, dash=\"dash\")\n",
        "    ), row=1, col=1)\n",
        "\n",
        "# Histogram\n",
        "fig.add_trace(go.Histogram(\n",
        "    x=rewards_logged, nbinsx=20,\n",
        "    marker_color=\"#00d4ff\", opacity=0.75, name=\"Distribution\"\n",
        "), row=1, col=2)\n",
        "\n",
        "fig.update_layout(\n",
        "    title=\"CivicAI GRPO Training Metrics\",\n",
        "    template=\"plotly_dark\", height=420,\n",
        "    paper_bgcolor=\"#0d1117\", font=dict(color=\"#e6edf3\"),\n",
        ")\n",
        "fig.show()\n",
        "fig.write_html(\"assets/training_metrics.html\")\n",
        "\n",
        "if rewards_logged:\n",
        "    console.print(f\"[cyan]Start reward : {rewards_logged[0]:.4f}\")\n",
        "    console.print(f\"[cyan]Final reward : {rewards_logged[-1]:.4f}\")\n",
        "    console.print(f\"[green]Improvement  : {rewards_logged[-1]-rewards_logged[0]:+.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "gzOFp6HNHesh",
        "outputId": "f226cfeb-00f2-4c09-f3a6-0a767b9dda64"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "\n",
              "\n",
              "    
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              "                        
                    
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              "\n",
              ""
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[36mStart reward : \u001b[0m\u001b[1;36m0.7967\u001b[0m\n"
            ],
            "text/html": [
              "
Start reward : 0.7967\n",
              "
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[36mFinal reward : \u001b[0m\u001b[1;36m0.7624\u001b[0m\n"
            ],
            "text/html": [
              "
Final reward : 0.7624\n",
              "
\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[32mImprovement  : \u001b[0m\u001b[1;32m-0.0343\u001b[0m\n"
            ],
            "text/html": [
              "
Improvement  : -0.0343\n",
              "
\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_trained_policy(\n",
        "    model, tokenizer, fetcher,\n",
        "    countries: List[str] = None,\n",
        "    tasks: List[str]     = None,\n",
        "    episodes: int        = 5,\n",
        ") -> pd.DataFrame:\n",
        "    \"\"\"Evaluate trained policy on all countries × all tasks.\"\"\"\n",
        "    countries = countries or list(RealTimeDataFetcher.COUNTRIES.keys())\n",
        "    tasks     = tasks     or list(AdvancedCivicAIEnv.TASKS.keys())\n",
        "    model.eval()\n",
        "    results   = []\n",
        "\n",
        "    for country in countries:\n",
        "        for task in tasks:\n",
        "            ep_rewards = []\n",
        "            env_eval   = AdvancedCivicAIEnv(fetcher, default_country=country)\n",
        "\n",
        "            for _ in range(episodes):\n",
        "                obs         = env_eval.reset(task, country)\n",
        "                ep_reward   = 0.0\n",
        "                for _ in range(20):\n",
        "                    prompt = build_prompt(obs)\n",
        "                    inputs = tokenizer(prompt, return_tensors=\"pt\",\n",
        "                                       truncation=True, max_length=300).to(DEVICE)\n",
        "                    with torch.no_grad():\n",
        "                        out = model.generate(\n",
        "                            **inputs, max_new_tokens=60,\n",
        "                            do_sample=False,\n",
        "                            pad_token_id=tokenizer.eos_token_id,\n",
        "                        )\n",
        "                    gen_tokens = out[0][inputs[\"input_ids\"].shape[1]:]\n",
        "                    text       = tokenizer.decode(gen_tokens, skip_special_tokens=True)\n",
        "                    action     = parse_action(text)\n",
        "                    obs, r, done, _ = env_eval.step(action)\n",
