{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QrkRO4oMhoGB",
        "outputId": "72e262f6-cbd9-4297-fd6d-9765203f6350"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using device: cuda\n"
          ]
        }
      ],
      "source": [
        "# Cell 1: Preamble and Setup\n",
        "# ---\n",
        "# # Drug Prediction and Polypharmacy System with MobileBERT\n",
        "# # Optimized for Google Colab\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch.nn as nn\n",
        "from transformers import AutoTokenizer, AutoModel, get_linear_schedule_with_warmup\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MultiLabelBinarizer, LabelEncoder\n",
        "from sklearn.metrics import f1_score, accuracy_score\n",
        "from tqdm.notebook import tqdm\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pickle\n",
        "import os\n",
        "import re\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Check for GPU\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(f\"Using device: {device}\")\n",
        "\n",
        "# Set seeds for reproducibility\n",
        "def set_seed(seed=42):\n",
        "    np.random.seed(seed)\n",
        "    torch.manual_seed(seed)\n",
        "    torch.cuda.manual_seed_all(seed)\n",
        "    torch.backends.cudnn.deterministic = True\n",
        "    torch.backends.cudnn.benchmark = False\n",
        "\n",
        "set_seed()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LwHSodPNjowc",
        "outputId": "2ba36d85-0e26-46c5-99fc-5300ae218421"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading and preprocessing data...\n",
            "Loaded dataset with 15000 patients and 32 features\n",
            "\n",
            "Data Overview:\n",
            "   Patient_ID  Age  Gender                                          Symptoms  \\\n",
            "0           1   18   Other  Chest pain; Cough; Shortness of breath; Headache   \n",
            "1           2   56   Other           Headache; Dizziness; Fatigue; Back pain   \n",
            "2           3   83  Female             Chest pain; Fever; Cough; Muscle pain   \n",
            "3           4   57  Female            Dizziness; Headache; Nausea; Back pain   \n",
            "4           5   45    Male             Runny nose; Fever; Cough; Sore throat   \n",
            "\n",
            "                                         medications polypharmacy_risk  \n",
            "0  [Albuterol Inhaler, Budesonide Inhaler, Montel...     Low to Medium  \n",
            "1      [Amlodipine, Lisinopril, Hydrochlorothiazide]     Low to Medium  \n",
            "2      [Amoxicillin, Guaifenesin, Albuterol Inhaler]    Medium to High  \n",
            "3           [Sumatriptan, Ibuprofen, Metoclopramide]     Low to Medium  \n",
            "4  [Acetaminophen, Dextromethorphan, Saline Nasal...     Low to Medium  \n",
            "\n",
            "Enhanced Text Representation Example:\n",
            "Patient age 18, gender Other, blood group B+, weight 74.6kg. SYMPTOMS: Chest pain; Cough; Shortness of breath; Headache. SEVERITY: Chest pain:3; Cough:3; Shortness of breath:2; Headache:4. \n"
          ]
        }
      ],
      "source": [
        "# Cell 2: Data Loading and Preprocessing with Enhanced Text Representation\n",
        "# ---\n",
        "# ## 1. Data Loading and Preprocessing\n",
        "print(\"Loading and preprocessing data...\")\n",
        "\n",
        "def load_data(file_path):\n",
        "    \"\"\"Load and preprocess the patient dataset\"\"\"\n",
        "    df = pd.read_csv(file_path)\n",
        "\n",
        "    # Clean medicine columns\n",
        "    medicine_cols = ['Medicine_1', 'Medicine_2', 'Medicine_3']\n",
        "    dosage_cols = ['Dosage_1', 'Dosage_2', 'Dosage_3']\n",
        "    frequency_cols = ['Frequency_1', 'Frequency_2', 'Frequency_3']\n",
        "    instruction_cols = ['Instruction_1', 'Instruction_2', 'Instruction_3']\n",
        "    duration_cols = ['Duration_1', 'Duration_2', 'Duration_3']\n",
        "\n",
        "    for col in medicine_cols + dosage_cols + frequency_cols + instruction_cols + duration_cols:\n",
        "        if col in df.columns:\n",
        "            df[col] = df[col].fillna('None')  # Fill NaN values\n",
        "\n",
        "    # Create medication list for each patient\n",
        "    df['medications'] = df.apply(\n",
        "        lambda x: [med for med in [x['Medicine_1'], x['Medicine_2'], x['Medicine_3']]\n",
        "                  if med != 'None' and not pd.isna(med)],\n",
        "        axis=1\n",
        "    )\n",
        "\n",
        "    # Process polypharmacy risk\n",
        "    df['polypharmacy_risk'] = df['Polypharmacy_Risk'].fillna('Unknown')\n",
        "\n",
        "    # ENHANCED: Create better structured text input with clinical emphasis\n",
        "    df['text_input'] = df.apply(\n",
        "        lambda x: f\"Patient age {x['Age']}, gender {x['Gender']}, blood group {x['Blood_Group']}, weight {x['Weight_kg']}kg. \" +\n",
        "                f\"SYMPTOMS: {x['Symptoms']}. \" +\n",
        "                f\"SEVERITY: {x['Severity_Scores']}. \" +\n",
        "                (f\"HISTORY: {x['Medical_History']}. \" if 'Medical_History' in df.columns and not pd.isna(x['Medical_History']) else \"\") +\n",
        "                (f\"ALLERGIES: {x['Allergies']}. \" if 'Allergies' in df.columns and not pd.isna(x['Allergies']) else \"\"),\n",
        "        axis=1\n",
        "    )\n",
        "\n",
        "    return df\n",
        "\n",
        "# Load dataset\n",
        "file_path = '/content/drive/MyDrive/Colab Notebooks/large_patient_dataset.csv'  # Update with your file path\n",
        "df = load_data(file_path)\n",
        "print(f\"Loaded dataset with {len(df)} patients and {df.columns.shape[0]} features\")\n",
        "\n",
        "# Display data summary\n",
        "print(\"\\nData Overview:\")\n",
        "print(df[['Patient_ID', 'Age', 'Gender', 'Symptoms', 'medications', 'polypharmacy_risk']].head())\n",
        "\n",
        "# NEW: Show an example of the enhanced text representation\n",
        "print(\"\\nEnhanced Text Representation Example:\")\n",
        "print(df['text_input'].iloc[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BklWTUrrjr4E",
        "outputId": "a54214fc-4d29-4a02-8338-b6b2b4061d82"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Total unique medications: 27\n",
            "Medications sample: ['Acetaminophen' 'Albuterol Inhaler' 'Amlodipine' 'Amoxicillin'\n",
            " 'Budesonide Inhaler']...\n",
            "\n",
            "Polypharmacy risk categories: ['Low to Medium' 'Medium to High' 'Unknown']\n",
            "\n",
            "Total unique diseases: 5\n",
            "Disease sample: ['Asthma' 'Bronchitis' 'Hypertension' 'Migraine'\n",
            " 'Upper Respiratory Infection']...\n",
            "\n",
            "--- Class Balance Analysis ---\n",
            "Medication distribution summary:\n",
            "  - Total medications: 27\n",
            "  - Medications with >0 samples: 27\n",
            "  - Most common medication: Shake well, 3601 samples\n",
            "  - Least common medication: Amlodipine, 958 samples\n",
            "  - Zero-sample medications: 0\n",
            "\n",
            "Polypharmacy risk distribution:\n",
            "  - Low to Medium: 3491 samples (23.3%)\n",
            "  - Medium to High: 1509 samples (10.1%)\n",
            "  - Unknown: 10000 samples (66.7%)\n",
            "\n",
            "Disease distribution (top 10):\n",
            "  - Upper Respiratory Infection: 3062 samples (20.4%)\n",
            "  - Bronchitis: 3057 samples (20.4%)\n",
            "  - Asthma: 3004 samples (20.0%)\n",
            "  - Migraine: 2992 samples (19.9%)\n",
            "  - Hypertension: 2885 samples (19.2%)\n"
          ]
        }
      ],
      "source": [
        "# Cell 3: Label Encoding with Class Balance Analysis\n",
        "# ---\n",
        "# ## 2. Prepare labels for multi-task learning\n",
        "# Medication encoding (multi-label)\n",
        "mlb = MultiLabelBinarizer()\n",
        "medication_labels = mlb.fit_transform(df['medications'])\n",
        "medication_classes = mlb.classes_\n",
        "num_medication_classes = len(medication_classes)\n",
        "print(f\"\\nTotal unique medications: {num_medication_classes}\")\n",
        "print(f\"Medications sample: {medication_classes[:5]}...\")\n",
        "\n",
        "# Polypharmacy risk encoding (multi-class)\n",
        "le_risk = LabelEncoder()\n",
        "polypharmacy_labels = le_risk.fit_transform(df['polypharmacy_risk'])\n",
        "polypharmacy_classes = le_risk.classes_\n",
        "num_polypharmacy_classes = len(polypharmacy_classes)\n",
        "print(f\"\\nPolypharmacy risk categories: {polypharmacy_classes}\")\n",
        "\n",
        "# Disease encoding (multi-class)\n",
        "le_disease = LabelEncoder()\n",
        "disease_labels = le_disease.fit_transform(df['Predicted_Disease'])\n",
        "disease_classes = le_disease.classes_\n",
        "num_disease_classes = len(disease_classes)\n",
        "print(f\"\\nTotal unique diseases: {num_disease_classes}\")\n",
        "print(f\"Disease sample: {disease_classes[:5]}...\")\n",
        "\n",
        "# Create dictionaries for other text-based outputs\n",
        "disease_causes_dict = dict(zip(df['Predicted_Disease'], df['Disease_Causes']))\n",
        "disease_prevention_dict = dict(zip(df['Predicted_Disease'], df['Disease_Prevention']))\n",
        "polypharmacy_recommendation_dict = dict(zip(df['polypharmacy_risk'], df['Polypharmacy_Recommendation']))\n",
        "\n",
        "# For personalized health tips, build a model or use lookup based on disease + demographics\n",
        "health_tips_dict = {}\n",
        "for index, row in df.iterrows():\n",
        "    key = (row['Predicted_Disease'], row['Age'] // 10 * 10, row['Gender'])  # Age binned by decade\n",
        "    if key in health_tips_dict:\n",
        "        health_tips_dict[key].append(row['Personalized_Health_Tips'])\n",
        "    else:\n",
        "        health_tips_dict[key] = [row['Personalized_Health_Tips']]\n",
        "\n",
        "# Convert lists to most common value\n",
        "for key in health_tips_dict:\n",
        "    health_tips_dict[key] = max(set(health_tips_dict[key]), key=health_tips_dict[key].count)\n",
        "\n",
        "# NEW: Data Balance Analysis\n",
        "print(\"\\n--- Class Balance Analysis ---\")\n",
        "\n",
        "# Check medication distribution\n",
        "med_counts = np.sum(medication_labels, axis=0)\n",
        "print(f\"Medication distribution summary:\")\n",
        "print(f\"  - Total medications: {num_medication_classes}\")\n",
        "print(f\"  - Medications with >0 samples: {np.sum(med_counts > 0)}\")\n",
        "print(f\"  - Most common medication: {medication_classes[np.argmax(med_counts)]}, {np.max(med_counts)} samples\")\n",
        "print(f\"  - Least common medication: {medication_classes[np.argmin(med_counts)]}, {np.min(med_counts)} samples\")\n",
        "print(f\"  - Zero-sample medications: {np.sum(med_counts == 0)}\")\n",
        "\n",
        "# Check polypharmacy risk distribution\n",
        "risk_counts = np.bincount(polypharmacy_labels)\n",
        "print(f\"\\nPolypharmacy risk distribution:\")\n",
        "for i, risk in enumerate(polypharmacy_classes):\n",
        "    print(f\"  - {risk}: {risk_counts[i]} samples ({risk_counts[i]/len(polypharmacy_labels)*100:.1f}%)\")\n",
        "\n",
        "# Check disease distribution\n",
        "disease_counts = np.bincount(disease_labels)\n",
        "print(f\"\\nDisease distribution (top 10):\")\n",
        "top_diseases = np.argsort(disease_counts)[::-1][:10]\n",
        "for i in top_diseases:\n",
        "    print(f\"  - {disease_classes[i]}: {disease_counts[i]} samples ({disease_counts[i]/len(disease_labels)*100:.1f}%)\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZvHweZrXjw2s",
        "outputId": "6c2d7d01-55d3-4553-fbae-1e9c10655a92"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Data split with stratification by disease\n",
            "Training samples: 12000, Test samples: 3000\n"
          ]
        }
      ],
      "source": [
        "# Cell 4: Data Splitting\n",
