diff --git "a/new_symp2disease.ipynb" "b/new_symp2disease.ipynb"
new file mode 100644--- /dev/null
+++ "b/new_symp2disease.ipynb"
@@ -0,0 +1,2903 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "authorship_tag": "ABX9TyMHvKnYH1nV6+05sjz3ESPf"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "source": [
+        "pip install gradio"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Z7n__8gLD0Fi",
+        "outputId": "13bd2be3-8345-44d7-d942-f85a54e4cc4e"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting gradio\n",
+            "  Downloading gradio-3.40.1-py3-none-any.whl (20.0 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.0/20.0 MB\u001b[0m \u001b[31m69.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting aiofiles<24.0,>=22.0 (from gradio)\n",
+            "  Downloading aiofiles-23.2.1-py3-none-any.whl (15 kB)\n",
+            "Requirement already satisfied: aiohttp~=3.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (3.8.5)\n",
+            "Requirement already satisfied: altair<6.0,>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (4.2.2)\n",
+            "Collecting fastapi (from gradio)\n",
+            "  Downloading fastapi-0.101.0-py3-none-any.whl (65 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.7/65.7 kB\u001b[0m \u001b[31m8.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting ffmpy (from gradio)\n",
+            "  Downloading ffmpy-0.3.1.tar.gz (5.5 kB)\n",
+            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "Collecting gradio-client>=0.4.0 (from gradio)\n",
+            "  Downloading gradio_client-0.4.0-py3-none-any.whl (297 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m297.4/297.4 kB\u001b[0m \u001b[31m30.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting httpx (from gradio)\n",
+            "  Downloading httpx-0.24.1-py3-none-any.whl (75 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.4/75.4 kB\u001b[0m \u001b[31m8.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting huggingface-hub>=0.14.0 (from gradio)\n",
+            "  Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m28.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: importlib-resources<7.0,>=1.3 in /usr/local/lib/python3.10/dist-packages (from gradio) (6.0.1)\n",
+            "Requirement already satisfied: jinja2<4.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (3.1.2)\n",
+            "Requirement already satisfied: markdown-it-py[linkify]>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (3.0.0)\n",
+            "Requirement already satisfied: markupsafe~=2.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (2.1.3)\n",
+            "Requirement already satisfied: matplotlib~=3.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (3.7.1)\n",
+            "Collecting mdit-py-plugins<=0.3.3 (from gradio)\n",
+            "  Downloading mdit_py_plugins-0.3.3-py3-none-any.whl (50 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.5/50.5 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: numpy~=1.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (1.23.5)\n",
+            "Collecting orjson~=3.0 (from gradio)\n",
+            "  Downloading orjson-3.9.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (140 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.3/140.3 kB\u001b[0m \u001b[31m15.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from gradio) (23.1)\n",
+            "Requirement already satisfied: pandas<3.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (1.5.3)\n",
+            "Requirement already satisfied: pillow<11.0,>=8.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (9.4.0)\n",
+            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from gradio) (2.1.1)\n",
+            "Collecting pydub (from gradio)\n",
+            "  Downloading pydub-0.25.1-py2.py3-none-any.whl (32 kB)\n",
+            "Collecting python-multipart (from gradio)\n",
+            "  Downloading python_multipart-0.0.6-py3-none-any.whl (45 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.7/45.7 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: pyyaml<7.0,>=5.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (6.0.1)\n",
+            "Requirement already satisfied: requests~=2.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (2.31.0)\n",
+            "Collecting semantic-version~=2.0 (from gradio)\n",
+            "  Downloading semantic_version-2.10.0-py2.py3-none-any.whl (15 kB)\n",
+            "Requirement already satisfied: typing-extensions~=4.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (4.7.1)\n",
+            "Collecting uvicorn>=0.14.0 (from gradio)\n",
+            "  Downloading uvicorn-0.23.2-py3-none-any.whl (59 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.5/59.5 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting websockets<12.0,>=10.0 (from gradio)\n",
+            "  Downloading websockets-11.0.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (129 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.9/129.9 kB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp~=3.0->gradio) (23.1.0)\n",
+            "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp~=3.0->gradio) (3.2.0)\n",
+            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp~=3.0->gradio) (6.0.4)\n",
+            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp~=3.0->gradio) (4.0.2)\n",
+            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp~=3.0->gradio) (1.9.2)\n",
+            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp~=3.0->gradio) (1.4.0)\n",
+            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp~=3.0->gradio) (1.3.1)\n",
+            "Requirement already satisfied: entrypoints in /usr/local/lib/python3.10/dist-packages (from altair<6.0,>=4.2.0->gradio) (0.4)\n",
+            "Requirement already satisfied: jsonschema>=3.0 in /usr/local/lib/python3.10/dist-packages (from altair<6.0,>=4.2.0->gradio) (4.19.0)\n",
+            "Requirement already satisfied: toolz in /usr/local/lib/python3.10/dist-packages (from altair<6.0,>=4.2.0->gradio) (0.12.0)\n",
+            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from gradio-client>=0.4.0->gradio) (2023.6.0)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.14.0->gradio) (3.12.2)\n",
+            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.14.0->gradio) (4.66.0)\n",
+            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py[linkify]>=2.0.0->gradio) (0.1.2)\n",
+            "Requirement already satisfied: linkify-it-py<3,>=1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py[linkify]>=2.0.0->gradio) (2.0.2)\n",
+            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio) (1.1.0)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio) (0.11.0)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio) (4.42.0)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio) (1.4.4)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio) (3.1.1)\n",
+            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio) (2.8.2)\n",
+            "INFO: pip is looking at multiple versions of mdit-py-plugins to determine which version is compatible with other requirements. This could take a while.\n",
+            "Collecting mdit-py-plugins<=0.3.3 (from gradio)\n",
+            "  Downloading mdit_py_plugins-0.3.2-py3-none-any.whl (50 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.4/50.4 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Downloading mdit_py_plugins-0.3.1-py3-none-any.whl (46 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.5/46.5 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Downloading mdit_py_plugins-0.3.0-py3-none-any.whl (43 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.7/43.7 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Downloading mdit_py_plugins-0.2.8-py3-none-any.whl (41 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.0/41.0 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Downloading mdit_py_plugins-0.2.7-py3-none-any.whl (41 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.0/41.0 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Downloading mdit_py_plugins-0.2.6-py3-none-any.whl (39 kB)\n",
+            "  Downloading mdit_py_plugins-0.2.5-py3-none-any.whl (39 kB)\n",
+            "INFO: pip is looking at multiple versions of mdit-py-plugins to determine which version is compatible with other requirements. This could take a while.\n",
+            "  Downloading mdit_py_plugins-0.2.4-py3-none-any.whl (39 kB)\n",
+            "  Downloading mdit_py_plugins-0.2.3-py3-none-any.whl (39 kB)\n",
+            "  Downloading mdit_py_plugins-0.2.2-py3-none-any.whl (39 kB)\n",
+            "  Downloading mdit_py_plugins-0.2.1-py3-none-any.whl (38 kB)\n",
+            "  Downloading mdit_py_plugins-0.2.0-py3-none-any.whl (38 kB)\n",
+            "INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See https://pip.pypa.io/warnings/backtracking for guidance. If you want to abort this run, press Ctrl + C.\n",
+            "  Downloading mdit_py_plugins-0.1.0-py3-none-any.whl (37 kB)\n",
+            "Collecting markdown-it-py[linkify]>=2.0.0 (from gradio)\n",
+            "  Downloading markdown_it_py-3.0.0-py3-none-any.whl (87 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m87.5/87.5 kB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Downloading markdown_it_py-2.2.0-py3-none-any.whl (84 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.5/84.5 kB\u001b[0m \u001b[31m10.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas<3.0,>=1.0->gradio) (2023.3)\n",
+            "Requirement already satisfied: annotated-types>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,<3.0.0,>=1.7.4->gradio) (0.5.0)\n",
+            "Requirement already satisfied: pydantic-core==2.4.0 in /usr/local/lib/python3.10/dist-packages (from pydantic!=1.8,!=1.8.1,!=2.0.0,!=2.0.1,<3.0.0,>=1.7.4->gradio) (2.4.0)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests~=2.0->gradio) (3.4)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests~=2.0->gradio) (2.0.4)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests~=2.0->gradio) (2023.7.22)\n",
+            "Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.10/dist-packages (from uvicorn>=0.14.0->gradio) (8.1.6)\n",
+            "Collecting h11>=0.8 (from uvicorn>=0.14.0->gradio)\n",
+            "  Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting starlette<0.28.0,>=0.27.0 (from fastapi->gradio)\n",
+            "  Downloading starlette-0.27.0-py3-none-any.whl (66 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.0/67.0 kB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting httpcore<0.18.0,>=0.15.0 (from httpx->gradio)\n",
+            "  Downloading httpcore-0.17.3-py3-none-any.whl (74 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m74.5/74.5 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from httpx->gradio) (1.3.0)\n",
+            "Requirement already satisfied: anyio<5.0,>=3.0 in /usr/local/lib/python3.10/dist-packages (from httpcore<0.18.0,>=0.15.0->httpx->gradio) (3.7.1)\n",
+            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio) (2023.7.1)\n",
+            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio) (0.30.2)\n",
+            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio) (0.9.2)\n",
+            "Requirement already satisfied: uc-micro-py in /usr/local/lib/python3.10/dist-packages (from linkify-it-py<3,>=1->markdown-it-py[linkify]>=2.0.0->gradio) (1.0.2)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib~=3.0->gradio) (1.16.0)\n",
+            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5.0,>=3.0->httpcore<0.18.0,>=0.15.0->httpx->gradio) (1.1.2)\n",
+            "Building wheels for collected packages: ffmpy\n",
+            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for ffmpy: filename=ffmpy-0.3.1-py3-none-any.whl size=5579 sha256=123cf75801dd455952791cdb1504beedda07f882e3c00eab584744f08413b59b\n",
+            "  Stored in directory: /root/.cache/pip/wheels/01/a6/d1/1c0828c304a4283b2c1639a09ad86f83d7c487ef34c6b4a1bf\n",
+            "Successfully built ffmpy\n",
+            "Installing collected packages: pydub, ffmpy, websockets, semantic-version, python-multipart, orjson, markdown-it-py, h11, aiofiles, uvicorn, starlette, mdit-py-plugins, huggingface-hub, httpcore, httpx, fastapi, gradio-client, gradio\n",
+            "  Attempting uninstall: markdown-it-py\n",
+            "    Found existing installation: markdown-it-py 3.0.0\n",
+            "    Uninstalling markdown-it-py-3.0.0:\n",
+            "      Successfully uninstalled markdown-it-py-3.0.0\n",
+            "  Attempting uninstall: mdit-py-plugins\n",
+            "    Found existing installation: mdit-py-plugins 0.4.0\n",
+            "    Uninstalling mdit-py-plugins-0.4.0:\n",
+            "      Successfully uninstalled mdit-py-plugins-0.4.0\n",
+            "Successfully installed aiofiles-23.2.1 fastapi-0.101.0 ffmpy-0.3.1 gradio-3.40.1 gradio-client-0.4.0 h11-0.14.0 httpcore-0.17.3 httpx-0.24.1 huggingface-hub-0.16.4 markdown-it-py-2.2.0 mdit-py-plugins-0.3.3 orjson-3.9.4 pydub-0.25.1 python-multipart-0.0.6 semantic-version-2.10.0 starlette-0.27.0 uvicorn-0.23.2 websockets-11.0.3\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Import Libraries"
+      ],
+      "metadata": {
+        "id": "pfzy8WdkkjVZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "kw5ABr7GzgbD",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "99aae0c7-722e-4e79-e169-ab2f5b4a002e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
+            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
+            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
+            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
+          ]
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "import torch\n",
+        "import nltk_utils"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Data Exploration"
+      ],
+      "metadata": {
+        "id": "Y1RoA7N_kpfX"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# import data\n",
+        "df= pd.read_csv('Symptom2Disease.csv')\n",
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "4lDtHIIczxMh",
+        "outputId": "fe1ccf94-39d6-4b90-991f-e64d6fc3cf47"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Unnamed: 0      label                                               text\n",
+              "0           0  Psoriasis  I have been experiencing a skin rash on my arm...\n",
+              "1           1  Psoriasis  My skin has been peeling, especially on my kne...\n",
+              "2           2  Psoriasis  I have been experiencing joint pain in my fing...\n",
+              "3           3  Psoriasis  There is a silver like dusting on my skin, esp...\n",
+              "4           4  Psoriasis  My nails have small dents or pits in them, and..."