        "                    ep_reward  += r\n",
        "                    if done: break\n",
        "                ep_rewards.append(ep_reward / 20)\n",
        "\n",
        "            results.append({\n",
        "                \"country\"  : RealTimeDataFetcher.COUNTRIES[country][\"name\"],\n",
        "                \"task\"     : task,\n",
        "                \"mean_r\"   : round(float(np.mean(ep_rewards)), 4),\n",
        "                \"std_r\"    : round(float(np.std(ep_rewards)),  4),\n",
        "                \"max_r\"    : round(float(np.max(ep_rewards)),  4),\n",
        "            })\n",
        "            console.log(f\"[dim]{country} / {task} → {results[-1]['mean_r']:.4f}\")\n",
        "\n",
        "    return pd.DataFrame(results)\n",
        "\n",
        "\n",
        "def baseline_score(fetcher, episodes=5):\n",
        "    \"\"\"Fixed 0.5 policy baseline.\"\"\"\n",
        "    env_b, total = AdvancedCivicAIEnv(fetcher, \"USA\"), []\n",
        "    for _ in range(episodes):\n",
        "        obs = env_b.reset(\"maximize_wellbeing\", \"USA\")\n",
        "        r   = 0.0\n",
        "        for _ in range(20):\n",
        "            obs, rew, done, _ = env_b.step(\n",
        "                {k: 0.5 for k in [\"tax\",\"jobs\",\"healthcare\",\"education\",\"infrastructure\"]}\n",
        "            )\n",
        "            r += rew\n",
        "        total.append(r / 20)\n",
        "    return float(np.mean(total))\n",
        "\n",
        "\n",
        "console.rule(\"[bold cyan]Evaluating Policy Across Countries & Tasks\")\n",
        "df_eval  = evaluate_trained_policy(model, tokenizer, fetcher, episodes=3)\n",
        "baseline = baseline_score(fetcher)\n",
        "\n",
        "console.print(df_eval.to_string(index=False))\n",
        "console.print(f\"\\n[bold]Baseline (fixed 0.5) : {baseline:.4f}\")\n",
        "console.print(f\"[bold green]Best trained score   : {df_eval['mean_r'].max():.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 670
        },
        "id": "k59vpTRYHiqS",
        "outputId": "07631bd5-5931-4d48-dcd0-3442b3e50782"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[32m⠦\u001b[0m \u001b[36mFetching live data for United States\u001b[0m\n"
            ],
            "text/html": [
              "
⠦ Fetching live data for United States\n",
              "
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [],
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              "
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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              "\u001b[2;36m[08:52:35]\u001b[0m\u001b[2;36m \u001b[0m\u001b[33m⚡ Shock event: recession at step \u001b[0m\u001b[1;33m0\u001b[0m                                                     \u001b]8;id=628492;file:///tmp/ipykernel_2561/4216972446.py\u001b\\\u001b[2m4216972446.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=532929;file:///tmp/ipykernel_2561/4216972446.py#75\u001b\\\u001b[2m75\u001b[0m\u001b]8;;\u001b\\\n"
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[08:52:35] ⚡ Shock event: recession at step 0                                                     4216972446.py:75\n",
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          },
          "metadata": {}
        },
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          "output_type": "display_data",
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              "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[33m⚡ Shock event: recession at step \u001b[0m\u001b[1;33m13\u001b[0m                                                    \u001b]8;id=73201;file:///tmp/ipykernel_2561/4216972446.py\u001b\\\u001b[2m4216972446.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=289666;file:///tmp/ipykernel_2561/4216972446.py#75\u001b\\\u001b[2m75\u001b[0m\u001b]8;;\u001b\\\n"