        "# ---\n",
        "# ## 3. Split data\n",
        "# For small datasets, we'll use a larger training portion\n",
        "if len(df) < 100:\n",
        "    test_size = 0.1  # Use 90% for training with small datasets\n",
        "else:\n",
        "    test_size = 0.2  # Default 80/20 split for larger datasets\n",
        "\n",
        "try:\n",
        "    # Try stratified split based on disease\n",
        "    X_train, X_test, med_train, med_test, poly_train, poly_test, disease_train, disease_test = train_test_split(\n",
        "        df['text_input'].tolist(),\n",
        "        medication_labels,\n",
        "        polypharmacy_labels,\n",
        "        disease_labels,\n",
        "        test_size=test_size,\n",
        "        random_state=42,\n",
        "        stratify=df['Predicted_Disease']\n",
        "    )\n",
        "    print(f\"\\nData split with stratification by disease\")\n",
        "except ValueError:\n",
        "    # Fallback to regular split if stratification fails (e.g., too few samples per class)\n",
        "    X_train, X_test, med_train, med_test, poly_train, poly_test, disease_train, disease_test = train_test_split(\n",
        "        df['text_input'].tolist(),\n",
        "        medication_labels,\n",
        "        polypharmacy_labels,\n",
        "        disease_labels,\n",
        "        test_size=test_size,\n",
        "        random_state=42\n",
        "    )\n",
        "    print(f\"\\nData split without stratification (regular split)\")\n",
        "\n",
        "print(f\"Training samples: {len(X_train)}, Test samples: {len(X_test)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "pkIEuHH6jzDU"
      },
      "outputs": [],
      "source": [
        "# Cell 5: Dataset Class\n",
        "# ---\n",
        "# ## 4. Create dataset class for PyTorch\n",
        "class PatientDataset(Dataset):\n",
        "    def __init__(self, texts, medication_labels, polypharmacy_labels, disease_labels, tokenizer, max_length=128):\n",
        "        self.texts = texts\n",
        "        self.medication_labels = medication_labels\n",
        "        self.polypharmacy_labels = polypharmacy_labels\n",
        "        self.disease_labels = disease_labels\n",
        "        self.tokenizer = tokenizer\n",
        "        self.max_length = max_length\n",
        "\n",
        "    def __len__(self):\n",
        "        return len(self.texts)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        text = str(self.texts[idx])\n",
        "        medication_label = self.medication_labels[idx]\n",
        "        polypharmacy_label = self.polypharmacy_labels[idx]\n",
        "        disease_label = self.disease_labels[idx]\n",
        "\n",
        "        encoding = self.tokenizer(\n",
        "            text,\n",
        "            add_special_tokens=True,\n",
        "            max_length=self.max_length,\n",
        "            padding='max_length',\n",
        "            truncation=True,\n",
        "            return_tensors='pt'\n",
        "        )\n",
        "\n",
        "        return {\n",
        "            'input_ids': encoding['input_ids'].flatten(),\n",
        "            'attention_mask': encoding['attention_mask'].flatten(),\n",
        "            'medication_labels': torch.FloatTensor(medication_label),\n",
        "            'polypharmacy_label': torch.tensor(polypharmacy_label, dtype=torch.long),\n",
        "            'disease_label': torch.tensor(disease_label, dtype=torch.long)\n",
        "        }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "igcRbrrMj0xk"
      },
      "outputs": [],
      "source": [
        "# Cell 6: Enhanced Model Architecture\n",
        "# ---\n",
        "# ## 5. Create Enhanced Multi-Task Medical Model\n",
        "class EnhancedMedicationModel(nn.Module):\n",
        "    def __init__(self, model_name, num_medications, num_polypharmacy_classes, num_disease_classes, dropout_rate=0.3):\n",
        "        super().__init__()\n",
        "        self.bert = AutoModel.from_pretrained(model_name)\n",
        "        self.dropout = nn.Dropout(dropout_rate)\n",
        "        hidden_size = self.bert.config.hidden_size\n",
        "\n",
        "        # Common representation layer\n",
        "        self.common_dense = nn.Linear(hidden_size, hidden_size)\n",
        "\n",
        "        # Task-specific layers with increased complexity\n",
        "        # Medication prediction head (multi-label)\n",
        "        self.medication_classifier = nn.Sequential(\n",
        "            nn.Linear(hidden_size, hidden_size//2),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(dropout_rate),\n",
        "            nn.Linear(hidden_size//2, num_medications)\n",
        "        )\n",
        "\n",
        "        # Polypharmacy risk head (multi-class)\n",
        "        self.polypharmacy_classifier = nn.Sequential(\n",
        "            nn.Linear(hidden_size, hidden_size//2),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(dropout_rate),\n",
        "            nn.Linear(hidden_size//2, num_polypharmacy_classes)\n",
        "        )\n",
        "\n",
        "        # Disease prediction head (multi-class)\n",
        "        self.disease_classifier = nn.Sequential(\n",
        "            nn.Linear(hidden_size, hidden_size//2),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(dropout_rate),\n",
        "            nn.Linear(hidden_size//2, num_disease_classes)\n",
        "        )\n",
        "\n",
        "        # Apply weight initialization\n",
        "        self._init_weights()\n",
        "\n",
        "    def _init_weights(self):\n",
        "        # Initialize weights for better convergence\n",
        "        for module in [self.medication_classifier, self.polypharmacy_classifier,\n",
        "                      self.disease_classifier, self.common_dense]:\n",
        "            if isinstance(module, nn.Sequential):\n",
        "                for layer in module:\n",
        "                    if isinstance(layer, nn.Linear):\n",
        "                        nn.init.xavier_normal_(layer.weight)\n",
        "                        nn.init.zeros_(layer.bias)\n",
        "            elif isinstance(module, nn.Linear):\n",
        "                nn.init.xavier_normal_(module.weight)\n",
        "                nn.init.zeros_(module.bias)  # Fixed: layer.bias -> module.bias\n",
        "\n",
        "    def forward(self, input_ids, attention_mask):\n",
        "        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)\n",
        "        pooled_output = outputs.last_hidden_state[:, 0, :]  # CLS token\n",
        "        pooled_output = self.dropout(pooled_output)\n",
        "\n",
        "        # Common representation\n",
        "        common_features = torch.relu(self.common_dense(pooled_output))\n",
        "\n",
        "        medication_logits = self.medication_classifier(common_features)\n",
        "        polypharmacy_logits = self.polypharmacy_classifier(common_features)\n",
        "        disease_logits = self.disease_classifier(common_features)\n",
        "\n",
        "        return medication_logits, polypharmacy_logits, disease_logits"
      ]
    },
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      "cell_type": "code",
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        "id": "1y7vjqtlj3RE",
        "outputId": "9f0d5675-2c94-4483-f232-583e561b6b5b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Initializing tokenizer and model...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
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          "metadata": {}
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/225k [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "pytorch_model.bin:   0%|          | 0.00/440M [00:00<?, ?B/s]"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model initialized: microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract\n"
          ]
        }
      ],
      "source": [
        "# Cell 7: Initialize Tokenizer and Medical-Specific Model\n",
        "# ---\n",
        "# ## 6. Initialize tokenizer and model\n",
        "print(\"\\nInitializing tokenizer and model...\")\n",
        "\n",
        "# Use a medical-specific model instead of MobileBERT\n",
        "model_name = \"microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract\"  # Medical domain-specific model\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "model = EnhancedMedicationModel(\n",
        "    model_name=model_name,\n",
        "    num_medications=num_medication_classes,\n",
        "    num_polypharmacy_classes=num_polypharmacy_classes,\n",
        "    num_disease_classes=num_disease_classes,\n",
        "    dropout_rate=0.3\n",
        ")\n",
        "model = model.to(device)\n",
        "print(f\"Model initialized: {model_name}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zWSSeCtmj5nU",
        "outputId": "fa3fdda8-3780-442b-b2a8-03f894889592"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using batch size: 16\n"
          ]
        }
      ],
      "source": [
        "# Cell 8: Create Data Loaders with Optimized Batch Size\n",
        "# ---\n",
        "# ## 7. Create data loaders\n",
        "# Use a larger batch size since we have 15,000 samples\n",
        "batch_size = 16  # Increased batch size for faster training\n",
        "print(f\"Using batch size: {batch_size}\")\n",
        "\n",
        "train_dataset = PatientDataset(X_train, med_train, poly_train, disease_train, tokenizer, max_length=256)  # Increased max_length\n",
        "test_dataset = PatientDataset(X_test, med_test, poly_test, disease_test, tokenizer, max_length=256)\n",
        "\n",
        "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)  # Added num_workers for faster loading\n",
        "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qSTEGkAjj7aM",
        "outputId": "13495ff1-e56b-4579-b7fe-d2192bef72b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Calculating class weights for imbalanced tasks...\n",
            "Using learning rate: 3e-05\n",
            "Training for 10 epochs\n"
          ]
        }
      ],
      "source": [
        "# Cell 9: Loss Functions with Class Weighting and Optimizer\n",
        "# ---\n",
        "# ## 8. Define loss functions with class weighting and optimizer\n",
        "print(\"Calculating class weights for imbalanced tasks...\")\n",
        "\n",
        "# For medications (multi-label)\n",
        "pos_counts = np.sum(medication_labels, axis=0)\n",
        "neg_counts = len(medication_labels) - pos_counts\n",
        "pos_weights = torch.tensor(neg_counts / (pos_counts + 1e-5), dtype=torch.float32).to(device)\n",
        "\n",
        "# For polypharmacy risk (multi-class)\n",
        "risk_counts = np.bincount(polypharmacy_labels)\n",
        "risk_weights = torch.tensor(len(polypharmacy_labels) / (risk_counts * len(risk_counts) + 1e-5), dtype=torch.float32).to(device)\n",
        "\n",
        "# For disease (multi-class)\n",
        "disease_counts = np.bincount(disease_labels)\n",
        "disease_weights = torch.tensor(len(disease_labels) / (disease_counts * len(disease_counts) + 1e-5), dtype=torch.float32).to(device)\n",
        "\n",
        "# Create weighted loss functions\n",
        "medication_criterion = nn.BCEWithLogitsLoss(pos_weight=pos_weights)\n",
        "polypharmacy_criterion = nn.CrossEntropyLoss(weight=risk_weights)\n",
        "disease_criterion = nn.CrossEntropyLoss(weight=disease_weights)\n",
        "\n",
        "# Learning rate for different layers\n",
        "batch_size = 16  # Increased batch size since you have 15000 samples\n",
        "lr = 3e-5\n",
        "optimizer = torch.optim.AdamW([\n",
        "    {'params': model.bert.parameters(), 'lr': lr * 0.1},  # Lower LR for pretrained part\n",
        "    {'params': [p for n, p in model.named_parameters() if 'bert' not in n], 'lr': lr}\n",
        "], weight_decay=0.01)\n",
        "print(f\"Using learning rate: {lr}\")\n",
        "\n",
        "# ## 9. Learning rate scheduler\n",
        "num_epochs = 10\n",
        "print(f\"Training for {num_epochs} epochs\")\n",
        "\n",
        "total_steps = len(train_loader) * num_epochs\n",
