{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from flask import Flask, request, jsonify\n",
    "import pickle\n",
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "app = Flask(__name__)\n",
    "\n",
    "# Load model dan TF-IDF\n",
    "model_path = \"model_svm.pkl\"\n",
    "tfidf_path = \"tfidf_vectorizer.pkl\"\n",
    "\n",
    "def load_model():\n",
    "    with open(model_path, 'rb') as f:\n",
    "        model = pickle.load(f)\n",
    "    with open(tfidf_path, 'rb') as f:\n",
    "        vectorizer = pickle.load(f)\n",
    "    return model, vectorizer\n",
    "\n",
    "model, vectorizer = load_model()\n",
    "\n",
    "@app.route('/predict', methods=['POST'])\n",
    "def predict():\n",
    "    data = request.json\n",
    "    full_text = data.get('full_text', '')\n",
    "    if not full_text:\n",
    "        return jsonify({\"error\": \"full_text is required\"}), 400\n",
    "    \n",
    "    text_tfidf = vectorizer.transform([full_text])\n",
    "    prediction = model.predict(text_tfidf)[0]\n",
    "    return jsonify({\"prediction\": prediction})\n",
    "\n",
    "# @app.route('/train', methods=['POST'])\n",
    "# def train():\n",
    "#     data = request.json\n",
    "#     df = pd.DataFrame(data)\n",
    "#     if 'full_text' not in df.columns or 'polarity' not in df.columns:\n",
    "#         return jsonify({\"error\": \"Data must contain 'full_text' and 'polarity'\"}), 400\n",
    "    \n",
    "#     X = df['full_text']\n",
    "#     y = df['polarity']\n",
    "    \n",
    "#     global vectorizer, model\n",
    "#     vectorizer = TfidfVectorizer()\n",
    "#     X_tfidf = vectorizer.fit_transform(X)\n",
    "    \n",
    "#     model = SVC()\n",
    "#     model.fit(X_tfidf, y)\n",
    "    \n",
    "#     # Save model & vectorizer\n",
    "#     with open(model_path, 'wb') as f:\n",
    "#         pickle.dump(model, f)\n",
    "#     with open(tfidf_path, 'wb') as f:\n",
    "#         pickle.dump(vectorizer, f)\n",
    "    \n",
    "#     return jsonify({\"message\": \"Model trained successfully\"})\n",
    "\n",
    "# @app.route('/accuracy', methods=['POST'])\n",
    "# def accuracy():\n",
    "#     data = request.json\n",
    "#     df = pd.DataFrame(data)\n",
    "#     if 'full_text' not in df.columns or 'polarity' not in df.columns:\n",
    "#         return jsonify({\"error\": \"Data must contain 'full_text' and 'polarity'\"}), 400\n",
    "    \n",
    "#     X = df['full_text']\n",
    "#     y_true = df['polarity']\n",
    "#     X_tfidf = vectorizer.transform(X)\n",
    "#     y_pred = model.predict(X_tfidf)\n",
    "#     acc = accuracy_score(y_true, y_pred)\n",
    "    \n",
    "#     return jsonify({\"accuracy\": acc})\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run(debug=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediksi Sentimen: 0\n"
     ]
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "# Load model dan vectorizer\n",
    "model = joblib.load('model_svm.pkl')\n",
    "vectorizer = joblib.load('tfidf_vectorizer.pkl')\n",
    "\n",
    "# Contoh prediksi teks baru\n",
    "new_text = [\"masyarakat tidak suka ikn\"]\n",
    "new_text_tfidf = vectorizer.transform(new_text)\n",
    "\n",
    "# Prediksi sentimen\n",
    "prediction = model.predict(new_text_tfidf)\n",
    "print(f\"Prediksi Sentimen: {prediction[0]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * Serving Flask app '__main__'\n",
      " * Debug mode: on\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\n",
      " * Running on all addresses (0.0.0.0)\n",
      " * Running on http://127.0.0.1:9000\n",
      " * Running on http://192.168.125.140:9000\n",
      "Press CTRL+C to quit\n",
      " * Restarting with stat\n"
     ]
    },
    {
     "ename": "SystemExit",
     "evalue": "1",
     "output_type": "error",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[1;31mSystemExit\u001b[0m\u001b[1;31m:\u001b[0m 1\n"
     ]
    }
   ],
   "source": [
    "from flask import Flask, request, jsonify\n",
    "from flask_cors import CORS\n",
    "import joblib\n",
    "\n",
    "app = Flask(__name__)\n",
    "CORS(app)\n",
    "\n",
    "# Load model dan vectorizer\n",
    "model = joblib.load('model_svm.pkl')\n",
    "vectorizer = joblib.load('tfidf_vectorizer.pkl')\n",
    "\n",
    "@app.route('/predict', methods=['POST'])\n",
    "def predict():\n",
    "    try:\n",
    "        data = request.json\n",
    "        text = data.get(\"text\", \"\")\n",
    "        \n",
    "        if not text:\n",
    "            return jsonify({\"error\": \"No text provided\"}), 400\n",
    "        \n",
    "        # Transformasi teks\n",
    "        text_tfidf = vectorizer.transform([text])\n",
    "        prediction = model.predict(text_tfidf)\n",
    "        \n",
    "        return jsonify({\"sentiment\": prediction[0]})\n",
    "    except Exception as e:\n",
    "        return jsonify({\"error\": str(e)}), 500\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run(host='0.0.0.0', port=9000, debug=True)\n"
   ]
  }
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