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              "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[33m⚡ Shock event: recession at step \u001b[0m\u001b[1;33m14\u001b[0m                                                    \u001b]8;id=356385;file:///tmp/ipykernel_2561/4216972446.py\u001b\\\u001b[2m4216972446.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=979630;file:///tmp/ipykernel_2561/4216972446.py#75\u001b\\\u001b[2m75\u001b[0m\u001b]8;;\u001b\\\n"
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          "metadata": {}
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              "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[33m⚡ Shock event: crime_spike at step \u001b[0m\u001b[1;33m15\u001b[0m                                                  \u001b]8;id=608093;file:///tmp/ipykernel_2561/4216972446.py\u001b\\\u001b[2m4216972446.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=691998;file:///tmp/ipykernel_2561/4216972446.py#75\u001b\\\u001b[2m75\u001b[0m\u001b]8;;\u001b\\\n"
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          "metadata": {}
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              "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[33m⚡ Shock event: recession at step \u001b[0m\u001b[1;33m3\u001b[0m                                                     \u001b]8;id=564742;file:///tmp/ipykernel_2561/4216972446.py\u001b\\\u001b[2m4216972446.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=507868;file:///tmp/ipykernel_2561/4216972446.py#75\u001b\\\u001b[2m75\u001b[0m\u001b]8;;\u001b\\\n"
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          "metadata": {}
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              "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[33m⚡ Shock event: boom at step \u001b[0m\u001b[1;33m6\u001b[0m                                                          \u001b]8;id=569827;file:///tmp/ipykernel_2561/4216972446.py\u001b\\\u001b[2m4216972446.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=395533;file:///tmp/ipykernel_2561/4216972446.py#75\u001b\\\u001b[2m75\u001b[0m\u001b]8;;\u001b\\\n"
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              "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[33m⚡ Shock event: boom at step \u001b[0m\u001b[1;33m12\u001b[0m                                                         \u001b]8;id=197816;file:///tmp/ipykernel_2561/4216972446.py\u001b\\\u001b[2m4216972446.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=731380;file:///tmp/ipykernel_2561/4216972446.py#75\u001b\\\u001b[2m75\u001b[0m\u001b]8;;\u001b\\\n"
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          "metadata": {}
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              "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[33m⚡ Shock event: boom at step \u001b[0m\u001b[1;33m10\u001b[0m                                                         \u001b]8;id=897594;file:///tmp/ipykernel_2561/4216972446.py\u001b\\\u001b[2m4216972446.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=813217;file:///tmp/ipykernel_2561/4216972446.py#75\u001b\\\u001b[2m75\u001b[0m\u001b]8;;\u001b\\\n"
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          "metadata": {}
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          "output_type": "display_data",
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              "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[33m⚡ Shock event: recession at step \u001b[0m\u001b[1;33m11\u001b[0m                                                    \u001b]8;id=495951;file:///tmp/ipykernel_2561/4216972446.py\u001b\\\u001b[2m4216972446.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=739485;file:///tmp/ipykernel_2561/4216972446.py#75\u001b\\\u001b[2m75\u001b[0m\u001b]8;;\u001b\\\n"
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          "metadata": {}
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          "output_type": "display_data",