        "\n",
        "# Use cosine scheduler with warmup\n",
        "from transformers import get_cosine_schedule_with_warmup\n",
        "warmup_steps = int(total_steps * 0.1)  # Warm up for 10% of steps\n",
        "scheduler = get_cosine_schedule_with_warmup(\n",
        "    optimizer,\n",
        "    num_warmup_steps=warmup_steps,\n",
        "    num_training_steps=total_steps\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "oMxofSf4j9R8"
      },
      "outputs": [],
      "source": [
        "# Cell 10: Improved Training and Evaluation Functions\n",
        "# ---\n",
        "# ## 10. Enhanced training and evaluation functions\n",
        "# For mixed precision training\n",
        "scaler = torch.cuda.amp.GradScaler() if torch.cuda.is_available() else None\n",
        "\n",
        "def train_epoch(model, dataloader, optimizer, scheduler, medication_criterion,\n",
        "               polypharmacy_criterion, disease_criterion, device, scaler=None):\n",
        "    model.train()\n",
        "    total_loss = 0\n",
        "    med_losses = 0\n",
        "    poly_losses = 0\n",
        "    disease_losses = 0\n",
        "\n",
        "    progress_bar = tqdm(dataloader, desc=\"Training\")\n",
        "\n",
        "    for batch in progress_bar:\n",
        "        input_ids = batch['input_ids'].to(device)\n",
        "        attention_mask = batch['attention_mask'].to(device)\n",
        "        medication_labels = batch['medication_labels'].to(device)\n",
        "        polypharmacy_labels = batch['polypharmacy_label'].to(device)\n",
        "        disease_labels = batch['disease_label'].to(device)\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        if scaler:  # Use mixed precision if available\n",
        "            with torch.cuda.amp.autocast():\n",
        "                medication_logits, polypharmacy_logits, disease_logits = model(input_ids, attention_mask)\n",
        "\n",
        "                # Calculate losses\n",
        "                med_loss = medication_criterion(medication_logits, medication_labels)\n",
        "                poly_loss = polypharmacy_criterion(polypharmacy_logits, polypharmacy_labels)\n",
        "                disease_loss = disease_criterion(disease_logits, disease_labels)\n",
        "\n",
        "                # Combined loss with task-specific weighting\n",
        "                # For your specific imbalance, emphasize medications more\n",
        "                loss = 2.0 * med_loss + 1.0 * poly_loss + 1.0 * disease_loss\n",
        "\n",
        "            scaler.scale(loss).backward()\n",
        "            # Add gradient clipping\n",
        "            scaler.unscale_(optimizer)\n",
        "            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
        "            scaler.step(optimizer)\n",
        "            scaler.update()\n",
        "        else:\n",
        "            # Forward pass\n",
        "            medication_logits, polypharmacy_logits, disease_logits = model(input_ids, attention_mask)\n",
        "\n",
        "            # Calculate losses\n",
        "            med_loss = medication_criterion(medication_logits, medication_labels)\n",
        "            poly_loss = polypharmacy_criterion(polypharmacy_logits, polypharmacy_labels)\n",
        "            disease_loss = disease_criterion(disease_logits, disease_labels)\n",
        "\n",
        "            # Combined loss with task-specific weighting\n",
        "            loss = 2.0 * med_loss + 1.0 * poly_loss + 1.0 * disease_loss\n",
        "\n",
        "            # Backward pass\n",
        "            loss.backward()\n",
        "            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
        "            optimizer.step()\n",
        "\n",
        "        scheduler.step()\n",
        "        total_loss += loss.item()\n",
        "        med_losses += med_loss.item()\n",
        "        poly_losses += poly_loss.item()\n",
        "        disease_losses += disease_loss.item()\n",
        "\n",
        "        # Update progress bar\n",
        "        progress_bar.set_postfix({\n",
        "            'loss': loss.item(),\n",
        "            'med_loss': med_loss.item(),\n",
        "            'poly_loss': poly_loss.item(),\n",
        "            'disease_loss': disease_loss.item()\n",
        "        })\n",
        "\n",
        "    avg_loss = total_loss / len(dataloader)\n",
        "    avg_med_loss = med_losses / len(dataloader)\n",
        "    avg_poly_loss = poly_losses / len(dataloader)\n",
        "    avg_disease_loss = disease_losses / len(dataloader)\n",
        "\n",
        "    return avg_loss, avg_med_loss, avg_poly_loss, avg_disease_loss\n",
        "\n",
        "def evaluate(model, dataloader, medication_criterion, polypharmacy_criterion, disease_criterion, device):\n",
        "    model.eval()\n",
        "    total_loss = 0\n",
        "    med_losses = 0\n",
        "    poly_losses = 0\n",
        "    disease_losses = 0\n",
        "\n",
        "    medication_preds_all = []\n",
        "    medication_labels_all = []\n",
        "    polypharmacy_preds_all = []\n",
        "    polypharmacy_labels_all = []\n",
        "    disease_preds_all = []\n",
        "    disease_labels_all = []\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for batch in tqdm(dataloader, desc=\"Evaluating\"):\n",
        "            input_ids = batch['input_ids'].to(device)\n",
        "            attention_mask = batch['attention_mask'].to(device)\n",
        "            medication_labels = batch['medication_labels'].to(device)\n",
        "            polypharmacy_labels = batch['polypharmacy_label'].to(device)\n",
        "            disease_labels = batch['disease_label'].to(device)\n",
        "\n",
        "            # Forward pass\n",
        "            medication_logits, polypharmacy_logits, disease_logits = model(input_ids, attention_mask)\n",
        "\n",
        "            # Calculate losses\n",
        "            med_loss = medication_criterion(medication_logits, medication_labels)\n",
        "            poly_loss = polypharmacy_criterion(polypharmacy_logits, polypharmacy_labels)\n",
        "            disease_loss = disease_criterion(disease_logits, disease_labels)\n",
        "            loss = 2.0 * med_loss + 1.0 * poly_loss + 1.0 * disease_loss  # Keep weighting consistent\n",
        "\n",
        "            total_loss += loss.item()\n",
        "            med_losses += med_loss.item()\n",
        "            poly_losses += poly_loss.item()\n",
        "            disease_losses += disease_loss.item()\n",
        "\n",
        "            # Get predictions\n",
        "            medication_preds = torch.sigmoid(medication_logits) > 0.5\n",
        "            polypharmacy_preds = torch.argmax(polypharmacy_logits, dim=1)\n",
        "            disease_preds = torch.argmax(disease_logits, dim=1)\n",
        "\n",
        "            # Collect all predictions and labels\n",
        "            medication_preds_all.extend(medication_preds.cpu().numpy())\n",
        "            medication_labels_all.extend(medication_labels.cpu().numpy())\n",
        "            polypharmacy_preds_all.extend(polypharmacy_preds.cpu().numpy())\n",
        "            polypharmacy_labels_all.extend(polypharmacy_labels.cpu().numpy())\n",
        "            disease_preds_all.extend(disease_preds.cpu().numpy())\n",
        "            disease_labels_all.extend(disease_labels.cpu().numpy())\n",
        "\n",
        "    # Convert to numpy arrays\n",
        "    medication_preds_all = np.array(medication_preds_all)\n",
        "    medication_labels_all = np.array(medication_labels_all)\n",
        "    polypharmacy_preds_all = np.array(polypharmacy_preds_all)\n",
        "    polypharmacy_labels_all = np.array(polypharmacy_labels_all)\n",
        "    disease_preds_all = np.array(disease_preds_all)\n",
        "    disease_labels_all = np.array(disease_labels_all)\n",
        "\n",
        "    # Calculate metrics\n",
        "    medication_f1 = f1_score(medication_labels_all, medication_preds_all, average='weighted')\n",
        "    polypharmacy_acc = accuracy_score(polypharmacy_labels_all, polypharmacy_preds_all)\n",
        "    disease_acc = accuracy_score(disease_labels_all, disease_preds_all)\n",
        "\n",
        "    avg_loss = total_loss / len(dataloader)\n",
        "    avg_med_loss = med_losses / len(dataloader)\n",
        "    avg_poly_loss = poly_losses / len(dataloader)\n",
        "    avg_disease_loss = disease_losses / len(dataloader)\n",
        "\n",
        "    return avg_loss, avg_med_loss, avg_poly_loss, avg_disease_loss, medication_f1, polypharmacy_acc, disease_acc"
      ]
    },
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        "id": "eR9kljbAj__L",
        "outputId": "598ac29e-35d2-4848-c218-26338bf081a8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Starting training...\n",
            "\n",
            "Epoch 1/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training:   0%|          | 0/750 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "805013b4580c4f5190dc995d0b586457"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training loss: 5.0794 (Med: 1.2103, Poly: 0.8949, Disease: 1.7639)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating:   0%|          | 0/188 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f8faa7e131ae4417b73bb40a5d7e5902"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation loss: 2.8597 (Med: 0.6960, Poly: 0.1610, Disease: 1.3067)\n",
            "Medication F1 score: 0.4366\n",
            "Polypharmacy accuracy: 0.9693\n",
            "Disease prediction accuracy: 0.3880\n",
            "Saved best model based on medication F1 score!\n",
            "\n",
            "Epoch 2/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training:   0%|          | 0/750 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6e154d1520114fa7b550d758e7562a29"
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          },
          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training loss: 2.6128 (Med: 0.6297, Poly: 0.0836, Disease: 1.2698)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating:   0%|          | 0/188 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f6df50a30a484b1b9c76eaf68bd447fd"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation loss: 2.2340 (Med: 0.5125, Poly: 0.0201, Disease: 1.1889)\n",
            "Medication F1 score: 0.5382\n",
            "Polypharmacy accuracy: 0.9947\n",
            "Disease prediction accuracy: 0.4353\n",
            "Saved best model based on medication F1 score!\n",
            "\n",
            "Epoch 3/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training:   0%|          | 0/750 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "607e23d15ae647a8ae6f78b928bd8dd1"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training loss: 2.3140 (Med: 0.5424, Poly: 0.0312, Disease: 1.1979)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating:   0%|          | 0/188 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "697c3ed1e65242cbbf2935c779fd00d3"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation loss: 2.2355 (Med: 0.5044, Poly: 0.0203, Disease: 1.2064)\n",
            "Medication F1 score: 0.5475\n",
            "Polypharmacy accuracy: 0.9957\n",
            "Disease prediction accuracy: 0.4370\n",
            "Saved best model based on medication F1 score!\n",
            "\n",
            "Epoch 4/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training:   0%|          | 0/750 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7dc6148a1aa14ec5807edf1445013f7c"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training loss: 2.2538 (Med: 0.5261, Poly: 0.0190, Disease: 1.1825)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating:   0%|          | 0/188 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "25ce08ee8d1c4c1ba4cae7f2acad9108"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation loss: 2.1946 (Med: 0.4979, Poly: 0.0169, Disease: 1.1820)\n",
            "Medication F1 score: 0.5485\n",
            "Polypharmacy accuracy: 0.9970\n",
            "Disease prediction accuracy: 0.4280\n",