+            ],
+            "text/html": [
+              "\n",
+              "\n",
+              "  <div id=\"df-a9d15972-f668-4715-bc62-957956ee8123\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Unnamed: 0</th>\n",
+              "      <th>label</th>\n",
+              "      <th>text</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>Psoriasis</td>\n",
+              "      <td>I have been experiencing a skin rash on my arm...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Psoriasis</td>\n",
+              "      <td>My skin has been peeling, especially on my kne...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2</td>\n",
+              "      <td>Psoriasis</td>\n",
+              "      <td>I have been experiencing joint pain in my fing...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3</td>\n",
+              "      <td>Psoriasis</td>\n",
+              "      <td>There is a silver like dusting on my skin, esp...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>4</td>\n",
+              "      <td>Psoriasis</td>\n",
+              "      <td>My nails have small dents or pits in them, and...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a9d15972-f668-4715-bc62-957956ee8123')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-872ea3fc-c67a-43ed-b4a7-ee7f1e0f8b39\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-872ea3fc-c67a-43ed-b4a7-ee7f1e0f8b39')\"\n",
+              "              title=\"Suggest charts.\"\n",
+              "              style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "    </div>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: #E8F0FE;\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: #1967D2;\n",
+              "    height: 32px;\n",
+              "    padding: 0 0 0 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: #E2EBFA;\n",
+              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: #174EA6;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "    background-color: #3B4455;\n",
+              "    fill: #D2E3FC;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart:hover {\n",
+              "    background-color: #434B5C;\n",
+              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "    fill: #FFFFFF;\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "    <script>\n",
+              "      async function quickchart(key) {\n",
+              "        const containerElement = document.querySelector('#' + key);\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      }\n",
+              "    </script>\n",
+              "\n",
+              "      <script>\n",
+              "\n",
+              "function displayQuickchartButton(domScope) {\n",
+              "  let quickchartButtonEl =\n",
+              "    domScope.querySelector('#df-872ea3fc-c67a-43ed-b4a7-ee7f1e0f8b39 button.colab-df-quickchart');\n",
+              "  quickchartButtonEl.style.display =\n",
+              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "}\n",
+              "\n",
+              "        displayQuickchartButton(document);\n",
+              "      </script>\n",
+              "      <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-a9d15972-f668-4715-bc62-957956ee8123 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-a9d15972-f668-4715-bc62-957956ee8123');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5FDdahbZz0Wf",
+        "outputId": "3526cb7b-8e7c-49cd-a8a3-8f303b77b324"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 1200 entries, 0 to 1199\n",
+            "Data columns (total 3 columns):\n",
+            " #   Column      Non-Null Count  Dtype \n",
+            "---  ------      --------------  ----- \n",
+            " 0   Unnamed: 0  1200 non-null   int64 \n",
+            " 1   label       1200 non-null   object\n",
+            " 2   text        1200 non-null   object\n",
+            "dtypes: int64(1), object(2)\n",
+            "memory usage: 28.2+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Check for data classes\n",
+        "df['label'].nunique()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XODx9gyQz3dy",
+        "outputId": "f1a9012f-e656-4a3d-d3ca-4283c139d635"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "24"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "a= [df['label'].unique()]\n",
+        "print(a)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RicfgpBfz62x",
+        "outputId": "99a3746f-9084-429b-8302-73b11a7d8c41"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[array(['Psoriasis', 'Varicose Veins', 'Typhoid', 'Chicken pox',\n",
+            "       'Impetigo', 'Dengue', 'Fungal infection', 'Common Cold',\n",
+            "       'Pneumonia', 'Dimorphic Hemorrhoids', 'Arthritis', 'Acne',\n",
+            "       'Bronchial Asthma', 'Hypertension', 'Migraine',\n",
+            "       'Cervical spondylosis', 'Jaundice', 'Malaria',\n",
+            "       'urinary tract infection', 'allergy',\n",
+            "       'gastroesophageal reflux disease', 'drug reaction',\n",
+            "       'peptic ulcer disease', 'diabetes'], dtype=object)]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# sort target data\n",
+        "target=['Psoriasis', 'Varicose Veins', 'Typhoid', 'Chicken pox',\n",
+        "       'Impetigo', 'Dengue', 'Fungal infection', 'Common Cold',\n",
+        "       'Pneumonia', 'Dimorphic Hemorrhoids', 'Arthritis', 'Acne',\n",
+        "       'Bronchial Asthma', 'Hypertension', 'Migraine',\n",
+        "       'Cervical spondylosis', 'Jaundice', 'Malaria',\n",
+        "       'urinary tract infection', 'allergy',\n",
+        "       'gastroesophageal reflux disease', 'drug reaction',\n",
+        "       'peptic ulcer disease', 'diabetes']\n",
+        "real_target= sorted(target)"
+      ],
+      "metadata": {
+        "id": "3lMkNCJjz-TK"
+      },
+      "execution_count": 7,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "real_target"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Fy-8tI6Lvl8L",
+        "outputId": "c606a4d7-4462-43ec-b417-e2ef437e29a1"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['Acne',\n",
+              " 'Arthritis',\n",
+              " 'Bronchial Asthma',\n",
+              " 'Cervical spondylosis',\n",
+              " 'Chicken pox',\n",
+              " 'Common Cold',\n",
+              " 'Dengue',\n",
+              " 'Dimorphic Hemorrhoids',\n",
+              " 'Fungal infection',\n",
+              " 'Hypertension',\n",
+              " 'Impetigo',\n",
+              " 'Jaundice',\n",
+              " 'Malaria',\n",
+              " 'Migraine',\n",
+              " 'Pneumonia',\n",
+              " 'Psoriasis',\n",
+              " 'Typhoid',\n",
+              " 'Varicose Veins',\n",
+              " 'allergy',\n",
+              " 'diabetes',\n",
+              " 'drug reaction',\n",
+              " 'gastroesophageal reflux disease',\n",
+              " 'peptic ulcer disease',\n",
+              " 'urinary tract infection']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "target_dict= {i:j for i,j in enumerate(sorted(target))}\n",
+        "target_dict"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "zXKfP11m0CfF",
+        "outputId": "14a5a06f-db7d-42f8-c245-dc2847e30b21"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{0: 'Acne',\n",
+              " 1: 'Arthritis',\n",
+              " 2: 'Bronchial Asthma',\n",
+              " 3: 'Cervical spondylosis',\n",
+              " 4: 'Chicken pox',\n",
+              " 5: 'Common Cold',\n",
+              " 6: 'Dengue',\n",
+              " 7: 'Dimorphic Hemorrhoids',\n",
+              " 8: 'Fungal infection',\n",
+              " 9: 'Hypertension',\n",
+              " 10: 'Impetigo',\n",
+              " 11: 'Jaundice',\n",
+              " 12: 'Malaria',\n",
+              " 13: 'Migraine',\n",
+              " 14: 'Pneumonia',\n",
+              " 15: 'Psoriasis',\n",
+              " 16: 'Typhoid',\n",
+              " 17: 'Varicose Veins',\n",
+              " 18: 'allergy',\n",
+              " 19: 'diabetes',\n",
+              " 20: 'drug reaction',\n",
+              " 21: 'gastroesophageal reflux disease',\n",
+              " 22: 'peptic ulcer disease',\n",
+              " 23: 'urinary tract infection'}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['label']= df['label'].replace({j:i for i,j in enumerate(sorted(target))})"
+      ],
+      "metadata": {
+        "id": "_jGNr54w0MG6"
+      },
+      "execution_count": 10,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "dZWf1flT0bBZ",
+        "outputId": "8ec9dfe3-3fd1-46e7-da3b-621ee01509e6"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   Unnamed: 0  label                                               text\n",
+              "0           0     15  I have been experiencing a skin rash on my arm...\n",
+              "1           1     15  My skin has been peeling, especially on my kne...\n",
+              "2           2     15  I have been experiencing joint pain in my fing...\n",
+              "3           3     15  There is a silver like dusting on my skin, esp...\n",
+              "4           4     15  My nails have small dents or pits in them, and..."