          "data": {
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              "\u001b[2;36m          \u001b[0m\u001b[2;36m \u001b[0m\u001b[33m⚡ Shock event: crime_spike at step \u001b[0m\u001b[1;33m17\u001b[0m                                                  \u001b]8;id=695928;file:///tmp/ipykernel_2561/4216972446.py\u001b\\\u001b[2m4216972446.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=830884;file:///tmp/ipykernel_2561/4216972446.py#75\u001b\\\u001b[2m75\u001b[0m\u001b]8;;\u001b\\\n"
            ],
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              "
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              "
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       country               task  mean_r  std_r  max_r\n",
              " United States  stabilize_economy  \u001b[1;36m0.7832\u001b[0m \u001b[1;36m0.0244\u001b[0m \u001b[1;36m0.8176\u001b[0m\n",
              " United States     improve_health  \u001b[1;36m0.9105\u001b[0m \u001b[1;36m0.0083\u001b[0m \u001b[1;36m0.9222\u001b[0m\n",
              " United States       reduce_crime  \u001b[1;36m0.8945\u001b[0m \u001b[1;36m0.0173\u001b[0m \u001b[1;36m0.9188\u001b[0m\n",
              " United States maximize_wellbeing  \u001b[1;36m0.8473\u001b[0m \u001b[1;36m0.0092\u001b[0m \u001b[1;36m0.8593\u001b[0m\n",
              "         India  stabilize_economy  \u001b[1;36m0.7258\u001b[0m \u001b[1;36m0.0205\u001b[0m \u001b[1;36m0.7502\u001b[0m\n",
              "         India     improve_health  \u001b[1;36m0.8542\u001b[0m \u001b[1;36m0.0047\u001b[0m \u001b[1;36m0.8590\u001b[0m\n",
              "         India       reduce_crime  \u001b[1;36m0.8858\u001b[0m \u001b[1;36m0.0074\u001b[0m \u001b[1;36m0.8959\u001b[0m\n",
              "         India maximize_wellbeing  \u001b[1;36m0.8157\u001b[0m \u001b[1;36m0.0355\u001b[0m \u001b[1;36m0.8448\u001b[0m\n",
              "United Kingdom  stabilize_economy  \u001b[1;36m0.7420\u001b[0m \u001b[1;36m0.0373\u001b[0m \u001b[1;36m0.7943\u001b[0m\n",
              "United Kingdom     improve_health  \u001b[1;36m0.8509\u001b[0m \u001b[1;36m0.0226\u001b[0m \u001b[1;36m0.8779\u001b[0m\n",
              "United Kingdom       reduce_crime  \u001b[1;36m0.8939\u001b[0m \u001b[1;36m0.0077\u001b[0m \u001b[1;36m0.9005\u001b[0m\n",
              "United Kingdom maximize_wellbeing  \u001b[1;36m0.8112\u001b[0m \u001b[1;36m0.0118\u001b[0m \u001b[1;36m0.8218\u001b[0m\n",
              "       Germany  stabilize_economy  \u001b[1;36m0.7562\u001b[0m \u001b[1;36m0.0425\u001b[0m \u001b[1;36m0.8156\u001b[0m\n",
              "       Germany     improve_health  \u001b[1;36m0.8647\u001b[0m \u001b[1;36m0.0126\u001b[0m \u001b[1;36m0.8791\u001b[0m\n",
              "       Germany       reduce_crime  \u001b[1;36m0.8903\u001b[0m \u001b[1;36m0.0077\u001b[0m \u001b[1;36m0.8970\u001b[0m\n",
              "       Germany maximize_wellbeing  \u001b[1;36m0.8107\u001b[0m \u001b[1;36m0.0045\u001b[0m \u001b[1;36m0.8160\u001b[0m\n",
              "         Japan  stabilize_economy  \u001b[1;36m0.7614\u001b[0m \u001b[1;36m0.0122\u001b[0m \u001b[1;36m0.7758\u001b[0m\n",
              "         Japan     improve_health  \u001b[1;36m0.8622\u001b[0m \u001b[1;36m0.0035\u001b[0m \u001b[1;36m0.8671\u001b[0m\n",
              "         Japan       reduce_crime  \u001b[1;36m0.8943\u001b[0m \u001b[1;36m0.0183\u001b[0m \u001b[1;36m0.9123\u001b[0m\n",
              "         Japan maximize_wellbeing  \u001b[1;36m0.8385\u001b[0m \u001b[1;36m0.0146\u001b[0m \u001b[1;36m0.8538\u001b[0m\n",
              "        Brazil  stabilize_economy  \u001b[1;36m0.7127\u001b[0m \u001b[1;36m0.0107\u001b[0m \u001b[1;36m0.7277\u001b[0m\n",
              "        Brazil     improve_health  \u001b[1;36m0.8521\u001b[0m \u001b[1;36m0.0276\u001b[0m \u001b[1;36m0.8740\u001b[0m\n",
              "        Brazil       reduce_crime  \u001b[1;36m0.8514\u001b[0m \u001b[1;36m0.0059\u001b[0m \u001b[1;36m0.8597\u001b[0m\n",