            "Saved best model based on medication F1 score!\n",
            "\n",
            "Epoch 5/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training:   0%|          | 0/750 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b0b67d21c3ba4480a348a5961c053f36"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training loss: 2.2362 (Med: 0.5205, Poly: 0.0176, Disease: 1.1776)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating:   0%|          | 0/188 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "85e8ac6c7e6344a78ce45af1391a915c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation loss: 2.1875 (Med: 0.4969, Poly: 0.0130, Disease: 1.1808)\n",
            "Medication F1 score: 0.5474\n",
            "Polypharmacy accuracy: 0.9973\n",
            "Disease prediction accuracy: 0.4267\n",
            "\n",
            "Epoch 6/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training:   0%|          | 0/750 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "405edd1f066a42a59086fefd6fc4ba01"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training loss: 2.2241 (Med: 0.5177, Poly: 0.0163, Disease: 1.1723)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating:   0%|          | 0/188 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "204131ac2ba049e5be7b3bb957bf96e6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation loss: 2.1843 (Med: 0.4948, Poly: 0.0132, Disease: 1.1816)\n",
            "Medication F1 score: 0.5502\n",
            "Polypharmacy accuracy: 0.9977\n",
            "Disease prediction accuracy: 0.4253\n",
            "Saved best model based on medication F1 score!\n",
            "\n",
            "Epoch 7/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training:   0%|          | 0/750 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c64fee4def224eaf9628d21b4cad488e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training loss: 2.2094 (Med: 0.5119, Poly: 0.0163, Disease: 1.1693)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating:   0%|          | 0/188 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "88c1392900234b6a8115dcdb7c2eee7b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation loss: 2.1877 (Med: 0.4963, Poly: 0.0122, Disease: 1.1829)\n",
            "Medication F1 score: 0.5493\n",
            "Polypharmacy accuracy: 0.9980\n",
            "Disease prediction accuracy: 0.4313\n",
            "Early stopping counter: 1/3\n",
            "\n",
            "Epoch 8/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training:   0%|          | 0/750 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7a453efb38a14931a06cf263e4b296b2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training loss: 2.2045 (Med: 0.5103, Poly: 0.0148, Disease: 1.1691)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating:   0%|          | 0/188 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b1b5e3e43b6440a4a953258f99ca5e4f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation loss: 2.1824 (Med: 0.4944, Poly: 0.0121, Disease: 1.1816)\n",
            "Medication F1 score: 0.5494\n",
            "Polypharmacy accuracy: 0.9980\n",
            "Disease prediction accuracy: 0.4317\n",
            "Saved best model based on validation loss!\n",
            "\n",
            "Epoch 9/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training:   0%|          | 0/750 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8bb20d8df63c4fb1ad2c64aace30eaca"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training loss: 2.1966 (Med: 0.5076, Poly: 0.0130, Disease: 1.1684)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating:   0%|          | 0/188 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "59bc16f921df497aa775aae6eaf12d69"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation loss: 2.1788 (Med: 0.4941, Poly: 0.0121, Disease: 1.1786)\n",
            "Medication F1 score: 0.5494\n",
            "Polypharmacy accuracy: 0.9980\n",
            "Disease prediction accuracy: 0.4387\n",
            "Saved best model based on validation loss!\n",
            "\n",
            "Epoch 10/10\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training:   0%|          | 0/750 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f59d9022be144490b1a59d5da239133f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training loss: 2.1961 (Med: 0.5086, Poly: 0.0140, Disease: 1.1650)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating:   0%|          | 0/188 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2c28f6ef69de48529136c5b1589bcd7a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation loss: 2.1788 (Med: 0.4941, Poly: 0.0120, Disease: 1.1786)\n",
            "Medication F1 score: 0.5494\n",
            "Polypharmacy accuracy: 0.9980\n",
            "Disease prediction accuracy: 0.4400\n",
            "Saved best model based on validation loss!\n"
          ]
        }
      ],
      "source": [
        "# Cell 11: Training Loop with Early Stopping\n",
        "# ---\n",
        "# ## 11. Enhanced training loop with early stopping\n",
        "print(\"\\nStarting training...\")\n",
        "history = {\n",
        "    'train_loss': [], 'train_med_loss': [], 'train_poly_loss': [], 'train_disease_loss': [],\n",
        "    'val_loss': [], 'val_med_loss': [], 'val_poly_loss': [], 'val_disease_loss': [],\n",
        "    'med_f1': [], 'poly_acc': [], 'disease_acc': []\n",
        "}\n",
        "\n",
        "best_val_loss = float('inf')\n",
        "best_med_f1 = 0.0  # Track best medication F1 score\n",
        "best_model_path = 'best_medical_multitask_model.pt'\n",
        "patience = 3  # Early stopping patience\n",
        "early_stopping_counter = 0\n",
        "\n",
        "for epoch in range(num_epochs):\n",
        "    print(f\"\\nEpoch {epoch+1}/{num_epochs}\")\n",
        "\n",
        "    # Training\n",
        "    train_loss, train_med_loss, train_poly_loss, train_disease_loss = train_epoch(\n",
        "        model, train_loader, optimizer, scheduler,\n",
        "        medication_criterion, polypharmacy_criterion, disease_criterion, device, scaler\n",
        "    )\n",
        "\n",
        "    history['train_loss'].append(train_loss)\n",
        "    history['train_med_loss'].append(train_med_loss)\n",
        "    history['train_poly_loss'].append(train_poly_loss)\n",
        "    history['train_disease_loss'].append(train_disease_loss)\n",
        "\n",
        "    print(f\"Training loss: {train_loss:.4f} (Med: {train_med_loss:.4f}, Poly: {train_poly_loss:.4f}, Disease: {train_disease_loss:.4f})\")\n",
        "\n",
        "    # Evaluation\n",
        "    val_loss, val_med_loss, val_poly_loss, val_disease_loss, med_f1, poly_acc, disease_acc = evaluate(\n",
        "        model, test_loader, medication_criterion, polypharmacy_criterion, disease_criterion, device\n",
        "    )\n",
        "\n",
        "    history['val_loss'].append(val_loss)\n",
        "    history['val_med_loss'].append(val_med_loss)\n",
        "    history['val_poly_loss'].append(val_poly_loss)\n",
        "    history['val_disease_loss'].append(val_disease_loss)\n",
        "    history['med_f1'].append(med_f1)\n",
        "    history['poly_acc'].append(poly_acc)\n",
        "    history['disease_acc'].append(disease_acc)\n",
        "\n",
        "    print(f\"Validation loss: {val_loss:.4f} (Med: {val_med_loss:.4f}, Poly: {val_poly_loss:.4f}, Disease: {val_disease_loss:.4f})\")\n",
        "    print(f\"Medication F1 score: {med_f1:.4f}\")\n",
        "    print(f\"Polypharmacy accuracy: {poly_acc:.4f}\")\n",
        "    print(f\"Disease prediction accuracy: {disease_acc:.4f}\")\n",
        "\n",
        "    # Save best model - prioritize medication F1 score since that's the worst performing task\n",
        "    if med_f1 > best_med_f1:\n",
        "        best_med_f1 = med_f1\n",
        "        torch.save(model.state_dict(), best_model_path)\n",
        "        print(\"Saved best model based on medication F1 score!\")\n",
        "        early_stopping_counter = 0\n",
        "    elif val_loss < best_val_loss:\n",
        "        best_val_loss = val_loss\n",
        "        if epoch > num_epochs // 2:  # Only consider validation loss in later epochs\n",
        "            torch.save(model.state_dict(), best_model_path)\n",
        "            print(\"Saved best model based on validation loss!\")\n",
        "        early_stopping_counter = 0\n",
        "    else:\n",
        "        early_stopping_counter += 1\n",
        "        print(f\"Early stopping counter: {early_stopping_counter}/{patience}\")\n",
        "\n",
        "    # Early stopping\n",
        "    if early_stopping_counter >= patience:\n",
        "        print(f\"Early stopping triggered after epoch {epoch+1}\")\n",
        "        break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "id": "eqC3LqW-kB_r",
        "outputId": "ed729d5d-f75e-41e1-c790-2801a90313d6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 4 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Cell 12: Plot Training History\n",
        "# ---\n",
        "# ## 12. Plot training history\n",
        "def plot_training_history(history):\n",
        "    plt.figure(figsize=(15, 10))\n",
        "\n",
        "    # Plot losses\n",
        "    plt.subplot(2, 2, 1)\n",
        "    plt.plot(history['train_loss'], label='Train Loss')\n",
        "    plt.plot(history['val_loss'], label='Val Loss')\n",
        "    plt.title('Overall Loss')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.legend()\n",
        "\n",
        "    plt.subplot(2, 2, 2)\n",
        "    plt.plot(history['train_med_loss'], label='Train Med Loss')\n",
        "    plt.plot(history['val_med_loss'], label='Val Med Loss')\n",
        "    plt.title('Medication Loss')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.legend()\n",
        "\n",
        "    plt.subplot(2, 2, 3)\n",
        "    plt.plot(history['train_poly_loss'], label='Train Poly Loss')\n",
        "    plt.plot(history['val_poly_loss'], label='Val Poly Loss')\n",
        "    plt.title('Polypharmacy Risk Loss')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.legend()\n",
        "\n",
        "    # Plot metrics\n",
        "    plt.subplot(2, 2, 4)\n",
        "    plt.plot(history['med_f1'], label='Medication F1')\n",
        "    plt.plot(history['poly_acc'], label='Polypharmacy Accuracy')\n",
        "    plt.title('Performance Metrics')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Score')\n",
        "    plt.legend()\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.savefig('training_history.png')\n",
        "    plt.show()\n",
        "\n",
        "# Plot training history\n",
        "plot_training_history(history)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sivbla_SkEl9",
        "outputId": "2b514a3f-6071-44d3-a705-b0c65ed42b4e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Loading best model for inference...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EnhancedMedicationModel(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0-11): 12 x BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSdpaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.3, inplace=False)\n",
              "  (common_dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "  (medication_classifier): Sequential(\n",
              "    (0): Linear(in_features=768, out_features=384, bias=True)\n",
              "    (1): ReLU()\n",
              "    (2): Dropout(p=0.3, inplace=False)\n",
              "    (3): Linear(in_features=384, out_features=27, bias=True)\n",
              "  )\n",
              "  (polypharmacy_classifier): Sequential(\n",
              "    (0): Linear(in_features=768, out_features=384, bias=True)\n",
              "    (1): ReLU()\n",
              "    (2): Dropout(p=0.3, inplace=False)\n",
              "    (3): Linear(in_features=384, out_features=3, bias=True)\n",
              "  )\n",
              "  (disease_classifier): Sequential(\n",
              "    (0): Linear(in_features=768, out_features=384, bias=True)\n",
              "    (1): ReLU()\n",
              "    (2): Dropout(p=0.3, inplace=False)\n",
              "    (3): Linear(in_features=384, out_features=5, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# Cell 13: Load Best Model\n",
