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     "end_time": "2024-10-18T02:33:03.992606Z",
     "start_time": "2024-10-18T02:33:03.198886Z"
    }
   },
   "source": [
    "from transformers import AutoTokenizer, AutoModelForTokenClassification\n",
    "import torch\n",
    "\n",
    "# Load the saved model and tokenizer\n",
    "model_path = \"./results/best_model\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_path)\n",
    "model = AutoModelForTokenClassification.from_pretrained(model_path)\n",
    "\n",
    "service_mapping = {\n",
    "        \"hotel\": [\"hotel\", \"hotels\", \"khách sạn\", \"khach san\", \"ks\"],\n",
    "        \"flight\": [\"flight\", \"flights\", \"vé máy bay\", \"máy bay\",\"may bay\"],\n",
    "        \"car rental\": [\"car rental\", \"car rentals\", \"thuê xe\", \"xe\"],\n",
    "        \"ticket\": [\"ticket\", \"tickets\", \"vé\", \"vé tham quan\",\"ve\", \"ve tham quan\"],\n",
    "        \"tour\": [\"tour\", \"tours\", \"du lịch\",\"du lich\"]\n",
    "    }\n",
    "\n",
    "# Define id2label mapping\n",
    "id2label = {0: \"O\", 1: \"B-SERVICE\", 2: \"I-SERVICE\", 3: \"B-LOCATION\", 4: \"I-LOCATION\"}\n",
    "\n",
    "def map_service(service):\n",
    "    service = service.lower()\n",
    "    for key, values in service_mapping.items():\n",
    "        if any(v in service for v in values):\n",
    "            return key\n",
    "    return None\n",
    "\n",
    "def predict(text):\n",
    "    # Tokenize the input text\n",
    "    inputs = tokenizer(text, return_tensors=\"pt\", truncation=True, padding=True)\n",
    "    with torch.no_grad():\n",
    "        outputs = model(**inputs)\n",
    "    predictions = torch.argmax(outputs.logits, dim=2)\n",
    "    predicted_labels = [id2label[p.item()] for p in predictions[0]]\n",
    "    word_ids = inputs.word_ids()\n",
    "    aligned_labels = []\n",
    "    current_word = None\n",
    "    for word_id, label in zip(word_ids, predicted_labels):\n",
    "        if word_id != current_word:\n",
    "            aligned_labels.append(label)\n",
    "            current_word = word_id\n",
    "    \n",
    "    # Extract entities\n",
    "    entities = {\"SERVICE\": [], \"LOCATION\": []}\n",
    "    current_entity = None\n",
    "    current_tokens = []\n",
    "    \n",
    "    words = text.split()\n",
    "    for word, label in zip(words, aligned_labels):\n",
    "        if label.startswith(\"B-\"):\n",
    "            if current_entity:\n",
    "                if current_entity == \"SERVICE\":\n",
    "                    mapped_service = map_service(\" \".join(current_tokens))\n",
    "                    if mapped_service:\n",
    "                        entities[current_entity].append(mapped_service)\n",
    "                else:\n",
    "                    entities[current_entity].append(\" \".join(current_tokens))\n",
    "            current_entity = label[2:]\n",
    "            current_tokens = [word]\n",
    "        elif label.startswith(\"I-\") and current_entity:\n",
    "            current_tokens.append(word)\n",
    "        else:\n",
    "            if current_entity:\n",
    "                if current_entity == \"SERVICE\":\n",
    "                    mapped_service = map_service(\" \".join(current_tokens))\n",
    "                    if mapped_service:\n",
    "                        entities[current_entity].append(mapped_service)\n",
    "                else:\n",
    "                    entities[current_entity].append(\" \".join(current_tokens))\n",
    "                current_entity = None\n",
    "                current_tokens = []\n",
    "    \n",
    "    if current_entity:\n",
    "        if current_entity == \"SERVICE\":\n",
    "            mapped_service = map_service(\" \".join(current_tokens))\n",
    "            if mapped_service:\n",
    "                entities[current_entity].append(mapped_service)\n",
    "        else:\n",
    "            entities[current_entity].append(\" \".join(current_tokens))\n",
    "            \n",
    "    if entities[\"SERVICE\"]:\n",
    "        entities[\"SERVICE\"] = [entities[\"SERVICE\"][0]]\n",
    "    \n",
    "    return entities\n",
    "\n",
    "# Test function\n",
    "def test_ner(text):\n",
    "    print(f\"Input: {text}\")\n",
    "    result = predict(text)\n",
    "    print(\"Output:\", result)\n",
    "    return result"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-10-18T03:19:29.075358Z",
     "start_time": "2024-10-18T03:19:28.999181Z"
    }
   },
   "cell_type": "code",
   "source": [
    "test_texts = [\n",
    "    \"tour du lich gia re da lat\"\n",
    "]\n",
    "\n",
    "for text in test_texts:\n",
    "    test_ner(text)\n",
    "    print()"
   ],
   "id": "65a6a938b3590ad2",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input: tour du lich gia re da lat\n",
      "Output: {'SERVICE': ['tour'], 'LOCATION': ['da lat']}\n",
      "\n"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": "",
   "id": "826408096ee93b4"
  }
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