              "        Brazil maximize_wellbeing  \u001b[1;36m0.7974\u001b[0m \u001b[1;36m0.0056\u001b[0m \u001b[1;36m0.8017\u001b[0m\n"
            ],
            "text/html": [
              "
       country               task  mean_r  std_r  max_r\n",
              " United States  stabilize_economy  0.7832 0.0244 0.8176\n",
              " United States     improve_health  0.9105 0.0083 0.9222\n",
              " United States       reduce_crime  0.8945 0.0173 0.9188\n",
              " United States maximize_wellbeing  0.8473 0.0092 0.8593\n",
              "         India  stabilize_economy  0.7258 0.0205 0.7502\n",
              "         India     improve_health  0.8542 0.0047 0.8590\n",
              "         India       reduce_crime  0.8858 0.0074 0.8959\n",
              "         India maximize_wellbeing  0.8157 0.0355 0.8448\n",
              "United Kingdom  stabilize_economy  0.7420 0.0373 0.7943\n",
              "United Kingdom     improve_health  0.8509 0.0226 0.8779\n",
              "United Kingdom       reduce_crime  0.8939 0.0077 0.9005\n",
              "United Kingdom maximize_wellbeing  0.8112 0.0118 0.8218\n",
              "       Germany  stabilize_economy  0.7562 0.0425 0.8156\n",
              "       Germany     improve_health  0.8647 0.0126 0.8791\n",
              "       Germany       reduce_crime  0.8903 0.0077 0.8970\n",
              "       Germany maximize_wellbeing  0.8107 0.0045 0.8160\n",
              "         Japan  stabilize_economy  0.7614 0.0122 0.7758\n",
              "         Japan     improve_health  0.8622 0.0035 0.8671\n",
              "         Japan       reduce_crime  0.8943 0.0183 0.9123\n",
              "         Japan maximize_wellbeing  0.8385 0.0146 0.8538\n",
              "        Brazil  stabilize_economy  0.7127 0.0107 0.7277\n",
              "        Brazil     improve_health  0.8521 0.0276 0.8740\n",
              "        Brazil       reduce_crime  0.8514 0.0059 0.8597\n",
              "        Brazil maximize_wellbeing  0.7974 0.0056 0.8017\n",
              "
\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "\u001b[1mBaseline \u001b[0m\u001b[1m(\u001b[0m\u001b[1mfixed \u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1m)\u001b[0m\u001b[1m : \u001b[0m\u001b[1;36m0.8596\u001b[0m\n"
            ],
            "text/html": [
              "
\n",
              "Baseline (fixed 0.5) : 0.8596\n",
              "
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1;32mBest trained score   : \u001b[0m\u001b[1;32m0.9105\u001b[0m\n"
            ],
            "text/html": [
              "
Best trained score   : 0.9105\n",
              "
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            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pivot = df_eval.pivot(index=\"country\", columns=\"task\", values=\"mean_r\")\n",
        "\n",
        "fig_heat = go.Figure(go.Heatmap(\n",
        "    z          = pivot.values,\n",
        "    x          = pivot.columns.tolist(),\n",
        "    y          = pivot.index.tolist(),\n",
        "    colorscale = \"RdYlGn\",\n",
        "    text       = np.round(pivot.values, 3),\n",
        "    texttemplate=\"%{text}\",\n",
        "    showscale  = True,\n",
        "    zmin=0.4, zmax=1.0,\n",
        "))\n",
        "fig_heat.add_shape(\n",
        "    type=\"line\", x0=-0.5, x1=len(pivot.columns)-0.5,\n",
        "    y0=-0.5, y1=len(pivot.index)-0.5,\n",
        "    line=dict(color=\"white\", width=0)\n",
        ")\n",
        "fig_heat.update_layout(\n",
        "    title  = \"Policy Performance Heatmap — Country × Task (GRPO Trained)\",\n",
        "    template=\"plotly_dark\", height=400,\n",
        "    paper_bgcolor=\"#0d1117\", font=dict(color=\"#e6edf3\"),\n",
        "    xaxis_title=\"Task\", yaxis_title=\"Country\",\n",
        ")\n",
        "fig_heat.show()\n",
        "fig_heat.write_html(\"assets/eval_heatmap.html\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "D4IQZzjRHm2J",