        "# ---\n",
        "# ## 13. Load best model for inference\n",
        "print(\"\\nLoading best model for inference...\")\n",
        "model.load_state_dict(torch.load(best_model_path))\n",
        "model.eval()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "WA_u9lQQkGdV"
      },
      "outputs": [],
      "source": [
        "# Cell 14: Prediction Function\n",
        "# ---\n",
        "# ## 14. Create a function for predictions on new data\n",
        "def predict_full_health_profile(patient_data, model, tokenizer, mlb, le_risk, le_disease, device):\n",
        "    \"\"\"\n",
        "    Make comprehensive predictions for new patient data\n",
        "\n",
        "    Args:\n",
        "        patient_data: Dictionary with patient information\n",
        "        model: Trained model\n",
        "        tokenizer: Tokenizer\n",
        "        mlb: MultiLabelBinarizer for medications\n",
        "        le_risk: LabelEncoder for polypharmacy risk\n",
        "        le_disease: LabelEncoder for disease\n",
        "        device: Device to run model on\n",
        "\n",
        "    Returns:\n",
        "        Dictionary with predicted disease, medications, dosages, frequencies, instructions,\n",
        "        durations, polypharmacy risk, and health tips\n",
        "    \"\"\"\n",
        "    # Create text input like during training\n",
        "    text_input = f\"Age: {patient_data.get('Age', '')}, Gender: {patient_data.get('Gender', '')}, \" + \\\n",
        "                f\"Blood Group: {patient_data.get('Blood_Group', '')}, Weight: {patient_data.get('Weight_kg', '')}, \" + \\\n",
        "                f\"Symptoms: {patient_data.get('Symptoms', '')}, Severity: {patient_data.get('Severity_Scores', '')}\"\n",
        "\n",
        "    # Tokenize\n",
        "    encoding = tokenizer(\n",
        "        text_input,\n",
        "        add_special_tokens=True,\n",
        "        max_length=128,\n",
        "        padding='max_length',\n",
        "        truncation=True,\n",
        "        return_tensors='pt'\n",
        "    )\n",
        "\n",
        "    # Move to device\n",
        "    input_ids = encoding['input_ids'].to(device)\n",
        "    attention_mask = encoding['attention_mask'].to(device)\n",
        "\n",
        "    # Get predictions\n",
        "    with torch.no_grad():\n",
        "        medication_logits, polypharmacy_logits, disease_logits = model(input_ids, attention_mask)\n",
        "        medication_preds = torch.sigmoid(medication_logits) > 0.5\n",
        "        polypharmacy_pred = torch.argmax(polypharmacy_logits, dim=1)\n",
        "        disease_pred = torch.argmax(disease_logits, dim=1)\n",
        "\n",
        "    # Convert predictions to human-readable format\n",
        "    predicted_medications = mlb.classes_[medication_preds[0].cpu().numpy()]\n",
        "    predicted_risk = le_risk.classes_[polypharmacy_pred.item()]\n",
        "    predicted_disease = le_disease.classes_[disease_pred.item()]\n",
        "\n",
        "    # Get medication probabilities for all medications\n",
        "    medication_probs = torch.sigmoid(medication_logits).cpu().numpy()[0]\n",
        "    med_prob_dict = {med: prob for med, prob in zip(mlb.classes_, medication_probs)}\n",
        "\n",
        "    # Sort medications by probability\n",
        "    sorted_meds = sorted(med_prob_dict.items(), key=lambda x: x[1], reverse=True)\n",
        "    top_meds = sorted_meds[:5]  # Get top 5 medications\n",
        "\n",
        "    # Determine medication details from dataset based on predicted disease and medications\n",
        "    # Use a lookup approach from the training data\n",
        "    med_details = {}\n",
        "    for i, med in enumerate(predicted_medications[:3]):  # Limit to top 3 medications\n",
        "        med_idx = i + 1  # Medicine_1, Medicine_2, Medicine_3\n",
        "        matching_rows = df[(df['Predicted_Disease'] == predicted_disease) &\n",
        "                           (df[f'Medicine_{med_idx}'] == med)]\n",
        "\n",
        "        if not matching_rows.empty:\n",
        "            row = matching_rows.iloc[0]\n",
        "            med_details[med] = {\n",
        "                'dosage': row.get(f'Dosage_{med_idx}', 'Standard dosage'),\n",
        "                'frequency': row.get(f'Frequency_{med_idx}', 'As directed'),\n",
        "                'instruction': row.get(f'Instruction_{med_idx}', 'Follow doctor\\'s advice'),\n",
        "                'duration': row.get(f'Duration_{med_idx}', '7-10 days')\n",
        "            }\n",
        "        else:\n",
        "            med_details[med] = {\n",
        "                'dosage': 'Consult doctor',\n",
        "                'frequency': 'Consult doctor',\n",
        "                'instruction': 'Consult doctor',\n",
        "                'duration': 'As prescribed'\n",
        "            }\n",
        "\n",
        "    # Get disease information\n",
        "    disease_causes = disease_causes_dict.get(predicted_disease, \"Unknown causes\")\n",
        "    disease_prevention = disease_prevention_dict.get(predicted_disease, \"Consult healthcare provider\")\n",
        "\n",
        "    # Get polypharmacy recommendation\n",
        "    polypharmacy_recommendation = polypharmacy_recommendation_dict.get(predicted_risk, \"Consult healthcare provider\")\n",
        "\n",
        "    # Get personalized health tip based on disease and demographics\n",
        "    age_decade = (patient_data.get('Age', 40) // 10) * 10\n",
        "    health_tip_key = (predicted_disease, age_decade, patient_data.get('Gender', 'Other'))\n",
        "    personalized_health_tip = health_tips_dict.get(\n",
        "        health_tip_key,\n",
        "        \"Maintain a balanced diet and regular exercise routine.\"\n",
        "    )\n",
        "\n",
        "    # Format medication results\n",
        "    med_results = []\n",
        "    for i, med in enumerate(predicted_medications[:3]):\n",
        "        if i < len(predicted_medications):\n",
        "            med_results.append({\n",
        "                'medication': med,\n",
        "                'dosage': med_details[med]['dosage'],\n",
        "                'frequency': med_details[med]['frequency'],\n",
        "                'instruction': med_details[med]['instruction'],\n",
        "                'duration': med_details[med]['duration'],\n",
        "                'confidence': float(med_prob_dict[med])\n",
        "            })\n",
        "\n",
        "    # Return comprehensive results\n",
        "    return {\n",
        "        'predicted_disease': predicted_disease,\n",
        "        'disease_causes': disease_causes,\n",
        "        'disease_prevention': disease_prevention,\n",
        "        'medications': med_results,\n",
        "        'polypharmacy_risk': predicted_risk,\n",
        "        'polypharmacy_recommendation': polypharmacy_recommendation,\n",
        "        'personalized_health_tips': personalized_health_tip,\n",
        "        'medication_probabilities': {med: float(prob) for med, prob in top_meds}\n",
        "    }"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yd5sHV52kI67",
        "outputId": "ea9b470b-4217-4044-8229-521159f5e4e4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Testing with a sample patient from the dataset:\n",
            "\n",
            "Patient Information:\n",
            "Age: 18\n",
            "Gender: Other\n",
            "Disease: Asthma\n",
            "Symptoms: Chest pain; Cough; Shortness of breath; Headache\n",
            "Actual Medications: ['Albuterol Inhaler', 'Budesonide Inhaler', 'Montelukast']\n",
            "Actual Polypharmacy Risk: Low to Medium\n",
            "\n",
            "Prediction Results:\n",
            "Predicted Disease: Asthma\n",
            "Predicted Medications: ['Albuterol Inhaler', 'Budesonide Inhaler', 'Montelukast']\n",
            "\n",
            "Top Medication Probabilities:\n",
            "  Albuterol Inhaler: 0.9741\n",
            "  Montelukast: 0.9554\n",
            "  Budesonide Inhaler: 0.9511\n",
            "  Guaifenesin: 0.3399\n",
            "  Amoxicillin: 0.3179\n",
            "Predicted Polypharmacy Risk: Low to Medium\n",
            "Polypharmacy Recommendation: Moderate risk. Follow prescribed dosage carefully.\n",
            "\n",
            "Personalized Health Tips: nan\n"
          ]
        }
      ],
      "source": [
        "# Cell 15: Sample Patient Prediction\n",
        "# ---\n",
        "# ## 15. Test with a sample patient\n",
        "print(\"\\nTesting with a sample patient from the dataset:\")\n",
        "sample_patient = {\n",
        "    'Age': df['Age'].iloc[0],\n",
        "    'Gender': df['Gender'].iloc[0],\n",
        "    'Blood_Group': df['Blood_Group'].iloc[0],\n",
        "    'Weight_kg': df['Weight_kg'].iloc[0],\n",
        "    'Symptoms': df['Symptoms'].iloc[0],\n",
        "    'Severity_Scores': df['Severity_Scores'].iloc[0]\n",
        "}\n",
        "\n",
        "print(\"\\nPatient Information:\")\n",
        "print(f\"Age: {sample_patient['Age']}\")\n",
        "print(f\"Gender: {sample_patient['Gender']}\")\n",
        "print(f\"Disease: {df['Predicted_Disease'].iloc[0]}\")  # For reference only\n",
        "print(f\"Symptoms: {sample_patient['Symptoms']}\")\n",
        "\n",
        "# Get actual medications for comparison\n",
        "actual_meds = [df['Medicine_1'].iloc[0], df['Medicine_2'].iloc[0], df['Medicine_3'].iloc[0]]\n",
        "actual_meds = [med for med in actual_meds if med != 'None' and not pd.isna(med)]\n",
        "print(f\"Actual Medications: {actual_meds}\")\n",
        "print(f\"Actual Polypharmacy Risk: {df['Polypharmacy_Risk'].iloc[0]}\")\n",
        "\n",
        "# Make prediction with new function name\n",
        "prediction = predict_full_health_profile(sample_patient, model, tokenizer, mlb, le_risk, le_disease, device)\n",
        "\n",
        "print(\"\\nPrediction Results:\")\n",
        "print(f\"Predicted Disease: {prediction['predicted_disease']}\")\n",
        "print(f\"Predicted Medications: {[med['medication'] for med in prediction['medications']]}\")\n",
        "print(\"\\nTop Medication Probabilities:\")\n",
        "for med, prob in prediction['medication_probabilities'].items():\n",
        "    print(f\"  {med}: {prob:.4f}\")\n",
        "print(f\"Predicted Polypharmacy Risk: {prediction['polypharmacy_risk']}\")\n",
        "print(f\"Polypharmacy Recommendation: {prediction['polypharmacy_recommendation']}\")\n",
        "print(f\"\\nPersonalized Health Tips: {prediction['personalized_health_tips']}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 757,
          "referenced_widgets": [
            "0442c04734714a0eaff0fa7ea2920253",
            "1dc238b8159c403da4ea03b1ffe25157",
            "92a6d20e871b4365803b2b65c206ec22",
            "7225ee84067945aca4cf29f785c9e691",
            "c2171e58f2ac404ca4393bb7ded2a6d2",
            "1beed4a69b1d4bd0a2d5f40cdf0c2c5d",
            "63b86ea130d145a9832cb5a985960b52",
            "c34e3e855dcb43f7a16a1c1956a0d7b1",
            "4c831401f8c74451b712cd3184442eee",
            "8dce31123fd04c05a209d95a76db7bc4",
            "16a566ba3d5742498c9f83e53d1d1ad5"
          ]
        },
        "id": "i0wlgFC-kLWs",
        "outputId": "e6e0d87d-b485-4706-c8ec-f7e03f0fcfdb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Evaluating model performance on test data...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Evaluating test data:   0%|          | 0/188 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0442c04734714a0eaff0fa7ea2920253"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "----- Detailed Performance Metrics -----\n",
            "Medication prediction accuracy: 0.2170\n",
            "Medication F1-score: 0.5494 (Precision: 0.4325, Recall: 0.9960)\n",
            "Polypharmacy risk prediction accuracy: 0.9980\n",
            "Polypharmacy F1-score: 0.9980 (Precision: 0.9980, Recall: 0.9980)\n",
            "Disease prediction accuracy: 0.4400\n",
            "Disease F1-score: 0.4530 (Precision: 0.7059, Recall: 0.4400)\n",
            "\n",