+            ],
+            "text/html": [
+              "\n",
+              "\n",
+              "  <div id=\"df-6716f2e8-a086-4041-8fcf-bb6d29a21b39\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Unnamed: 0</th>\n",
+              "      <th>label</th>\n",
+              "      <th>text</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>15</td>\n",
+              "      <td>I have been experiencing a skin rash on my arm...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>15</td>\n",
+              "      <td>My skin has been peeling, especially on my kne...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2</td>\n",
+              "      <td>15</td>\n",
+              "      <td>I have been experiencing joint pain in my fing...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3</td>\n",
+              "      <td>15</td>\n",
+              "      <td>There is a silver like dusting on my skin, esp...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>4</td>\n",
+              "      <td>15</td>\n",
+              "      <td>My nails have small dents or pits in them, and...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6716f2e8-a086-4041-8fcf-bb6d29a21b39')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-0d7c8368-ac58-4b61-b65d-55abc794d32f\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-0d7c8368-ac58-4b61-b65d-55abc794d32f')\"\n",
+              "              title=\"Suggest charts.\"\n",
+              "              style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "    </div>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: #E8F0FE;\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: #1967D2;\n",
+              "    height: 32px;\n",
+              "    padding: 0 0 0 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: #E2EBFA;\n",
+              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: #174EA6;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "    background-color: #3B4455;\n",
+              "    fill: #D2E3FC;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart:hover {\n",
+              "    background-color: #434B5C;\n",
+              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "    fill: #FFFFFF;\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "    <script>\n",
+              "      async function quickchart(key) {\n",
+              "        const containerElement = document.querySelector('#' + key);\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      }\n",
+              "    </script>\n",
+              "\n",
+              "      <script>\n",
+              "\n",
+              "function displayQuickchartButton(domScope) {\n",
+              "  let quickchartButtonEl =\n",
+              "    domScope.querySelector('#df-0d7c8368-ac58-4b61-b65d-55abc794d32f button.colab-df-quickchart');\n",
+              "  quickchartButtonEl.style.display =\n",
+              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "}\n",
+              "\n",
+              "        displayQuickchartButton(document);\n",
+              "      </script>\n",
+              "      <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-6716f2e8-a086-4041-8fcf-bb6d29a21b39 button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-6716f2e8-a086-4041-8fcf-bb6d29a21b39');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.drop('Unnamed: 0', axis= 1, inplace= True)"
+      ],
+      "metadata": {
+        "id": "J4rvU7zn0eTJ"
+      },
+      "execution_count": 12,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.duplicated().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wYhQdWRW0jwy",
+        "outputId": "72434333-517f-4271-b868-f8296074df08"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "47"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df[df.duplicated]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "WTUzzKbb0ogZ",
+        "outputId": "4f7b0388-ba04-4629-ec50-358383d97306"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      label                                               text\n",
+              "163       4  I'm feeling fatigued and have no energy. I can...\n",
+              "387       5  I've been quite exhausted and ill. My throat h...\n",
+              "430      14  I have a really high fever, and I have problem...\n",
+              "433      14  I'm having a hard time breathing and I feel re...\n",
+              "438      14  Lately I've been experiencing chills, fatigue,...\n",
+              "469       7  I've been constipated and it's really hard to ...\n",
+              "470       7  Since I've been constipated, using the restroo...\n",
+              "471       7  I've been constipated and it's really hard to ...\n",
+              "487       7  Lately I've been experiencing constipation and...\n",
+              "489       7  I've recently been suffering from constipation...\n",
+              "490       7  I've been experiencing a lot of bowel movement...\n",
+              "491       7  I'm having a lot of trouble with my bowel move...\n",
+              "492       7  My bowel motions have been really difficult fo...\n",
+              "493       7  I've been experiencing a lot of problems with ...\n",
+              "520       1  I've been feeling really weak in my muscles an...\n",
+              "521       1  My muscles have been feeling really weak, and ...\n",
+              "526       1  I've been experiencing stiffness and weakness ...\n",
+              "527       1  I've been feeling really weak in my muscles an...\n",
+              "563       0  A nasty rash has just appeared on my skin. Bla...\n",
+              "573       0  I just developed a really nasty rash on my ski...\n",
+              "574       0  I've been dealing with a really nasty rash on ...\n",
+              "580       0  A skin rash with several pus-filled pimples an...\n",
+              "647       2  I've been struggling with fatigue and a consta...\n",
+              "706      13  I've been facing visual disruptions, seeing th...\n",
+              "738      13  Along with excessive appetite, a stiff neck, h...\n",
+              "748      13  I have been experiencing acidity, indigestion,...\n",
+              "778       3  Back pain, a persistent cough, and numbness in...\n",
+              "821      11  I've been feeling extremely scratchy, sick, an...\n",
+              "822      11  I've been feeling extremely scratchy, sick, an...\n",
+              "834      11  I've been exhausted and experiencing nausea an...\n",
+              "835      11  I have been suffering from itching, vomiting, ...\n",
+              "836      11  I've been feeling scratchy, sick, and worn out...\n",
+              "837      11  The itch, the nausea, and the weariness have b...\n",
+              "838      11  I have been experiencing intense itching, vomi...\n",
+              "839      11  I've been feeling extremely scratchy, sick, an...\n",
+              "840      11  I've felt really scratchy, nauseated, and worn...\n",
+              "841      11  I have been having severe itching, vomiting, a...\n",
+              "842      11  I've been feeling really scratchy, dizzy, and ...\n",
+              "843      11  I've been experiencing intense itchiness, naus...\n",
+              "852      12  I've had a high temperature, vomiting, chills,...\n",
+              "859      12  I've been experiencing severe body itchiness, ...\n",
+              "866      12  I have a high fever, severe itching, chills, a...\n",
+              "867      12  I have a high temperature, vomiting, chills, a...\n",
+              "873      12  I've had a high temperature, vomiting, chills,...\n",
+              "894      12  I have a high fever, severe itching, chills, a...\n",
+              "1048     21  Even when I don't have anything acidic in my s...\n",
+              "1049     21  I'm not in the mood to eat, and swallowing is ..."
+            ],
+            "text/html": [
+              "\n",
+              "\n",
+              "  <div id=\"df-fbacbaef-45f2-4004-9019-fbeef17780cf\">\n",
+              "    <div class=\"colab-df-container\">\n",
+              "      <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>label</th>\n",
+              "      <th>text</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>163</th>\n",
+              "      <td>4</td>\n",
+              "      <td>I'm feeling fatigued and have no energy. I can...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>387</th>\n",
+              "      <td>5</td>\n",
+              "      <td>I've been quite exhausted and ill. My throat h...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>430</th>\n",
+              "      <td>14</td>\n",
+              "      <td>I have a really high fever, and I have problem...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>433</th>\n",
+              "      <td>14</td>\n",
+              "      <td>I'm having a hard time breathing and I feel re...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>438</th>\n",
+              "      <td>14</td>\n",
+              "      <td>Lately I've been experiencing chills, fatigue,...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>469</th>\n",
+              "      <td>7</td>\n",
+              "      <td>I've been constipated and it's really hard to ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>470</th>\n",
+              "      <td>7</td>\n",
+              "      <td>Since I've been constipated, using the restroo...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>471</th>\n",
+              "      <td>7</td>\n",
+              "      <td>I've been constipated and it's really hard to ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>487</th>\n",
+              "      <td>7</td>\n",
+              "      <td>Lately I've been experiencing constipation and...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>489</th>\n",
+              "      <td>7</td>\n",
+              "      <td>I've recently been suffering from constipation...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>490</th>\n",
+              "      <td>7</td>\n",
+              "      <td>I've been experiencing a lot of bowel movement...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>491</th>\n",
+              "      <td>7</td>\n",
+              "      <td>I'm having a lot of trouble with my bowel move...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>492</th>\n",
+              "      <td>7</td>\n",
+              "      <td>My bowel motions have been really difficult fo...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>493</th>\n",
+              "      <td>7</td>\n",
+              "      <td>I've been experiencing a lot of problems with ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>520</th>\n",
+              "      <td>1</td>\n",
+              "      <td>I've been feeling really weak in my muscles an...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>521</th>\n",
+              "      <td>1</td>\n",
+              "      <td>My muscles have been feeling really weak, and ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>526</th>\n",
+              "      <td>1</td>\n",
+              "      <td>I've been experiencing stiffness and weakness ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>527</th>\n",
