Disease_prediction_random_forest.ipynb

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   "source": [
    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/gianlab/machine-learning-notebooks/blob/main/Disease_prediction_random_forest.ipynb\">\n",
    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
    "</a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3820a4d7",
   "metadata": {},
   "source": [
    "## Install packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "UNeEIzUWNvPy",
   "metadata": {
    "colab": {
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "Collecting skops\n",
      "  Downloading skops-0.9.0-py3-none-any.whl (120 kB)\n",
      "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/120.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
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      "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.24->skops) (1.25.2)\n",
      "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.24->skops) (1.11.4)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.24->skops) (1.3.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.24->skops) (3.3.0)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->skops) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->skops) (3.6)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->skops) (2.0.7)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->skops) (2024.2.2)\n",
      "Installing collected packages: skops\n",
      "Successfully installed skops-0.9.0\n"
     ]
    }
   ],
   "source": [
    "!pip install skops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94-OUCJASaIo",
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    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 361
    },
    "id": "94-OUCJASaIo",
    "outputId": "022ebb5d-3b77-4c8a-82cf-1e8d2d53742a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting scikit-learn==1.3.2\n",
      "  Downloading scikit_learn-1.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (10.8 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.8/10.8 MB\u001b[0m \u001b[31m51.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: numpy<2.0,>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.3.2) (1.25.2)\n",
      "Requirement already satisfied: scipy>=1.5.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.3.2) (1.11.4)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.3.2) (1.3.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn==1.3.2) (3.3.0)\n",
      "Installing collected packages: scikit-learn\n",
      "  Attempting uninstall: scikit-learn\n",
      "    Found existing installation: scikit-learn 1.2.2\n",
      "    Uninstalling scikit-learn-1.2.2:\n",
      "      Successfully uninstalled scikit-learn-1.2.2\n",
      "Successfully installed scikit-learn-1.3.2\n"
     ]
    },
    {
     "data": {
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        "packages": [
         "sklearn"
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   ],
   "source": [
    "!pip install scikit-learn==1.3.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "U68ghxxLVSVK",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "U68ghxxLVSVK",
    "outputId": "c6d93fd9-43a2-41b4-c05b-6a08c99e7fca"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting numpy==1.26.2\n",
      "  Downloading numpy-1.26.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.2/18.2 MB\u001b[0m \u001b[31m57.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: numpy\n",
      "  Attempting uninstall: numpy\n",
      "    Found existing installation: numpy 1.25.2\n",
      "    Uninstalling numpy-1.25.2:\n",
      "      Successfully uninstalled numpy-1.25.2\n",
      "Successfully installed numpy-1.26.2\n"
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   ],
   "source": [
    "!pip install numpy==1.26.2"
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  },
  {
   "cell_type": "markdown",
   "id": "80eb9c66",
   "metadata": {
    "id": "80eb9c66"
   },
   "source": [
    "## Load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b34220dc",
   "metadata": {
    "id": "b34220dc"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle\n",
    "from skops import hub_utils"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c433bf6",
   "metadata": {
    "id": "7c433bf6"
   },
   "source": [
    "## Model download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5900daff",
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    "id": "5900daff",
    "outputId": "01f3031d-eea9-4d29-ac65-a10e14ff21a2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
      "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
      "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
      "You will be able to reuse this secret in all of your notebooks.\n",
      "Please note that authentication is recommended but still optional to access public models or datasets.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "27772751d0b9471bb95b6ba988e9a56f",
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       "version_minor": 0
      },
      "text/plain": [
       "Fetching 8 files:   0%|          | 0/8 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "version_major": 2,
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      "text/plain": [
       "Symptom-severity.csv:   0%|          | 0.00/2.33k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "repo_id = \"gianlab/random-forest-model-disease-symptom-prediction\"\n",
    "download_repo = \"downloaded-model\"\n",
    "hub_utils.download(repo_id=repo_id, dst=download_repo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f8eedef",
   "metadata": {
    "id": "3f8eedef"
   },
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('downloaded-model/Symptom-severity.csv')\n",
    "df1['Symptom'] = df1['Symptom'].str.replace('_',' ')\n",
    "discrp = pd.read_csv(\"downloaded-model/symptom_Description.csv\")\n",
    "ektra7at = pd.read_csv(\"downloaded-model/symptom_precaution.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "627b14b3",
   "metadata": {
    "id": "627b14b3"
   },
   "outputs": [],
   "source": [
    "def predd(x,S1,S2,S3,S4,S5,S6,S7,S8,S9,S10,S11,S12,S13,S14,S15,S16,S17):\n",
    "    psymptoms = [S1,S2,S3,S4,S5,S6,S7,S8,S9,S10,S11,S12,S13,S14,S15,S16,S17]\n",
    "    #print(psymptoms)\n",
    "    a = np.array(df1[\"Symptom\"])\n",
    "    b = np.array(df1[\"weight\"])\n",
    "    for j in range(len(psymptoms)):\n",
    "        for k in range(len(a)):\n",
    "            if psymptoms[j]==a[k]:\n",
    "                psymptoms[j]=b[k]\n",
    "    psy = [psymptoms]\n",
    "    pred2 = x.predict(psy)\n",
    "    disp= discrp[discrp['Disease']==pred2[0]]\n",
    "    disp = disp.values[0][1]\n",
    "    recomnd = ektra7at[ektra7at['Disease']==pred2[0]]\n",
    "    c=np.where(ektra7at['Disease']==pred2[0])[0][0]\n",
    "    precuation_list=[]\n",
    "    for i in range(1,len(ektra7at.iloc[c])):\n",
    "          precuation_list.append(ektra7at.iloc[c,i])\n",
    "    print(\"The Disease Name: \",pred2[0])\n",
    "    print(\"The Disease Discription: \",disp)\n",
    "    print(\"Recommended Things to do at home: \")\n",
    "    for i in precuation_list:\n",
    "        print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a4bd94a",
   "metadata": {
    "id": "8a4bd94a"
   },
   "source": [
    "## Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "875109fd",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "875109fd",
    "outputId": "115be0b4-d19a-4e09-9582-ae23ae9c1437"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Disease Name:  Paralysis (brain hemorrhage)\n",
      "The Disease Discription:  Intracerebral hemorrhage (ICH) is when blood suddenly bursts into brain tissue, causing damage to your brain. Symptoms usually appear suddenly during ICH. They include headache, weakness, confusion, and paralysis, particularly on one side of your body.\n",
      "Recommended Things to do at home: \n",
      "massage\n",
      "eat healthy\n",
      "exercise\n",
      "consult doctor\n"
     ]
    }
   ],
   "source": [
    "pickled_model = pickle.load(open('downloaded-model/skops-iw9h_jza.pkl', 'rb'))\n",
    "predd(pickled_model,\"stomach pain\",\"chills\",\"nodal skin eruptions\",\"muscle weakness\",0,0,0,0,0,0,0,0,0,0,0,0,0)"
   ]
  }
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