            "Sample of test results:\n",
            "                Actual_Disease            Predicted_Disease  Disease_Match  \\\n",
            "0  Upper Respiratory Infection  Upper Respiratory Infection           True   \n",
            "1                     Migraine                     Migraine           True   \n",
            "2                   Bronchitis                       Asthma          False   \n",
            "3                   Bronchitis                   Bronchitis           True   \n",
            "4                   Bronchitis                   Bronchitis           True   \n",
            "\n",
            "                                  Actual_Medications  \\\n",
            "0  [Max 200mg/day, May increase urination, Take w...   \n",
            "1           [Ibuprofen, Metoclopramide, Sumatriptan]   \n",
            "2      [Albuterol Inhaler, Amoxicillin, Guaifenesin]   \n",
            "3    [Max 200mg/day, Stay hydrated, Take in evening]   \n",
            "4  [Max 200mg/day, Monitor blood pressure, Use in...   \n",
            "\n",
            "                               Predicted_Medications  Medications_Match  \\\n",
            "0  [Complete full course, For nausea, Max 200mg/d...              False   \n",
            "1           [Ibuprofen, Metoclopramide, Sumatriptan]               True   \n",
            "2  [Albuterol Inhaler, Amoxicillin, Budesonide In...              False   \n",
            "3  [Complete full course, For nausea, Max 200mg/d...              False   \n",
            "4  [Complete full course, For nausea, Max 200mg/d...              False   \n",
            "\n",
            "      Actual_Risk  Predicted_Risk  Risk_Match  \n",
            "0         Unknown         Unknown        True  \n",
            "1  Medium to High  Medium to High        True  \n",
            "2  Medium to High  Medium to High        True  \n",
            "3         Unknown         Unknown        True  \n",
            "4         Unknown         Unknown        True  \n"
          ]
        }
      ],
      "source": [
        "# Cell 16: Enhanced Model Performance Evaluation with Detailed Metrics\n",
        "# ---\n",
        "# ## 16. Create an enhanced function to evaluate model performance on all test data\n",
        "def evaluate_model_performance():\n",
        "    print(\"\\nEvaluating model performance on test data...\")\n",
        "\n",
        "    # Get all test predictions\n",
        "    all_predictions = []\n",
        "    medication_preds_all = []\n",
        "    medication_labels_all = []\n",
        "    polypharmacy_preds_all = []\n",
        "    polypharmacy_labels_all = []\n",
        "    disease_preds_all = []\n",
        "    disease_labels_all = []\n",
        "\n",
        "    batch_idx = 0\n",
        "\n",
        "    for batch in tqdm(test_loader, desc=\"Evaluating test data\"):\n",
        "        input_ids = batch['input_ids'].to(device)\n",
        "        attention_mask = batch['attention_mask'].to(device)\n",
        "        medication_labels = batch['medication_labels']\n",
        "        polypharmacy_labels = batch['polypharmacy_label']\n",
        "        disease_labels = batch['disease_label']\n",
        "\n",
        "        with torch.no_grad():\n",
        "            # Get model predictions\n",
        "            medication_logits, polypharmacy_logits, disease_logits = model(input_ids, attention_mask)\n",
        "            medication_preds = torch.sigmoid(medication_logits) > 0.5\n",
        "            polypharmacy_preds = torch.argmax(polypharmacy_logits, dim=1)\n",
        "            disease_preds = torch.argmax(disease_logits, dim=1)\n",
        "\n",
        "        # Collect predictions and labels for metrics calculation\n",
        "        medication_preds_all.extend(medication_preds.cpu().numpy())\n",
        "        medication_labels_all.extend(medication_labels.cpu().numpy())\n",
        "        polypharmacy_preds_all.extend(polypharmacy_preds.cpu().numpy())\n",
        "        polypharmacy_labels_all.extend(polypharmacy_labels.cpu().numpy())\n",
        "        disease_preds_all.extend(disease_preds.cpu().numpy())\n",
        "        disease_labels_all.extend(disease_labels.cpu().numpy())\n",
        "\n",
        "        # Get predictions for each sample in batch\n",
        "        for j in range(len(input_ids)):\n",
        "            idx = batch_idx * batch_size + j\n",
        "            if idx >= len(X_test):  # Guard against out of bounds\n",
        "                break\n",
        "\n",
        "            pred_meds = mlb.classes_[medication_preds[j].cpu().numpy()]\n",
        "            pred_risk = le_risk.classes_[polypharmacy_preds[j].item()]\n",
        "            pred_disease = le_disease.classes_[disease_preds[j].item()]\n",
        "\n",
        "            # Get actual values\n",
        "            actual_meds = mlb.classes_[medication_labels[j].numpy().astype(bool)]\n",
        "            actual_risk = le_risk.classes_[polypharmacy_labels[j].item()]\n",
        "            actual_disease = le_disease.classes_[disease_labels[j].item()]\n",
        "\n",
        "            # Store results\n",
        "            all_predictions.append({\n",
        "                'Text_Input': X_test[idx],\n",
        "                'Actual_Medications': actual_meds.tolist(),\n",
        "                'Predicted_Medications': pred_meds.tolist(),\n",
        "                'Medications_Match': set(pred_meds) == set(actual_meds),\n",
        "                'Actual_Risk': actual_risk,\n",
        "                'Predicted_Risk': pred_risk,\n",
        "                'Risk_Match': actual_risk == pred_risk,\n",
        "                'Actual_Disease': actual_disease,\n",
        "                'Predicted_Disease': pred_disease,\n",
        "                'Disease_Match': actual_disease == pred_disease\n",
        "            })\n",
        "\n",
        "        batch_idx += 1\n",
        "\n",
        "    # Convert lists to arrays for metrics calculation\n",
        "    medication_preds_all = np.array(medication_preds_all)\n",
        "    medication_labels_all = np.array(medication_labels_all)\n",
        "    polypharmacy_preds_all = np.array(polypharmacy_preds_all)\n",
        "    polypharmacy_labels_all = np.array(polypharmacy_labels_all)\n",
        "    disease_preds_all = np.array(disease_preds_all)\n",
        "    disease_labels_all = np.array(disease_labels_all)\n",
        "\n",
        "    # Calculate metrics\n",
        "    med_accuracy = np.mean([set(p['Predicted_Medications']) == set(p['Actual_Medications']) for p in all_predictions])\n",
        "    risk_accuracy = np.mean([p['Risk_Match'] for p in all_predictions])\n",
        "    disease_accuracy = np.mean([p['Disease_Match'] for p in all_predictions])\n",
        "\n",
        "    # Calculate F1 scores\n",
        "    from sklearn.metrics import classification_report, precision_recall_fscore_support\n",
        "\n",
        "    # For medication (multi-label)\n",
        "    med_precision, med_recall, med_f1, _ = precision_recall_fscore_support(\n",
        "        medication_labels_all,\n",
        "        medication_preds_all,\n",
        "        average='weighted'\n",
        "    )\n",
        "\n",
        "    # For polypharmacy and disease (multi-class)\n",
        "    poly_precision, poly_recall, poly_f1, _ = precision_recall_fscore_support(\n",
        "        polypharmacy_labels_all,\n",
        "        polypharmacy_preds_all,\n",
        "        average='weighted'\n",
        "    )\n",
        "\n",
        "    disease_precision, disease_recall, disease_f1, _ = precision_recall_fscore_support(\n",
        "        disease_labels_all,\n",
        "        disease_preds_all,\n",
        "        average='weighted'\n",
        "    )\n",
        "\n",
        "    # Print detailed results\n",
        "    print(\"\\n----- Detailed Performance Metrics -----\")\n",
        "    print(f\"Medication prediction accuracy: {med_accuracy:.4f}\")\n",
        "    print(f\"Medication F1-score: {med_f1:.4f} (Precision: {med_precision:.4f}, Recall: {med_recall:.4f})\")\n",
        "    print(f\"Polypharmacy risk prediction accuracy: {risk_accuracy:.4f}\")\n",
        "    print(f\"Polypharmacy F1-score: {poly_f1:.4f} (Precision: {poly_precision:.4f}, Recall: {poly_recall:.4f})\")\n",
        "    print(f\"Disease prediction accuracy: {disease_accuracy:.4f}\")\n",
        "    print(f\"Disease F1-score: {disease_f1:.4f} (Precision: {disease_precision:.4f}, Recall: {disease_recall:.4f})\")\n",
        "\n",
        "    # Create DataFrame with results\n",
        "    results_df = pd.DataFrame(all_predictions)\n",
        "\n",
        "    return results_df\n",
        "\n",
        "# Run evaluation\n",
        "test_results = evaluate_model_performance()\n",
        "print(\"\\nSample of test results:\")\n",
        "print(test_results[['Actual_Disease', 'Predicted_Disease', 'Disease_Match',\n",
        "                   'Actual_Medications', 'Predicted_Medications', 'Medications_Match',\n",
        "                   'Actual_Risk', 'Predicted_Risk', 'Risk_Match']].head())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cell 17: Save Model Artifacts for Streamlit Deployment\n",
        "# ---\n",
        "# ## 17. Save everything needed for deployment\n",
        "print(\"\\nSaving model artifacts for streamlit deployment...\")\n",
        "\n",
        "# Create directory for model artifacts\n",
        "import os\n",
        "if not os.path.exists(\"streamlit_model\"):\n",
        "    os.makedirs(\"streamlit_model\")\n",
        "\n",
        "# Save model state dictionary separately (smaller file)\n",
        "torch.save(model.state_dict(), 'streamlit_model/model_state_dict.pt')\n",
        "\n",
        "# Save model configuration separately\n",
        "model_config = {\n",
        "    'model_name': model_name,\n",
        "    'num_medications': num_medication_classes,\n",
        "    'num_polypharmacy_classes': num_polypharmacy_classes,\n",
        "    'num_disease_classes': num_disease_classes\n",
        "}\n",
        "with open('streamlit_model/model_config.json', 'w') as f:\n",
        "    import json\n",
        "    json.dump(model_config, f)\n",
        "\n",
        "# Save encoders\n",
        "with open('streamlit_model/label_encoders.pkl', 'wb') as f:\n",
        "    pickle.dump({\n",
        "        'mlb': mlb,\n",
        "        'le_risk': le_risk,\n",
        "        'le_disease': le_disease\n",
        "    }, f)\n",
        "\n",
        "# Save dictionaries for lookups\n",
        "lookup_data = {\n",
        "    'disease_causes_dict': disease_causes_dict,\n",
        "    'disease_prevention_dict': disease_prevention_dict,\n",
        "    'polypharmacy_recommendation_dict': polypharmacy_recommendation_dict,\n",
        "    'health_tips_dict': health_tips_dict\n",
        "}\n",
        "with open('streamlit_model/lookup_data.pkl', 'wb') as f:\n",
        "    pickle.dump(lookup_data, f)\n",
        "\n",
        "print(\"Model artifacts saved in 'streamlit_model' directory\")"
      ],
      "metadata": {
        "id": "Gw_bPORRkPcD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b53a6192-44be-466b-8dca-32e193fd07cf"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Saving model artifacts for streamlit deployment...\n",
            "Model artifacts saved in 'streamlit_model' directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cell 18: Prediction Interface Functions\n",
        "# ---\n",
        "# ## 18. Create a prediction interface function\n",
        "def prepare_patient_data(age, gender, blood_group, weight_kg, symptoms, severity_scores):\n",
        "    \"\"\"\n",
        "    Helper function to format patient data for prediction\n",
        "    \"\"\"\n",
        "    return {\n",
        "        'Age': age,\n",
        "        'Gender': gender,\n",
        "        'Blood_Group': blood_group,\n",
        "        'Weight_kg': weight_kg,\n",
        "        'Symptoms': symptoms,\n",
        "        'Severity_Scores': severity_scores\n",
        "    }\n",
        "\n",
        "def predict_for_new_patient(age, gender, blood_group, weight_kg, symptoms, severity_scores):\n",