+              "      <td>1</td>\n",
+              "      <td>I've been feeling really weak in my muscles an...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>563</th>\n",
+              "      <td>0</td>\n",
+              "      <td>A nasty rash has just appeared on my skin. Bla...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>573</th>\n",
+              "      <td>0</td>\n",
+              "      <td>I just developed a really nasty rash on my ski...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>574</th>\n",
+              "      <td>0</td>\n",
+              "      <td>I've been dealing with a really nasty rash on ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>580</th>\n",
+              "      <td>0</td>\n",
+              "      <td>A skin rash with several pus-filled pimples an...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>647</th>\n",
+              "      <td>2</td>\n",
+              "      <td>I've been struggling with fatigue and a consta...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>706</th>\n",
+              "      <td>13</td>\n",
+              "      <td>I've been facing visual disruptions, seeing th...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>738</th>\n",
+              "      <td>13</td>\n",
+              "      <td>Along with excessive appetite, a stiff neck, h...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>748</th>\n",
+              "      <td>13</td>\n",
+              "      <td>I have been experiencing acidity, indigestion,...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>778</th>\n",
+              "      <td>3</td>\n",
+              "      <td>Back pain, a persistent cough, and numbness in...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>821</th>\n",
+              "      <td>11</td>\n",
+              "      <td>I've been feeling extremely scratchy, sick, an...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>822</th>\n",
+              "      <td>11</td>\n",
+              "      <td>I've been feeling extremely scratchy, sick, an...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>834</th>\n",
+              "      <td>11</td>\n",
+              "      <td>I've been exhausted and experiencing nausea an...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>835</th>\n",
+              "      <td>11</td>\n",
+              "      <td>I have been suffering from itching, vomiting, ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>836</th>\n",
+              "      <td>11</td>\n",
+              "      <td>I've been feeling scratchy, sick, and worn out...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>837</th>\n",
+              "      <td>11</td>\n",
+              "      <td>The itch, the nausea, and the weariness have b...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>838</th>\n",
+              "      <td>11</td>\n",
+              "      <td>I have been experiencing intense itching, vomi...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>839</th>\n",
+              "      <td>11</td>\n",
+              "      <td>I've been feeling extremely scratchy, sick, an...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>840</th>\n",
+              "      <td>11</td>\n",
+              "      <td>I've felt really scratchy, nauseated, and worn...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>841</th>\n",
+              "      <td>11</td>\n",
+              "      <td>I have been having severe itching, vomiting, a...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>842</th>\n",
+              "      <td>11</td>\n",
+              "      <td>I've been feeling really scratchy, dizzy, and ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>843</th>\n",
+              "      <td>11</td>\n",
+              "      <td>I've been experiencing intense itchiness, naus...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>852</th>\n",
+              "      <td>12</td>\n",
+              "      <td>I've had a high temperature, vomiting, chills,...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>859</th>\n",
+              "      <td>12</td>\n",
+              "      <td>I've been experiencing severe body itchiness, ...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>866</th>\n",
+              "      <td>12</td>\n",
+              "      <td>I have a high fever, severe itching, chills, a...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>867</th>\n",
+              "      <td>12</td>\n",
+              "      <td>I have a high temperature, vomiting, chills, a...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>873</th>\n",
+              "      <td>12</td>\n",
+              "      <td>I've had a high temperature, vomiting, chills,...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>894</th>\n",
+              "      <td>12</td>\n",
+              "      <td>I have a high fever, severe itching, chills, a...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1048</th>\n",
+              "      <td>21</td>\n",
+              "      <td>Even when I don't have anything acidic in my s...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1049</th>\n",
+              "      <td>21</td>\n",
+              "      <td>I'm not in the mood to eat, and swallowing is ...</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fbacbaef-45f2-4004-9019-fbeef17780cf')\"\n",
+              "              title=\"Convert this dataframe to an interactive table.\"\n",
+              "              style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
+              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-f98cb302-5936-4174-8034-a022213069b6\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f98cb302-5936-4174-8034-a022213069b6')\"\n",
+              "              title=\"Suggest charts.\"\n",
+              "              style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "    </div>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: #E8F0FE;\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: #1967D2;\n",
+              "    height: 32px;\n",
+              "    padding: 0 0 0 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: #E2EBFA;\n",
+              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: #174EA6;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "    background-color: #3B4455;\n",
+              "    fill: #D2E3FC;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart:hover {\n",
+              "    background-color: #434B5C;\n",
+              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "    fill: #FFFFFF;\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "    <script>\n",
+              "      async function quickchart(key) {\n",
+              "        const containerElement = document.querySelector('#' + key);\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      }\n",
+              "    </script>\n",
+              "\n",
+              "      <script>\n",
+              "\n",
+              "function displayQuickchartButton(domScope) {\n",
+              "  let quickchartButtonEl =\n",
+              "    domScope.querySelector('#df-f98cb302-5936-4174-8034-a022213069b6 button.colab-df-quickchart');\n",
+              "  quickchartButtonEl.style.display =\n",
+              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "}\n",
+              "\n",
+              "        displayQuickchartButton(document);\n",
+              "      </script>\n",
+              "      <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "      <script>\n",
+              "        const buttonEl =\n",
+              "          document.querySelector('#df-fbacbaef-45f2-4004-9019-fbeef17780cf button.colab-df-convert');\n",
+              "        buttonEl.style.display =\n",
+              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "        async function convertToInteractive(key) {\n",
+              "          const element = document.querySelector('#df-fbacbaef-45f2-4004-9019-fbeef17780cf');\n",
+              "          const dataTable =\n",
+              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                     [key], {});\n",
+              "          if (!dataTable) return;\n",
+              "\n",
+              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "            + ' to learn more about interactive tables.';\n",
+              "          element.innerHTML = '';\n",
+              "          dataTable['output_type'] = 'display_data';\n",
+              "          await google.colab.output.renderOutput(dataTable, element);\n",
+              "          const docLink = document.createElement('div');\n",
+              "          docLink.innerHTML = docLinkHtml;\n",
+              "          element.appendChild(docLink);\n",
+              "        }\n",
+              "      </script>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.drop_duplicates(inplace= True)"
+      ],
+      "metadata": {
+        "id": "LnR_tvss0riM"
+      },
+      "execution_count": 15,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['label'].value_counts()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "P7Qv1XLn0v8g",
+        "outputId": "0061f0c5-df45-48fe-becd-cac01a2027b0"
+      },
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "15    50\n",
+              "17    50\n",
+              "22    50\n",
+              "20    50\n",
+              "18    50\n",
+              "23    50\n",
+              "9     50\n",
+              "19    50\n",
+              "8     50\n",
+              "6     50\n",
+              "10    50\n",
+              "16    50\n",
+              "5     49\n",
+              "3     49\n",
+              "4     49\n",
+              "2     49\n",
+              "21    48\n",
+              "14    47\n",
+              "13    47\n",
+              "1     46\n",
+              "0     46\n",
+              "12    44\n",
+              "7     41\n",
+              "11    38\n",
+              "Name: label, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_data, test_data= train_test_split(df, test_size=0.15, random_state=42 )"
+      ],
+      "metadata": {
+        "id": "P6R_UB3p0zLG"
+      },
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_data.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SNi-gm0Z03Z6",
+        "outputId": "a91a0bc5-00dc-450f-cb37-b821aec82296"
+      },
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "Int64Index: 980 entries, 618 to 1173\n",
+            "Data columns (total 2 columns):\n",
+            " #   Column  Non-Null Count  Dtype \n",
+            "---  ------  --------------  ----- \n",
+            " 0   label   980 non-null    int64 \n",
+            " 1   text    980 non-null    object\n",
+            "dtypes: int64(1), object(1)\n",
+            "memory usage: 23.0+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "test_data.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QC_xSoD51CTL",
+        "outputId": "5a15326a-ba4c-4896-b428-7e175a1a67c3"
+      },
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "Int64Index: 173 entries, 794 to 139\n",
+            "Data columns (total 2 columns):\n",
+            " #   Column  Non-Null Count  Dtype \n",