        "outputId": "62fc9a66-986d-49fc-c758-299015385b51"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "\n",
              "\n",
              "    
                            \n",
              "                        
                    
\n",
              "\n",
              ""
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained(\"checkpoints/civicai-lora\")\n",
        "tokenizer.save_pretrained(\"checkpoints/civicai-lora\")\n",
        "\n",
        "# Save results JSON\n",
        "results_json = {\n",
        "    \"run_timestamp\"  : datetime.now().isoformat(),\n",
        "    \"model\"          : MODEL_NAME,\n",
        "    \"lora_rank\"      : 8,\n",
        "    \"training_epochs\": 3,\n",
        "    \"num_countries\"  : len(RealTimeDataFetcher.COUNTRIES),\n",
        "    \"num_tasks\"      : len(AdvancedCivicAIEnv.TASKS),\n",
        "    \"data_source\"    : \"World Bank Open API (live)\",\n",
        "    \"baseline_reward\": round(baseline, 4),\n",
        "    \"best_reward\"    : round(float(df_eval[\"mean_r\"].max()), 4),\n",
        "    \"improvement\"    : round(float(df_eval[\"mean_r\"].max()) - baseline, 4),\n",
        "    \"reward_history\" : rewards_logged,\n",
        "    \"eval_by_country_task\": df_eval.to_dict(orient=\"records\"),\n",
        "    \"real_data_snapshot\"  : df_world[[\"country\",\"inflation_pct\",\"unemployment_pct\",\n",
        "                                       \"health_exp_gdp\",\"gdp_growth\"]].to_dict(orient=\"records\"),\n",
        "}\n",
        "\n",
        "with open(\"assets/training_results.json\", \"w\") as f:\n",
        "    json.dump(results_json, f, indent=2)\n",
        "\n",
        "console.rule(\"[bold green]All Done\")\n",
        "console.print(f\"[green]✓ LoRA checkpoint  → checkpoints/civicai-lora/\")\n",
        "console.print(f\"[green]✓ Results JSON     → assets/training_results.json\")\n",
        "console.print(f\"[green]✓ Dashboard HTML   → assets/global_dashboard.html\")\n",
        "console.print(f\"[green]✓ Training metrics → assets/training_metrics.html\")\n",
        "console.print(f\"[green]✓ Eval heatmap     → assets/eval_heatmap.html\")\n",
        "console.print(f\"\\n[bold cyan]Baseline  : {baseline:.4f}\")\n",
        "console.print(f\"[bold green]Best score: {df_eval['mean_r'].max():.4f}\")\n",
        "console.print(f\"[bold green]Delta     : {df_eval['mean_r'].max() - baseline:+.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "u5mBd7UdU3tE",
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──────────────────────────────────────────────────── All Done ─────────────────────────────────────────────────────\n",
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✓ LoRA checkpoint  → checkpoints/civicai-lora/\n",
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✓ Results JSON     → assets/training_results.json\n",
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\n"
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✓ Dashboard HTML   → assets/global_dashboard.html\n",
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\n"
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✓ Training metrics → assets/training_metrics.html\n",
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\n"
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✓ Eval heatmap     → assets/eval_heatmap.html\n",
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\n"
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\n",
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\n"
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Best score: 0.9105\n",
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\n"
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Delta     : +0.0509\n",
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⠸ Fetching live data for Germany\n
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