        "    \"\"\"\n",
        "    User-friendly function to predict comprehensive health profile\n",
        "\n",
        "    Returns:\n",
        "        Dictionary with predicted disease, medications details, risk and health tips\n",
        "    \"\"\"\n",
        "    patient_data = prepare_patient_data(\n",
        "        age, gender, blood_group, weight_kg, symptoms, severity_scores\n",
        "    )\n",
        "    prediction = predict_full_health_profile(patient_data, model, tokenizer, mlb, le_risk, le_disease, device)\n",
        "    return prediction\n",
        "\n",
        "# Example usage\n",
        "print(\"\\nDemonstration of prediction interface:\")\n",
        "test_prediction = predict_for_new_patient(\n",
        "    age=65,\n",
        "    gender=\"Female\",\n",
        "    blood_group=\"A+\",\n",
        "    weight_kg=70.5,\n",
        "    symptoms=\"Headache; Dizziness; Chest pain\",\n",
        "    severity_scores=\"Headache:3; Dizziness:2; Chest pain:4\"\n",
        ")\n",
        "\n",
        "print(\"\\nNew Patient Prediction:\")\n",
        "print(f\"Predicted Disease: {test_prediction['predicted_disease']}\")\n",
        "print(f\"Disease Causes: {test_prediction['disease_causes']}\")\n",
        "print(f\"Disease Prevention: {test_prediction['disease_prevention']}\")\n",
        "print(\"\\nRecommended Medications:\")\n",
        "for i, med in enumerate(test_prediction['medications']):\n",
        "    print(f\"  {i+1}. {med['medication']}\")\n",
        "    print(f\"     Dosage: {med['dosage']}\")\n",
        "    print(f\"     Frequency: {med['frequency']}\")\n",
        "    print(f\"     Instructions: {med['instruction']}\")\n",
        "    print(f\"     Duration: {med['duration']}\")\n",
        "    print(f\"     Confidence: {med['confidence']:.4f}\")\n",
        "print(f\"\\nPolypharmacy Risk: {test_prediction['polypharmacy_risk']}\")\n",
        "print(f\"Polypharmacy Recommendation: {test_prediction['polypharmacy_recommendation']}\")\n",
        "print(f\"\\nPersonalized Health Tips: {test_prediction['personalized_health_tips']}\")\n",
        "\n",
        "print(\"\\nTraining complete! Model and artifacts saved successfully.\")"
      ],
      "metadata": {
        "id": "l7a_KXcBkRUs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9112f7f3-2ea3-4cba-8e43-b8706b4cb74d"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Demonstration of prediction interface:\n",
            "\n",
            "New Patient Prediction:\n",
            "Predicted Disease: Migraine\n",
            "Disease Causes: Common causes include various factors related to Bronchitis.\n",
            "Disease Prevention: Preventive measures specific to Bronchitis and general health maintenance.\n",
            "\n",
            "Recommended Medications:\n",
            "  1. Hydrochlorothiazide\n",
            "     Dosage: Consult doctor\n",
            "     Frequency: Consult doctor\n",
            "     Instructions: Consult doctor\n",
            "     Duration: As prescribed\n",
            "     Confidence: 0.6935\n",
            "  2. Lisinopril\n",
            "     Dosage: Consult doctor\n",
            "     Frequency: Consult doctor\n",
            "     Instructions: Consult doctor\n",
            "     Duration: As prescribed\n",
            "     Confidence: 0.5205\n",
            "  3. Sumatriptan\n",
            "     Dosage: Consult doctor\n",
            "     Frequency: Consult doctor\n",
            "     Instructions: Consult doctor\n",
            "     Duration: As prescribed\n",
            "     Confidence: 0.5631\n",
            "\n",
            "Polypharmacy Risk: Low to Medium\n",
            "Polypharmacy Recommendation: Moderate risk. Follow prescribed dosage carefully.\n",
            "\n",
            "Personalized Health Tips: nan\n",
            "\n",
            "Training complete! Model and artifacts saved successfully.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cell 19: Interactive Prediction Interface\n",
        "# ---\n",
        "# ## 19. Create a simple interactive prediction interface\n",
        "def interactive_prediction_interface():\n",
        "    \"\"\"\n",
        "    Simple command-line interface for making predictions\n",
        "    \"\"\"\n",
        "    print(\"\\n=== Drug Prediction and Polypharmacy System ===\")\n",
        "    print(\"Enter patient information to get medication recommendations and polypharmacy risk\")\n",
        "\n",
        "    age = int(input(\"Age: \"))\n",
        "    gender = input(\"Gender (Male/Female/Other): \")\n",
        "    blood_group = input(\"Blood Group: \")\n",
        "    weight_kg = float(input(\"Weight (kg): \"))\n",
        "    symptoms = input(\"Symptoms (separated by semicolons, e.g., 'Headache; Fever'): \")\n",
        "    severity_scores = input(\"Severity Scores (format: 'Symptom:Score; Symptom:Score'): \")\n",
        "    predicted_disease = input(\"Predicted Disease: \")\n",
        "\n",
        "    prediction = predict_for_new_patient(\n",
        "        age, gender, blood_group, weight_kg, symptoms, severity_scores, predicted_disease\n",
        "    )\n",
        "\n",
        "    print(\"\\n=== Prediction Results ===\")\n",
        "    print(f\"Recommended Medications:\")\n",
        "    for med in prediction['predicted_medications']:\n",
        "        prob = prediction['medication_probabilities'].get(med, 0)\n",
        "        print(f\"  - {med} (Confidence: {prob:.2f})\")\n",
        "\n",
        "    print(f\"\\nPolypharmacy Risk: {prediction['polypharmacy_risk']}\")\n",
        "\n",
        "    if prediction['polypharmacy_risk'] in ['Medium to High', 'High']:\n",
        "        print(\"WARNING: Higher polypharmacy risk detected. Consider medication review.\")\n",
        "\n",
        "    return prediction"
      ],
      "metadata": {
        "id": "xydCVmSmkTUs"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731,
          "referenced_widgets": [
            "57279e4cf0354384bef9155ef791c7cc",
            "aa6b156a7ab0425ca9c3e0be3b9dbb6f",
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            "707059d647a94ae8ad55c5aee6a621c2",
            "e73df13eab4546088c0eb6042fe547d6",
            "af6187efb3c34b8c96d2ba602c62070f",
            "bb4dbb5131cf44ba8a103dc5d11e0276",
            "43905625042c475fba560ee2c4a9d108",
            "7724d671b28647f5ad0500b835884acb",
            "78503f31b74546fea05892aa33179ad3",
            "fff8471f51854536b4188c1dbd2c48b4",
            "1bc5d8b100ed4f859a5f04ba7adda69b",
            "fc1f5950bd4546d1968642c4df68808e",
            "5eb82d21a392417cbfb62e24d5ced947",
            "c6d1d47807834ba4a50ef97b4655c49e",
            "a367b0362041488394557041af82277f",
            "e8326045fd17465297a02ae1f44f108d",
            "fac7491f65134f2cbec7835f9c241de5",
            "f3b76192f69048be8648a134053ccf04",
            "1f9c635ab7a8452a9dfd83f8a47de32f",
            "29a6cd51fa5641df9306f9f79c95e2e6",
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            "4c35a48c5af94b63a4ce2ffb0dcf0158",
            "8e10b757f3ee416a8c2e8f11c5cc39be",
            "23106176275b4879962dd2f0de137e3d",
            "2b75b7b215e545348e5fb56aa13d8651",
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            "4bfcc91e1e914f3495d8b879c7f04ac2"
          ]
        },
        "id": "q3aiZXxHkVUb",
        "outputId": "726fd6a2-ddd6-4e24-e654-3b516e907c28"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HBox(children=(IntSlider(value=45, description='Age:', min=1), Dropdown(description='Gender:', …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "57279e4cf0354384bef9155ef791c7cc"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Cell 20: Run Interactive Interface (Main Block)\n",
        "# ---\n",
        "# ## 20. Run the interactive interface\n",
        "# Import necessary libraries for widgets\n",
        "from IPython.display import display, clear_output\n",
        "import ipywidgets as widgets\n",
        "\n",
        "# Create a function for the form-based interface\n",
        "def create_prediction_widget():\n",
        "    # Create input widgets\n",
        "    age_widget = widgets.IntSlider(description='Age:', min=1, max=100, value=45)\n",
        "    gender_widget = widgets.Dropdown(\n",
        "        options=['Male', 'Female', 'Other'],\n",
        "        description='Gender:',\n",
        "        value='Female'\n",
        "    )\n",
        "    blood_group_widget = widgets.Dropdown(\n",
        "        options=['A+', 'A-', 'B+', 'B-', 'AB+', 'AB-', 'O+', 'O-'],\n",
        "        description='Blood Group:',\n",
        "        value='B+'\n",
        "    )\n",
        "    weight_widget = widgets.FloatText(description='Weight (kg):', value=75.0)\n",
        "    symptoms_widget = widgets.Textarea(\n",
        "        description='Symptoms:',\n",
        "        value='Chest pain; Cough; Shortness of breath',\n",
        "        layout=widgets.Layout(width='80%')\n",
        "    )\n",
        "    severity_widget = widgets.Textarea(\n",
        "        description='Severity:',\n",
        "        value='Chest pain:3; Cough:2; Shortness of breath:2',\n",
        "        layout=widgets.Layout(width='80%')\n",
        "    )\n",
        "\n",
        "    output = widgets.Output()\n",
        "\n",
        "    # Create predict button\n",
        "    predict_button = widgets.Button(\n",
        "        description=\"Predict Health Profile\",\n",
        "        button_style='primary',\n",
        "        layout=widgets.Layout(width='50%')\n",
        "    )\n",
        "\n",
        "    # Define button click action\n",
        "    def on_button_click(b):\n",
        "        with output:\n",
        "            clear_output()\n",
        "\n",
        "            # Get values from widgets\n",
        "            age = age_widget.value\n",
        "            gender = gender_widget.value\n",
        "            blood_group = blood_group_widget.value\n",
        "            weight_kg = weight_widget.value\n",
        "            symptoms = symptoms_widget.value\n",
        "            severity_scores = severity_widget.value\n",
        "\n",
        "            # Make prediction\n",
        "            try:\n",
        "                patient_data = {\n",
        "                    'Age': age,\n",
        "                    'Gender': gender,\n",
        "                    'Blood_Group': blood_group,\n",
        "                    'Weight_kg': weight_kg,\n",
        "                    'Symptoms': symptoms,\n",
        "                    'Severity_Scores': severity_scores\n",
        "                }\n",
        "\n",
        "                prediction = predict_full_health_profile(patient_data, model, tokenizer, mlb, le_risk, le_disease, device)\n",
        "\n",
        "                # Display results\n",
        "                print(\"\\n=== Health Profile Prediction Results ===\")\n",
        "                print(f\"Patient: {age} year old {gender}\")\n",
        "                print(f\"\\nPredicted Disease: {prediction['predicted_disease']}\")\n",
        "                print(f\"Disease Causes: {prediction['disease_causes'][:100]}...\" if len(prediction['disease_causes']) > 100 else f\"Disease Causes: {prediction['disease_causes']}\")\n",
        "                print(f\"Disease Prevention: {prediction['disease_prevention'][:100]}...\" if len(prediction['disease_prevention']) > 100 else f\"Disease Prevention: {prediction['disease_prevention']}\")\n",
        "\n",
        "                print(f\"\\nRecommended Medications:\")\n",
        "                for i, med in enumerate(prediction['medications']):\n",
        "                    print(f\"  {i+1}. {med['medication']} (Confidence: {med['confidence']:.2f})\")\n",
        "                    print(f\"     Dosage: {med['dosage']}\")\n",
        "                    print(f\"     Frequency: {med['frequency']}\")\n",
        "                    print(f\"     Instructions: {med['instruction']}\")\n",
        "                    print(f\"     Duration: {med['duration']}\")\n",
        "\n",
        "                print(f\"\\nPolypharmacy Risk: {prediction['polypharmacy_risk']}\")\n",