+            "---  ------  --------------  ----- \n",
+            " 0   label   173 non-null    int64 \n",
+            " 1   text    173 non-null    object\n",
+            "dtypes: int64(1), object(1)\n",
+            "memory usage: 4.1+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_data['label'].value_counts().sort_index()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4FaxbciD1El2",
+        "outputId": "69423719-3cab-4122-cc7b-5abfdeddde49"
+      },
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0     37\n",
+              "1     41\n",
+              "2     41\n",
+              "3     40\n",
+              "4     44\n",
+              "5     42\n",
+              "6     41\n",
+              "7     32\n",
+              "8     40\n",
+              "9     43\n",
+              "10    45\n",
+              "11    32\n",
+              "12    40\n",
+              "13    41\n",
+              "14    37\n",
+              "15    45\n",
+              "16    41\n",
+              "17    41\n",
+              "18    40\n",
+              "19    46\n",
+              "20    44\n",
+              "21    38\n",
+              "22    45\n",
+              "23    44\n",
+              "Name: label, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "test_data['label'].value_counts().sort_index()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2y54iXig1LqJ",
+        "outputId": "ca536083-4ad4-4ba0-838f-3759eca9e552"
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0      9\n",
+              "1      5\n",
+              "2      8\n",
+              "3      9\n",
+              "4      5\n",
+              "5      7\n",
+              "6      9\n",
+              "7      9\n",
+              "8     10\n",
+              "9      7\n",
+              "10     5\n",
+              "11     6\n",
+              "12     4\n",
+              "13     6\n",
+              "14    10\n",
+              "15     5\n",
+              "16     9\n",
+              "17     9\n",
+              "18    10\n",
+              "19     4\n",
+              "20     6\n",
+              "21    10\n",
+              "22     5\n",
+              "23     6\n",
+              "Name: label, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 21
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Prepare data for training\n",
+        "\n",
+        "- Convert Text to TF-IDF Vectors\n",
+        "- Convert Vectors to Pytorch Tensors\n",
+        "- Convert tensors to pytorch dataloaders"
+      ],
+      "metadata": {
+        "id": "2PYVVpB2lBdh"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Convert text to vectors"
+      ],
+      "metadata": {
+        "id": "pmViJWyClSEL"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "vectorizer= nltk_utils.vectorizer()"
+      ],
+      "metadata": {
+        "id": "P6bbmklS1q-q"
+      },
+      "execution_count": 22,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "vectorizer.fit(train_data.text)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 271
+        },
+        "id": "5wCPe7oI2I8K",
+        "outputId": "0946fb17-7aa2-4dc9-9014-4717e2d3bfb7"
+      },
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py:528: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py:409: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens [\"'d\", \"'s\", 'abov', 'ani', 'becaus', 'befor', 'could', 'doe', 'dure', 'might', 'must', \"n't\", 'need', 'onc', 'onli', 'ourselv', 'sha', 'themselv', 'veri', 'whi', 'wo', 'would', 'yourselv'] not in stop_words.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "TfidfVectorizer(stop_words=['i', 'me', 'my', 'myself', 'we', 'our', 'ours',\n",
+              "                            'ourselves', 'you', \"you're\", \"you've\", \"you'll\",\n",
+              "                            \"you'd\", 'your', 'yours', 'yourself', 'yourselves',\n",
+              "                            'he', 'him', 'his', 'himself', 'she', \"she's\",\n",
+              "                            'her', 'hers', 'herself', 'it', \"it's\", 'its',\n",
+              "                            'itself', ...],\n",
+              "                tokenizer=<function tokenize at 0x7cd239754d30>)"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TfidfVectorizer(stop_words=[&#x27;i&#x27;, &#x27;me&#x27;, &#x27;my&#x27;, &#x27;myself&#x27;, &#x27;we&#x27;, &#x27;our&#x27;, &#x27;ours&#x27;,\n",
+              "                            &#x27;ourselves&#x27;, &#x27;you&#x27;, &quot;you&#x27;re&quot;, &quot;you&#x27;ve&quot;, &quot;you&#x27;ll&quot;,\n",
+              "                            &quot;you&#x27;d&quot;, &#x27;your&#x27;, &#x27;yours&#x27;, &#x27;yourself&#x27;, &#x27;yourselves&#x27;,\n",
+              "                            &#x27;he&#x27;, &#x27;him&#x27;, &#x27;his&#x27;, &#x27;himself&#x27;, &#x27;she&#x27;, &quot;she&#x27;s&quot;,\n",
+              "                            &#x27;her&#x27;, &#x27;hers&#x27;, &#x27;herself&#x27;, &#x27;it&#x27;, &quot;it&#x27;s&quot;, &#x27;its&#x27;,\n",
+              "                            &#x27;itself&#x27;, ...],\n",
+              "                tokenizer=&lt;function tokenize at 0x7cd239754d30&gt;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer(stop_words=[&#x27;i&#x27;, &#x27;me&#x27;, &#x27;my&#x27;, &#x27;myself&#x27;, &#x27;we&#x27;, &#x27;our&#x27;, &#x27;ours&#x27;,\n",
+              "                            &#x27;ourselves&#x27;, &#x27;you&#x27;, &quot;you&#x27;re&quot;, &quot;you&#x27;ve&quot;, &quot;you&#x27;ll&quot;,\n",
+              "                            &quot;you&#x27;d&quot;, &#x27;your&#x27;, &#x27;yours&#x27;, &#x27;yourself&#x27;, &#x27;yourselves&#x27;,\n",
+              "                            &#x27;he&#x27;, &#x27;him&#x27;, &#x27;his&#x27;, &#x27;himself&#x27;, &#x27;she&#x27;, &quot;she&#x27;s&quot;,\n",
+              "                            &#x27;her&#x27;, &#x27;hers&#x27;, &#x27;herself&#x27;, &#x27;it&#x27;, &quot;it&#x27;s&quot;, &#x27;its&#x27;,\n",
+              "                            &#x27;itself&#x27;, ...],\n",
+              "                tokenizer=&lt;function tokenize at 0x7cd239754d30&gt;)</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "vectorizer.get_feature_names_out()[: 100]\n",
+        "vectorizer= vectorizer"
+      ],
+      "metadata": {
+        "id": "Tatp2DyG2LQF"
+      },
+      "execution_count": 24,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "vectorizer"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 179
+        },
+        "id": "Nql0ED231MlT",
+        "outputId": "bd9f5dd5-704e-4cb0-8a96-2f573630f474"
+      },
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "TfidfVectorizer(stop_words=['i', 'me', 'my', 'myself', 'we', 'our', 'ours',\n",
+              "                            'ourselves', 'you', \"you're\", \"you've\", \"you'll\",\n",
+              "                            \"you'd\", 'your', 'yours', 'yourself', 'yourselves',\n",
+              "                            'he', 'him', 'his', 'himself', 'she', \"she's\",\n",
+              "                            'her', 'hers', 'herself', 'it', \"it's\", 'its',\n",
+              "                            'itself', ...],\n",
+              "                tokenizer=<function tokenize at 0x7cd239754d30>)"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>TfidfVectorizer(stop_words=[&#x27;i&#x27;, &#x27;me&#x27;, &#x27;my&#x27;, &#x27;myself&#x27;, &#x27;we&#x27;, &#x27;our&#x27;, &#x27;ours&#x27;,\n",
+              "                            &#x27;ourselves&#x27;, &#x27;you&#x27;, &quot;you&#x27;re&quot;, &quot;you&#x27;ve&quot;, &quot;you&#x27;ll&quot;,\n",
+              "                            &quot;you&#x27;d&quot;, &#x27;your&#x27;, &#x27;yours&#x27;, &#x27;yourself&#x27;, &#x27;yourselves&#x27;,\n",
+              "                            &#x27;he&#x27;, &#x27;him&#x27;, &#x27;his&#x27;, &#x27;himself&#x27;, &#x27;she&#x27;, &quot;she&#x27;s&quot;,\n",
+              "                            &#x27;her&#x27;, &#x27;hers&#x27;, &#x27;herself&#x27;, &#x27;it&#x27;, &quot;it&#x27;s&quot;, &#x27;its&#x27;,\n",
+              "                            &#x27;itself&#x27;, ...],\n",
+              "                tokenizer=&lt;function tokenize at 0x7cd239754d30&gt;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">TfidfVectorizer</label><div class=\"sk-toggleable__content\"><pre>TfidfVectorizer(stop_words=[&#x27;i&#x27;, &#x27;me&#x27;, &#x27;my&#x27;, &#x27;myself&#x27;, &#x27;we&#x27;, &#x27;our&#x27;, &#x27;ours&#x27;,\n",
+              "                            &#x27;ourselves&#x27;, &#x27;you&#x27;, &quot;you&#x27;re&quot;, &quot;you&#x27;ve&quot;, &quot;you&#x27;ll&quot;,\n",
+              "                            &quot;you&#x27;d&quot;, &#x27;your&#x27;, &#x27;yours&#x27;, &#x27;yourself&#x27;, &#x27;yourselves&#x27;,\n",
+              "                            &#x27;he&#x27;, &#x27;him&#x27;, &#x27;his&#x27;, &#x27;himself&#x27;, &#x27;she&#x27;, &quot;she&#x27;s&quot;,\n",
+              "                            &#x27;her&#x27;, &#x27;hers&#x27;, &#x27;herself&#x27;, &#x27;it&#x27;, &quot;it&#x27;s&quot;, &#x27;its&#x27;,\n",
+              "                            &#x27;itself&#x27;, ...],\n",
+              "                tokenizer=&lt;function tokenize at 0x7cd239754d30&gt;)</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 25
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data_input= vectorizer.transform(train_data.text)\n",
+        "test_data_input= vectorizer.transform(test_data.text)"
+      ],
+      "metadata": {
+        "id": "-DWLYaEQ2iq-"
+      },
+      "execution_count": 26,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data_input.shape, test_data_input.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PY58z-TP2nl9",
+        "outputId": "78f4a228-928b-417f-9b2c-4d43d490a4ba"
+      },
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "((980, 1080), (173, 1080))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data_input[0]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "arlW5Guj2uX9",
+        "outputId": "478cfdb0-0bd0-41b3-a8cc-8658fae3ee10"
+      },
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<1x1080 sparse matrix of type '<class 'numpy.float64'>'\n",
+              "\twith 23 stored elements in Compressed Sparse Row format>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Convert vectors to tensors\n",
+        "input_data_tensors= torch.tensor(data_input.toarray()).to(torch.float32)\n",
+        "test_data_tensors= torch.tensor(test_data_input.toarray()).to(torch.float32)"
+      ],
+      "metadata": {
+        "id": "pk9EQnAD2ymM"
+      },
+      "execution_count": 29,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "input_data_tensors.shape, input_data_tensors.dtype"
+      ],
+      "metadata": {
+        "id": "r3SVyRWnEkUU",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9b01184a-5c6a-4e9d-f882-26a25175aa76"
+      },
+      "execution_count": 30,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(torch.Size([980, 1080]), torch.float32)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 30
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "test_data_tensors.shape,test_data_tensors.dtype"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UqcyhYtUGR7U",
