{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# todo: export the model from train.ipynb\n",
    "\n",
    "# todo: copy import from train.ipynb\n",
    "# todo: copy normalize() from train.ipynb\n",
    "# todo: copy predict() from train.ipynb\n",
    "\n",
    "# todo: inject saved model into predict()\n",
    "# todo: turn predict() into a GreatAI service\n",
    "\n",
    "# todo: log prediction into output trace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from great_ai.utilities import clean\n",
    "from great_ai import use_model, GreatAI, log_metric\n",
    "import re\n",
    "\n",
    "def normalize(text: str) -> str:\n",
    "    cleaned = clean(text, convert_to_ascii=True).lower()\n",
    "    return re.sub(r\"[^a-z]+\", \" \", cleaned)\n",
    "\n",
    "@GreatAI.create\n",
    "@use_model('financial-sentiment')\n",
    "def predict_financial_sentiment(text: str, model):\n",
    "    \"\"\"Classify news articles into Negative, Neutral, or Positive classes using a linear SVM.\"\"\"\n",
    "    text = normalize(text)\n",
    "    features = model.named_steps[\"tfidfvectorizer\"].transform([text])\n",
    "    prediction = model.named_steps[\"sgdclassifier\"].predict(features)[0]\n",
    "\n",
    "    explanation = [\n",
    "        (feature_name, weight)\n",
    "        for weight, feature_name in sorted(\n",
    "            (\n",
    "                (feature_weight * feature, feature_name)\n",
    "                for feature_name, feature_weight, feature in zip(\n",
    "                    model.named_steps[\"tfidfvectorizer\"].get_feature_names_out(),\n",
    "                    model.named_steps[\"sgdclassifier\"].coef_[list(model.named_steps[\"sgdclassifier\"].classes_).index(prediction)],\n",
    "                    features.toarray()[0],\n",
    "                )\n",
    "                if feature * feature_weight != 0\n",
    "            ),\n",
    "            reverse=True,\n",
    "        )\n",
    "    ][:10]\n",
    "\n",
    "    log_metric('prediction', prediction, disable_logging=True)\n",
    "\n",
    "    return prediction, explanation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# todo: add integration test (copy metric derivation from train.ipynb)\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    from great_ai import query_ground_truth\n",
    "    import matplotlib.pyplot as plt\n",
    "    from sklearn import metrics\n",
    "\n",
    "    test_split = query_ground_truth('test', return_max_count=200)\n",
    "\n",
    "    traces = predict.process_batch([t.input for t in test_split])\n",
    "    y_predicted = [t.output[0] for t in traces]\n",
    "    y_test = [t.output for t in test_split]\n",
    "\n",
    "    %matplotlib inline\n",
    "    plt.rcParams[\"figure.figsize\"] = (10, 10)\n",
    "    plt.rcParams[\"font.size\"] = 16\n",
    "\n",
    "    print(metrics.classification_report(y_test, y_predicted))\n",
    "    metrics.ConfusionMatrixDisplay.from_predictions(\n",
    "        y_true=y_test,\n",
    "        y_pred=y_predicted,\n",
    "        xticks_rotation=\"vertical\",\n",
    "        normalize=\"pred\",\n",
    "        values_format=\".2f\",\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# todo: serve prediction model\n",
    "# todo: open dashboard\n",
    "\n",
    "!great-ai deploy.ipynb"
   ]
  }
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