+        "outputId": "a7b647a9-28d3-45a7-d19b-828851ebf3b7"
+      },
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(torch.Size([173, 1080]), torch.float32)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 31
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_data_output= torch.tensor(train_data['label'].values)\n",
+        "test_data_output= torch.tensor(test_data['label'].values)"
+      ],
+      "metadata": {
+        "id": "IA1WGNVnGVJY"
+      },
+      "execution_count": 32,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_data_output.shape, test_data_output.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tuXUxwpcGZVd",
+        "outputId": "2b53d03d-bbc6-4304-cc4c-f60691194d4e"
+      },
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(torch.Size([980]), torch.Size([173]))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 33
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Join input and target data together and create dataloaders"
+      ],
+      "metadata": {
+        "id": "vj0VK9NnGfCj"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import preprocess_data\n",
+        "import model"
+      ],
+      "metadata": {
+        "id": "CFXBkZuBGnpd"
+      },
+      "execution_count": 34,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_dataset= preprocess_data.preprocess_data(input_data_tensors, train_data_output)\n",
+        "test_dataset= preprocess_data.preprocess_data(test_data_tensors, test_data_output)"
+      ],
+      "metadata": {
+        "id": "T8CCIT5yGshH"
+      },
+      "execution_count": 35,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_dataset[0]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gP8SZWE8Gvkv",
+        "outputId": "62bbcf77-8e89-4f08-e099-7e1a845fc357"
+      },
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(tensor([0., 0., 0.,  ..., 0., 0., 0.]), tensor(2))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_dataloader= preprocess_data.dataloader(dataset=train_dataset,\n",
+        "                                             batch_size=32, shuffle= True, num_workers=2)\n",
+        "test_dataloader= preprocess_data.dataloader(dataset=test_dataset,\n",
+        "                                             batch_size=32, shuffle= False, num_workers=2)"
+      ],
+      "metadata": {
+        "id": "wA6eMHITGzt8"
+      },
+      "execution_count": 37,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "len(train_dataloader), len(test_dataloader)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "o_GMPlh-LIuU",
+        "outputId": "68c3229c-af7c-4dbc-ffbf-9fe40af61137"
+      },
+      "execution_count": 38,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(31, 6)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 38
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "text, target= next(iter(train_dataloader))"
+      ],
+      "metadata": {
+        "id": "Y26dkRSRLZsi"
+      },
+      "execution_count": 39,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "text.shape, target.shape"
+      ],
+      "metadata": {
+        "id": "X1KEYvGILexu",
+        "outputId": "b3e108e3-d041-4489-9a4b-106e5dc75dee",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 40,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(torch.Size([32, 1080]), torch.Size([32]))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 40
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "device= 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+        "device"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 36
+        },
+        "id": "rkaKKB_e8y85",
+        "outputId": "522453b1-aadf-4814-ea78-06ede1a6273a"
+      },
+      "execution_count": 41,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'cpu'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 41
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model= model.RNN_model()"
+      ],
+      "metadata": {
+        "id": "fUbWo4-M8zbQ"
+      },
+      "execution_count": 42,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dummy_x= torch.rand(size= [1,1080])\n",
+        "dummy_x.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bMQUuV1Z8zq8",
+        "outputId": "d2bda840-01af-4186-ade5-d8089cdb8d46"
+      },
+      "execution_count": 43,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "torch.Size([1, 1080])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 43
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model(dummy_x)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ZnvhejXE8z5x",
+        "outputId": "2d300e12-0a34-4aff-ff8d-c1f837d5deaa"
+      },
+      "execution_count": 44,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "tensor([[ 0.3610,  0.0999, -0.4230, -0.1520, -0.3612,  0.3440,  0.0806, -0.1656,\n",
+              "          0.1344,  0.0907, -0.2140, -0.0877, -0.5789,  0.1970,  0.3892, -0.1477,\n",
+              "          0.1714, -0.0009, -0.2150, -0.1346, -0.2228,  0.0901, -0.4715, -0.2032]],\n",
+              "       grad_fn=<AddmmBackward0>)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 44
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Train data"
+      ],
+      "metadata": {
+        "id": "6m0rr8vllcV9"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Import metrics\n",
+        "from sklearn.metrics import accuracy_score, f1_score"
+      ],
+      "metadata": {
+        "id": "Et9hV_E480JZ"
+      },
+      "execution_count": 45,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Import loss function and optimizers\n",
+        "from torch.nn.modules.loss import CrossEntropyLoss\n",
+        "loss_fn= CrossEntropyLoss()\n",
+        "optimizer= torch.optim.SGD(model.parameters(), lr= 0.1, weight_decay=0)"
+      ],
+      "metadata": {
+        "id": "xa-GSB6l80V9"
+      },
+      "execution_count": 46,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Train Model"
+      ],
+      "metadata": {
+        "id": "AS3AnXFwm1j5"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "epoch= 500\n",
+        "\n",
+        "results= {\n",
+        "      \"train_loss\": [],\n",
+        "      \"train_accuracy\": [],\n",
+        "      \"test_loss\": [],\n",
+        "      \"test_accuracy\": []\n",
+        "      }\n",
+        "\n",
+        "for i in range(epoch):\n",
+        "  train_loss=0\n",
+        "  train_acc=0\n",
+        "  for batch, (X, y) in enumerate(train_dataloader):\n",
+        "    X, y= X.to(device), y.to(device)\n",
+        "    # Train the model\n",
+        "    model.train()\n",
+        "\n",
+        "    optimizer.zero_grad()\n",
+        "\n",
+        "    y_logits= model(X)\n",
+        "\n",
+        "    # Calculate the loss\n",
+        "    loss= loss_fn(y_logits, y)\n",
+        "    train_loss += loss\n",
+        "\n",
+        "    # ypreds\n",
+        "    y_preds= torch.argmax(torch.softmax(y_logits, dim=1), dim=1)\n",
+        "    accuracy = accuracy_score(y, y_preds)\n",
+        "    train_acc += accuracy\n",
+        "\n",
+        "    # zero grad\n",
+        "    #optimizer.zero_grad()\n",
+        "\n",
+        "    # Loss backward\n",
+        "    loss.backward()\n",
+        "\n",
+        "    # Optimizer step\n",
+        "    optimizer.step()\n",
+        "\n",
+        "  train_loss /= len(train_dataloader)\n",
+        "  train_acc /=len(train_dataloader)\n",
+        "\n",
+        "  test_loss = 0\n",
+        "  test_acc=0\n",
+        "  model.eval()\n",
+        "  with torch.inference_mode():\n",
+        "    for X, y in test_dataloader:\n",
+        "      X, y= X.to(device), y.to(device)\n",
+        "      y_logits= model(X)\n",
+        "      loss= loss_fn(y_logits, y)\n",
+        "      test_loss += loss\n",
+        "      test_preds= torch.argmax(torch.softmax(y_logits, dim=1), dim=1)\n",
+        "      accuracy = accuracy_score(y, test_preds)\n",
+        "      test_acc += accuracy\n",
+        "    test_loss /= len(test_dataloader)\n",
+        "    test_acc /= len(test_dataloader)\n",
+        "\n",
+        "  results['train_loss'].append(train_loss.item())\n",
+        "  results['train_accuracy'].append(train_acc.item())\n",
+        "  results['test_loss'].append(test_loss.item())\n",
+        "  results['test_accuracy'].append(test_acc.item())\n",
+        "  if i % 50 == 0:\n",
+        "    print(f\"\\nTrain loss: {train_loss:.5f} | Train Acc: {train_acc:.5f} | Test loss: {test_loss:.5f} | Test Acc: {test_acc:.5f} |\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ppocdsju80iS",
+        "outputId": "486d6dfe-d85a-4e29-b939-d189ac518a15"
+      },
+      "execution_count": 47,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Train loss: 3.17753 | Train Acc: 0.03992 | Test loss: 3.16496 | Test Acc: 0.12179 |\n",
+            "\n",
+            "Train loss: 0.63488 | Train Acc: 0.95968 | Test loss: 0.75937 | Test Acc: 0.89343 |\n",
+            "\n",
+            "Train loss: 0.09802 | Train Acc: 1.00000 | Test loss: 0.22350 | Test Acc: 0.96875 |\n",
+            "\n",
+            "Train loss: 0.03849 | Train Acc: 1.00000 | Test loss: 0.14364 | Test Acc: 0.98438 |\n",
+            "\n",
+            "Train loss: 0.02220 | Train Acc: 1.00000 | Test loss: 0.12408 | Test Acc: 0.98438 |\n",
+            "\n",
+            "Train loss: 0.01508 | Train Acc: 1.00000 | Test loss: 0.10855 | Test Acc: 0.97917 |\n",
+            "\n",
+            "Train loss: 0.01129 | Train Acc: 1.00000 | Test loss: 0.09876 | Test Acc: 0.99479 |\n",
+            "\n",
+            "Train loss: 0.00890 | Train Acc: 1.00000 | Test loss: 0.08936 | Test Acc: 0.99479 |\n",
+            "\n",
+            "Train loss: 0.00731 | Train Acc: 1.00000 | Test loss: 0.08967 | Test Acc: 0.99479 |\n",
+            "\n",
+            "Train loss: 0.00626 | Train Acc: 1.00000 | Test loss: 0.08865 | Test Acc: 0.99479 |\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import matplotlib.pyplot as plt"
+      ],
+      "metadata": {
+        "id": "nO9wqwy-80v2"
+      },
+      "execution_count": 48,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Plot Loss Curve"
+      ],
+      "metadata": {
+        "id": "PVg5tmJ8m5oK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(10,5))\n",
+        "plt.subplot(1,2,1)\n",
+        "plt.plot(results['train_loss'], label= 'train')\n",
+        "plt.plot(results['test_loss'], label= 'test')\n",
+        "plt.title('loss curve for train and test')\n",
+        "plt.legend();\n",
+        "plt.subplot(1,2,2)\n",
+        "plt.plot(results['train_accuracy'], label= 'train')\n",
+        "plt.plot(results['test_accuracy'], label= 'test')\n",
+        "plt.title('accuracy score for train and test')\n",
+        "plt.legend();"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 468
+        },
+        "id": "ruvVWd05808i",
+        "outputId": "edbdc8b3-ab06-4c92-a6db-0bac83fdbf8d"
+      },
+      "execution_count": 49,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 2 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Evaluate Model With New Data"
+      ],
+      "metadata": {
+        "id": "LW-zloram-qS"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "new_data= 'I have been having burning pain anytime i am peeing, what could be the issue?'"
+      ],
+      "metadata": {
+        "id": "udn69qsD-bLp"
+      },
+      "execution_count": 50,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "transformed_new= vectorizer.transform([new_data])\n",
+        "transformed_new= torch.tensor(transformed_new.toarray()).to(torch.float32)\n",
+        "transformed_new.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fuOiFXhL-bvV",
+        "outputId": "80c52b5c-d6ac-4260-a22c-096def5fffbc"
+      },
+      "execution_count": 51,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "torch.Size([1, 1080])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 51
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model.eval()\n",
+        "with torch.inference_mode():\n",
+        "  y_logits=model(transformed_new)\n",
+        "  test_preds= torch.argmax(torch.softmax(y_logits, dim=1), dim=1)\n",
+        "  test_pred= target_dict[test_preds.item()]\n"
+      ],
+      "metadata": {
+        "id": "ZSf_POgB-b8H"
+      },
+      "execution_count": 52,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(f'based on your symptoms, I believe you are having {test_pred}')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oZBcgT0o-lpY",
+        "outputId": "fa552ce8-c8c2-4e0a-c707-7b4e780aaabb"
+      },
+      "execution_count": 53,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "based on your symptoms, I believe you are having urinary tract infection\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Save Model State Dict"
+      ],
+      "metadata": {
+        "id": "MnedOUcbnFQV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from pathlib import Path"
+      ],
+      "metadata": {
+        "id": "f60umq-Z-pC1"
+      },
+      "execution_count": 54,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "target_dir_path = Path('Models')\n",
+        "target_dir_path.mkdir(parents=True,\n",
+        "                      exist_ok=True)\n",
+        "model_path= target_dir_path / 'pretrained_symtom_to_disease_model.pth'\n",
+        "torch.save(obj=model.state_dict(),f= model_path)"
+      ],
+      "metadata": {
+        "id": "Tn467qOG-pRw"
+      },
+      "execution_count": 55,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "target_dict"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "C_jzILVbvNX2",
+        "outputId": "7220d1f1-fbd2-47d7-e588-3996d9dae1a7"
+      },
+      "execution_count": 56,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{0: 'Acne',\n",
+              " 1: 'Arthritis',\n",
+              " 2: 'Bronchial Asthma',\n",
+              " 3: 'Cervical spondylosis',\n",
+              " 4: 'Chicken pox',\n",
+              " 5: 'Common Cold',\n",
+              " 6: 'Dengue',\n",
+              " 7: 'Dimorphic Hemorrhoids',\n",
+              " 8: 'Fungal infection',\n",
+              " 9: 'Hypertension',\n",
+              " 10: 'Impetigo',\n",
+              " 11: 'Jaundice',\n",
+              " 12: 'Malaria',\n",
+              " 13: 'Migraine',\n",
+              " 14: 'Pneumonia',\n",
+              " 15: 'Psoriasis',\n",
+              " 16: 'Typhoid',\n",
+              " 17: 'Varicose Veins',\n",
+              " 18: 'allergy',\n",
+              " 19: 'diabetes',\n",
+              " 20: 'drug reaction',\n",
+              " 21: 'gastroesophageal reflux disease',\n",
+              " 22: 'peptic ulcer disease',\n",
+              " 23: 'urinary tract infection'}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 56
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Deploy Model"
+      ],
+      "metadata": {
+        "id": "d32UI4vnnLSG"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Import and class names setup\n",
+        "import gradio as gr\n",
+        "import os\n",
+        "import torch\n",
+        "import random\n",
+        "import nltk_utils\n",
+        "import pandas as pd\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "import time\n",
+        "\n",
+        "from model import RNN_model\n",
+        "from timeit import default_timer as timer\n",
+        "from typing import Tuple, Dict\n",
+        "\n",
+        "# Import data\n",
+        "df= pd.read_csv('Symptom2Disease.csv')\n",
+        "df.drop('Unnamed: 0', axis= 1, inplace= True)\n",
+        "\n",
+        "# Preprocess data\n",
+        "df.drop_duplicates(inplace= True)\n",
+        "train_data, test_data= train_test_split(df, test_size=0.15, random_state=42 )\n",
+        "\n",
+        "# Setup class names\n",
+        "class_names= {0: 'Acne',\n",
+        "              1: 'Arthritis',\n",
+        "              2: 'Bronchial Asthma',\n",
+        "              3: 'Cervical spondylosis',\n",
+        "              4: 'Chicken pox',\n",
+        "              5: 'Common Cold',\n",
+        "              6: 'Dengue',\n",
+        "              7: 'Dimorphic Hemorrhoids',\n",
+        "              8: 'Fungal infection',\n",
+        "              9: 'Hypertension',\n",
+        "              10: 'Impetigo',\n",
+        "              11: 'Jaundice',\n",
+        "              12: 'Malaria',\n",
+        "              13: 'Migraine',\n",
+        "              14: 'Pneumonia',\n",
+        "              15: 'Psoriasis',\n",
+        "              16: 'Typhoid',\n",
+        "              17: 'Varicose Veins',\n",
+        "              18: 'allergy',\n",
+        "              19: 'diabetes',\n",
+        "              20: 'drug reaction',\n",
+        "              21: 'gastroesophageal reflux disease',\n",
+        "              22: 'peptic ulcer disease',\n",
+        "              23: 'urinary tract infection'\n",
+        "              }\n",
+        "\n",
+        "vectorizer= nltk_utils.vectorizer()\n",
+        "vectorizer.fit(train_data.text)\n",
+        "\n",
+        "\n",
+        "\n",
+        "# Model and transforms preparation\n",
+        "model= RNN_model()\n",
+        "# Load state dict\n",
+        "model.load_state_dict(torch.load(\n",
+        "    f= '/content/Models/pretrained_symtom_to_disease_model.pth',\n",
+        "    map_location= torch.device('cpu')\n",
+        "    )\n",
+        ")\n",
+        "# Disease Advice\n",
+        "disease_advice = {\n",
+        "    'Acne': \"Maintain a proper skincare routine, avoid excessive touching of the affected areas, and consider using over-the-counter topical treatments. If severe, consult a dermatologist.\",\n",
+        "    'Arthritis': \"Stay active with gentle exercises, manage weight, and consider pain-relief strategies like hot/cold therapy. Consult a rheumatologist for tailored guidance.\",\n",
+        "    'Bronchial Asthma': \"Follow prescribed inhaler and medication regimen, avoid triggers like smoke and allergens, and have an asthma action plan. Regular check-ups with a pulmonologist are important.\",\n",
+        "    'Cervical spondylosis': \"Maintain good posture, do neck exercises, and use ergonomic support. Physical therapy and pain management techniques might be helpful.\",\n",
+        "    'Chicken pox': \"Rest, maintain hygiene, and avoid scratching. Consult a doctor for appropriate antiviral treatment.\",\n",
+        "    'Common Cold': \"Get plenty of rest, stay hydrated, and consider over-the-counter remedies for symptom relief. Seek medical attention if symptoms worsen or last long.\",\n",
+        "    'Dengue': \"Stay hydrated, rest, and manage fever with acetaminophen. Seek medical care promptly, as dengue can escalate quickly.\",\n",
+        "    'Dimorphic Hemorrhoids': \"Follow a high-fiber diet, maintain good hygiene, and consider stool softeners. Consult a doctor if symptoms persist.\",\n",
+        "    'Fungal infection': \"Keep the affected area clean and dry, use antifungal creams, and avoid sharing personal items. Consult a dermatologist if it persists.\",\n",
+        "    'Hypertension': \"Follow a balanced diet, exercise regularly, reduce salt intake, and take prescribed medications. Regular check-ups with a healthcare provider are important.\",\n",
+        "    'Impetigo': \"Keep the affected area clean, use prescribed antibiotics, and avoid close contact. Consult a doctor for proper treatment.\",\n",
+        "    'Jaundice': \"Get plenty of rest, maintain hydration, and follow a doctor's advice for diet and medications. Regular monitoring is important.\",\n",
+        "    'Malaria': \"Take prescribed antimalarial medications, rest, and manage fever. Seek medical attention for severe cases.\",\n",
+        "    'Migraine': \"Identify triggers, manage stress, and consider pain-relief medications. Consult a neurologist for personalized management.\",\n",
+        "    'Pneumonia': \"Follow prescribed antibiotics, rest, stay hydrated, and monitor symptoms. Seek immediate medical attention for severe cases.\",\n",
+        "    'Psoriasis': \"Moisturize, use prescribed creams, and avoid triggers. Consult a dermatologist for effective management.\",\n",
+        "    'Typhoid': \"Take prescribed antibiotics, rest, and stay hydrated. Dietary precautions are important. Consult a doctor for proper treatment.\",\n",
+        "    'Varicose Veins': \"Elevate legs, exercise regularly, and wear compression stockings. Consult a vascular specialist for evaluation and treatment options.\",\n",
+        "    'allergy': \"Identify triggers, manage exposure, and consider antihistamines. Consult an allergist for comprehensive management.\",\n",
+        "    'diabetes': \"Follow a balanced diet, exercise, monitor blood sugar levels, and take prescribed medications. Regular visits to an endocrinologist are essential.\",\n",
+        "    'drug reaction': \"Discontinue the suspected medication, seek medical attention if symptoms are severe, and inform healthcare providers about the reaction.\",\n",
+        "    'gastroesophageal reflux disease': \"Follow dietary changes, avoid large meals, and consider medications. Consult a doctor for personalized management.\",\n",
+        "    'peptic ulcer disease': \"Avoid spicy and acidic foods, take prescribed medications, and manage stress. Consult a gastroenterologist for guidance.\",\n",
+        "    'urinary tract infection': \"Stay hydrated, take prescribed antibiotics, and maintain good hygiene. Consult a doctor for appropriate treatment.\"\n",
+        "}\n",
+        "\n",
+        "howto= \"\"\"Welcome to the <b>Medical Chatbot</b>, powered by Gradio.\n",
+        "Currently, the chatbot can WELCOME YOU, PREDICT DISEASE based on your symptoms and SUGGEST POSSIBLE SOLUTIONS AND RECOMENDATIONS, and BID YOU FAREWELL.\n",
+        "<br><br>\n",
+        "Here's a quick guide to get you started:<br><br>\n",
+        "<b>How to Start:</b> Simply type your messages in the textbox to chat with the Chatbot and press enter!<br><br>\n",
+        "The bot will respond based on the best possible answers to your messages. For now, let's keep it SIMPLE as I'm working hard to enhance its capabilities in the future.\n",
+        "\n",
+        "\"\"\"\n",
+        "\n",
+        "\n",
+        "# Create the gradio demo\n",
+        "with gr.Blocks(css = \"\"\"#col_container { margin-left: auto; margin-right: auto;} #chatbot {height: 520px; overflow: auto;}\"\"\") as demo:\n",
+        "  gr.HTML('<h1 align=\"center\">Medical Chatbot: Your Virtual Health Guide 🌟🏥🤖\"</h1>')\n",
+        "  gr.HTML('<h3 align=\"center\">To know more about this project click, <a href=\"https://github.com/Monsurat-Onabajo/Medical_chatbot\" target=\"_blank\">Here</a>')\n",
+        "  with gr.Accordion(\"Follow these Steps to use the Gradio WebUI\", open=True):\n",
+        "      gr.HTML(howto)\n",
+        "  chatbot = gr.Chatbot()\n",
+        "  msg = gr.Textbox()\n",
+        "  clear = gr.ClearButton([msg, chatbot])\n",
+        "\n",
+        "  def respond(message, chat_history):\n",
+        "    # Random greetings in list format\n",
+        "    greetings = [\n",
+        "        \"hello!\",'hello', 'hii !', 'hi', \"hi there!\",  \"hi there!\", \"heyy\", 'good morning', 'good afternoon', 'good evening'\n",
+        "        \"hey\", \"how are you\", \"how are you?\", \"how is it going\", \"how is it going?\",\n",
+        "        \"what's up?\", \"how are you?\",\n",
+        "        \"hey, how are you?\", \"what is popping\"\n",
+        "        \"good to see you!\", \"howdy!\",\n",
+        "        \"hi, nice to meet you.\", \"hiya!\",\n",
+        "        \"hi\", \"hi, what's new?\",\n",
+        "        \"hey, how's your day?\", \"hi, how have you been?\", \"greetings\",\n",
+        "        ]\n",
+        "    # Random Greetings responses\n",
+        "    responses = [\n",
+        "        \"Thank you for using our medical chatbot. Please provide the symptoms you're experiencing, and I'll do my best to predict the possible disease.\",\n",
+        "        \"Hello! I'm here to help you with medical predictions based on your symptoms. Please describe your symptoms in as much detail as possible.\",\n",
+        "        \"Greetings! I am a specialized medical chatbot trained to predict potential diseases based on the symptoms you provide. Kindly list your symptoms explicitly.\",\n",
+        "        \"Welcome to the medical chatbot. To assist you accurately, please share your symptoms in explicit detail.\",\n",
+        "        \"Hi there! I'm a medical chatbot specialized in analyzing symptoms to suggest possible diseases. Please provide your symptoms explicitly.\",\n",
+        "        \"Hey! I'm your medical chatbot. Describe your symptoms with as much detail as you can, and I'll generate potential disease predictions.\",\n",
+        "        \"How can I assist you today? I'm a medical chatbot trained to predict diseases based on symptoms. Please be explicit while describing your symptoms.\",\n",
+        "        \"Hello! I'm a medical chatbot capable of predicting diseases based on the symptoms you provide. Your explicit symptom description will help me assist you better.\",\n",
+        "        \"Greetings! I'm here to help with medical predictions. Describe your symptoms explicitly, and I'll offer insights into potential diseases.\",\n",
+        "        \"Hi, I'm the medical chatbot. I've been trained to predict diseases from symptoms. The more explicit you are about your symptoms, the better I can assist you.\",\n",
+        "        \"Hi, I specialize in medical predictions based on symptoms. Kindly provide detailed symptoms for accurate disease predictions.\",\n",
+        "        \"Hello! I'm a medical chatbot with expertise in predicting diseases from symptoms. Please describe your symptoms explicitly to receive accurate insights.\",\n",
+        "        ]\n",
+        "    # Random goodbyes\n",
+        "    goodbyes = [\n",
+        "        \"farewell!\",'bye', 'goodbye','good-bye', 'good bye', 'bye', 'thank you', 'later', \"take care!\",\n",
+        "        \"see you later!\", 'see you', 'see ya', 'see-you', 'thanks', 'thank', 'bye bye', 'byebye'\n",
+        "        \"catch you on the flip side!\", \"adios!\",\n",
+        "        \"goodbye for now!\", \"till we meet again!\",\n",
+        "        \"so long!\", \"hasta la vista!\",\n",
+        "        \"bye-bye!\", \"keep in touch!\",\n",
+        "        \"toodles!\", \"ciao!\",\n",
+        "        \"later, gator!\", \"stay safe and goodbye!\",\n",
+        "        \"peace out!\", \"until next time!\", \"off I go!\",\n",
+        "        ]\n",
+        "    # Random Goodbyes responses\n",
+        "    goodbye_replies = [\n",
+        "        \"Take care of yourself! If you have more questions, don't hesitate to reach out.\",\n",
+        "        \"Stay well! Remember, I'm here if you need further medical advice.\",\n",
+        "        \"Goodbye for now! Don't hesitate to return if you need more information in the future.\",\n",
+        "        \"Wishing you good health ahead! Feel free to come back if you have more concerns.\",\n",
+        "        \"Farewell! If you have more symptoms or questions, don't hesitate to consult again.\",\n",
+        "        \"Take care and stay informed about your health. Feel free to chat anytime.\",\n",
+        "        \"Bye for now! Remember, your well-being is a priority. Don't hesitate to ask if needed.\",\n",
+        "        \"Have a great day ahead! If you need medical guidance later on, I'll be here.\",\n",
+        "        \"Stay well and take it easy! Reach out if you need more medical insights.\",\n",
+        "        \"Until next time! Prioritize your health and reach out if you need assistance.\",\n",
+        "        \"Goodbye! Your health matters. Feel free to return if you have more health-related queries.\",\n",
+        "        \"Stay healthy and stay curious about your health! If you need more info, just ask.\",\n",
+        "        \"Wishing you wellness on your journey! If you have more questions, I'm here to help.\",\n",
+        "        \"Take care and remember, your health is important. Don't hesitate to reach out if needed.\",\n",
+        "        \"Goodbye for now! Stay informed and feel free to consult if you require medical advice.\",\n",
+        "        \"Stay well and stay proactive about your health! If you have more queries, feel free to ask.\",\n",
+        "        \"Farewell! Remember, I'm here whenever you need reliable medical information.\",\n",
+        "        \"Bye for now! Stay vigilant about your health and don't hesitate to return if necessary.\",\n",
+        "        \"Take care and keep your well-being a priority! Reach out if you have more health questions.\",\n",
+        "        \"Wishing you good health ahead! Don't hesitate to chat if you need medical insights.\",\n",
+        "        \"Goodbye! Stay well and remember, I'm here to assist you with medical queries.\",\n",
+        "    ]\n",
+        "\n",
+        "    # Create couple of if-else statements to capture/mimick peoples's Interaction\n",
+        "    if message.lower() in greetings:\n",
+        "      bot_message= random.choice(responses)\n",
+        "    elif message.lower() in goodbyes:\n",
+        "      bot_message= random.choice(goodbye_replies)\n",
+        "    else:\n",
+        "      transform_text= vectorizer.transform([message])\n",
+        "      transform_text= torch.tensor(transform_text.toarray()).to(torch.float32)\n",
+        "      model.eval()\n",
+        "      with torch.inference_mode():\n",
+        "        y_logits=model(transform_text)\n",
+        "        pred_prob= torch.argmax(torch.softmax(y_logits, dim=1), dim=1)\n",
+        "\n",
+        "      test_pred= class_names[pred_prob.item()]\n",
+        "      bot_message = f' Based on your symptoms, I believe you are having {test_pred} and I would advice you {disease_advice[test_pred]}'\n",
+        "    chat_history.append((message, bot_message))\n",
+        "    time.sleep(2)\n",
+        "    return \"\", chat_history\n",
+        "\n",
+        "  msg.submit(respond, [msg, chatbot], [msg, chatbot])\n",
+        "# Launch the demo\n",
+        "demo.launch()\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "otWWMGaZLimX",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 712
+        },
+        "outputId": "f46fbf9a-cc01-467c-f0ed-29d745091c20"
+      },
+      "execution_count": 68,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py:528: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-packages/sklearn/feature_extraction/text.py:409: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens [\"'d\", \"'s\", 'abov', 'ani', 'becaus', 'befor', 'could', 'doe', 'dure', 'might', 'must', \"n't\", 'need', 'onc', 'onli', 'ourselv', 'sha', 'themselv', 'veri', 'whi', 'wo', 'would', 'yourselv'] not in stop_words.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
+            "Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
+            "\n",
+            "To create a public link, set `share=True` in `launch()`.\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "(async (port, path, width, height, cache, element) => {\n",
+              "                        if (!google.colab.kernel.accessAllowed && !cache) {\n",
+              "                            return;\n",
+              "                        }\n",
+              "                        element.appendChild(document.createTextNode(''));\n",
+              "                        const url = await google.colab.kernel.proxyPort(port, {cache});\n",
+              "\n",
+              "                        const external_link = document.createElement('div');\n",
+              "                        external_link.innerHTML = `\n",
+              "                            <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
+              "                                Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n",
+              "                                    https://localhost:${port}${path}\n",
+              "                                </a>\n",
+              "                            </div>\n",
+              "                        `;\n",
+              "                        element.appendChild(external_link);\n",
+              "\n",
+              "                        const iframe = document.createElement('iframe');\n",
+              "                        iframe.src = new URL(path, url).toString();\n",
+              "                        iframe.height = height;\n",
+              "                        iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
+              "                        iframe.width = width;\n",
+              "                        iframe.style.border = 0;\n",
+              "                        element.appendChild(iframe);\n",
+              "                    })(7871, \"/\", \"100%\", 500, false, window.element)"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": []
+          },
+          "metadata": {},
+          "execution_count": 68
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "FsgtBKB0v-ub"
+      },
+      "execution_count": 57,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file