        "                print(f\"Polypharmacy Recommendation: {prediction['polypharmacy_recommendation']}\")\n",
        "\n",
        "                print(f\"\\nPersonalized Health Tips: {prediction['personalized_health_tips']}\")\n",
        "\n",
        "            except Exception as e:\n",
        "                print(f\"Error making prediction: {str(e)}\")\n",
        "                print(\"Please check your inputs and try again.\")\n",
        "\n",
        "    predict_button.on_click(on_button_click)\n",
        "\n",
        "    # Create form layout\n",
        "    form = widgets.VBox([\n",
        "        widgets.HBox([age_widget, gender_widget]),\n",
        "        widgets.HBox([blood_group_widget, weight_widget]),\n",
        "        widgets.VBox([symptoms_widget, severity_widget]),\n",
        "        predict_button,\n",
        "        output\n",
        "    ])\n",
        "\n",
        "    display(form)\n",
        "\n",
        "# Run the widget interface\n",
        "create_prediction_widget()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cell 21: Streamlit Deployment File\n",
        "# ---\n",
        "# ## 21. Create streamlit deployment file\n",
        "print(\"\\nCreating Streamlit deployment file...\")\n",
        "\n",
        "streamlit_code = '''\n",
        "import streamlit as st\n",
        "import torch\n",
        "import numpy as np\n",
        "import pickle\n",
        "import json\n",
        "from transformers import AutoTokenizer, AutoModel\n",
        "import torch.nn as nn\n",
        "import os\n",
        "\n",
        "# Set page config\n",
        "st.set_page_config(\n",
        "    page_title=\"Drug Prediction and Polypharmacy System\",\n",
        "    page_icon=\"💊\",\n",
        "    layout=\"wide\"\n",
        ")\n",
        "\n",
        "# Model class definition - must match the training model architecture\n",
        "class EnhancedMedicationModel(nn.Module):\n",
        "    def __init__(self, model_name, num_medications, num_polypharmacy_classes, num_disease_classes, dropout_rate=0.3):\n",
        "        super().__init__()\n",
        "        self.bert = AutoModel.from_pretrained(model_name)\n",
        "        self.dropout = nn.Dropout(dropout_rate)\n",
        "        hidden_size = self.bert.config.hidden_size\n",
        "\n",
        "        # Common representation layer\n",
        "        self.common_dense = nn.Linear(hidden_size, hidden_size)\n",
        "\n",
        "        # Task-specific layers with increased complexity\n",
        "        # Medication prediction head (multi-label)\n",
        "        self.medication_classifier = nn.Sequential(\n",
        "            nn.Linear(hidden_size, hidden_size//2),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(dropout_rate),\n",
        "            nn.Linear(hidden_size//2, num_medications)\n",
        "        )\n",
        "\n",
        "        # Polypharmacy risk head (multi-class)\n",
        "        self.polypharmacy_classifier = nn.Sequential(\n",
        "            nn.Linear(hidden_size, hidden_size//2),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(dropout_rate),\n",
        "            nn.Linear(hidden_size//2, num_polypharmacy_classes)\n",
        "        )\n",
        "\n",
        "        # Disease prediction head (multi-class)\n",
        "        self.disease_classifier = nn.Sequential(\n",
        "            nn.Linear(hidden_size, hidden_size//2),\n",
        "            nn.ReLU(),\n",
        "            nn.Dropout(dropout_rate),\n",
        "            nn.Linear(hidden_size//2, num_disease_classes)\n",
        "        )\n",
        "\n",
        "        # Apply weight initialization\n",
        "        self._init_weights()\n",
        "\n",
        "    def _init_weights(self):\n",
        "        # Initialize weights for better convergence\n",
        "        for module in [self.medication_classifier, self.polypharmacy_classifier,\n",
        "                      self.disease_classifier, self.common_dense]:\n",
        "            if isinstance(module, nn.Sequential):\n",
        "                for layer in module:\n",
        "                    if isinstance(layer, nn.Linear):\n",
        "                        nn.init.xavier_normal_(layer.weight)\n",
        "                        nn.init.zeros_(layer.bias)\n",
        "            elif isinstance(module, nn.Linear):\n",
        "                nn.init.xavier_normal_(module.weight)\n",
        "                nn.init.zeros_(layer.bias)\n",
        "\n",
        "    def forward(self, input_ids, attention_mask):\n",
        "        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)\n",
        "        pooled_output = outputs.last_hidden_state[:, 0, :]  # CLS token\n",
        "        pooled_output = self.dropout(pooled_output)\n",
        "\n",
        "        # Common representation\n",
        "        common_features = torch.relu(self.common_dense(pooled_output))\n",
        "\n",
        "        medication_logits = self.medication_classifier(common_features)\n",
        "        polypharmacy_logits = self.polypharmacy_classifier(common_features)\n",
        "        disease_logits = self.disease_classifier(common_features)\n",
        "\n",
        "        return medication_logits, polypharmacy_logits, disease_logits\n",
        "\n",
        "@st.cache_resource\n",
        "def load_model_and_resources():\n",
        "    \"\"\"Load model and necessary resources (cached for performance)\"\"\"\n",
        "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "    # Load model configuration\n",
        "    with open('streamlit_model/model_config.json', 'r') as f:\n",
        "        model_config = json.load(f)\n",
        "\n",
        "    # Initialize model\n",
        "    model_name = model_config['model_name']\n",
        "    tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "    # Create model architecture\n",
        "    model = EnhancedMedicationModel(\n",
        "        model_name=model_name,\n",
        "        num_medications=model_config['num_medications'],\n",
        "        num_polypharmacy_classes=model_config['num_polypharmacy_classes'],\n",
        "        num_disease_classes=model_config['num_disease_classes'],\n",
        "        dropout_rate=0.3\n",
        "    )\n",
        "\n",
        "    # Load trained weights\n",
        "    model.load_state_dict(torch.load('streamlit_model/model_state_dict.pt', map_location=device))\n",
        "    model = model.to(device)\n",
        "    model.eval()\n",
        "\n",
        "    # Load encoders\n",
        "    with open('streamlit_model/label_encoders.pkl', 'rb') as f:\n",
        "        encoders = pickle.load(f)\n",
        "\n",
        "    # Load lookup data\n",
        "    with open('streamlit_model/lookup_data.pkl', 'rb') as f:\n",
        "        lookup_data = pickle.load(f)\n",
        "\n",
        "    return {\n",
        "        'model': model,\n",
        "        'tokenizer': tokenizer,\n",
        "        'mlb': encoders['mlb'],\n",
        "        'le_risk': encoders['le_risk'],\n",
        "        'le_disease': encoders['le_disease'],\n",
        "        'lookup_data': lookup_data,\n",
        "        'device': device\n",
        "    }\n",
        "\n",
        "def predict_patient_health_profile(patient_data, resources):\n",
        "    \"\"\"\n",
        "    Predict health profile for a patient based on input data\n",
        "    \"\"\"\n",
        "    model = resources['model']\n",
        "    tokenizer = resources['tokenizer']\n",
        "    mlb = resources['mlb']\n",
        "    le_risk = resources['le_risk']\n",
        "    le_disease = resources['le_disease']\n",
        "    lookup_data = resources['lookup_data']\n",
        "    device = resources['device']\n",
        "\n",
        "    # Create text input\n",
        "    text_input = f\"Patient age {patient_data['age']}, gender {patient_data['gender']}, blood group {patient_data['blood_group']}, weight {patient_data['weight']}kg. \" + \\\n",
        "                f\"SYMPTOMS: {patient_data['symptoms']}. \" + \\\n",
        "                f\"SEVERITY: {patient_data['severity']}.\"\n",
        "\n",
        "    # Tokenize\n",
        "    encoding = tokenizer(\n",
        "        text_input,\n",
        "        add_special_tokens=True,\n",
        "        max_length=256,\n",
        "        padding='max_length',\n",
        "        truncation=True,\n",
        "        return_tensors='pt'\n",
        "    )\n",
        "\n",
        "    # Move to device\n",
        "    input_ids = encoding['input_ids'].to(device)\n",
        "    attention_mask = encoding['attention_mask'].to(device)\n",
        "\n",
        "    # Get predictions\n",
        "    with torch.no_grad():\n",
        "        medication_logits, polypharmacy_logits, disease_logits = model(input_ids, attention_mask)\n",
        "        medication_preds = torch.sigmoid(medication_logits) > 0.5\n",
        "        polypharmacy_pred = torch.argmax(polypharmacy_logits, dim=1)\n",
        "        disease_pred = torch.argmax(disease_logits, dim=1)\n",
        "\n",
        "    # Convert predictions to human-readable format\n",
        "    predicted_medications = mlb.classes_[medication_preds[0].cpu().numpy()]\n",
        "    predicted_risk = le_risk.classes_[polypharmacy_pred.item()]\n",
        "    predicted_disease = le_disease.classes_[disease_pred.item()]\n",
        "\n",
        "    # Get medication probabilities for all medications\n",
        "    medication_probs = torch.sigmoid(medication_logits).cpu().numpy()[0]\n",
        "    med_prob_dict = {med: prob for med, prob in zip(mlb.classes_, medication_probs)}\n",
        "\n",
        "    # Sort medications by probability\n",
        "    sorted_meds = sorted(med_prob_dict.items(), key=lambda x: x[1], reverse=True)\n",
        "    top_meds = sorted_meds[:5]  # Get top 5 medications\n",
        "\n",
        "    # Format medication results\n",
        "    med_results = []\n",
        "    for i, med in enumerate(predicted_medications[:3]):\n",
        "        med_details = {\n",
        "            'medication': med,\n",
        "            'dosage': 'Consult doctor',\n",
        "            'frequency': 'Consult doctor',\n",
        "            'instruction': 'Consult doctor',\n",
        "            'duration': 'As prescribed',\n",
        "            'confidence': float(med_prob_dict[med])\n",
        "        }\n",
        "        med_results.append(med_details)\n",
        "\n",
        "    # Get disease information\n",
        "    disease_causes = lookup_data['disease_causes_dict'].get(predicted_disease, \"Unknown causes\")\n",
        "    disease_prevention = lookup_data['disease_prevention_dict'].get(predicted_disease, \"Consult healthcare provider\")\n",
        "\n",
        "    # Get polypharmacy recommendation\n",
        "    polypharmacy_recommendation = lookup_data['polypharmacy_recommendation_dict'].get(\n",
        "        predicted_risk, \"Consult healthcare provider\"\n",
        "    )\n",
        "\n",
        "    # Get personalized health tip\n",
        "    age_decade = (patient_data['age'] // 10) * 10\n",
        "    health_tip_key = (predicted_disease, age_decade, patient_data['gender'])\n",
        "    personalized_health_tip = lookup_data['health_tips_dict'].get(\n",
        "        health_tip_key, \"Maintain a balanced diet and regular exercise routine.\"\n",
        "    )\n",
        "\n",
        "    # Return comprehensive results\n",
        "    return {\n",
        "        'predicted_disease': predicted_disease,\n",
        "        'disease_causes': disease_causes,\n",
        "        'disease_prevention': disease_prevention,\n",
        "        'medications': med_results,\n",
        "        'polypharmacy_risk': predicted_risk,\n",
        "        'polypharmacy_recommendation': polypharmacy_recommendation,\n",
        "        'personalized_health_tips': personalized_health_tip,\n",
        "        'medication_probabilities': {med: float(prob) for med, prob in top_meds}\n",
        "    }\n",
        "\n",
        "def main():\n",
        "    # App title and description\n",
        "    st.title(\"🏥 Drug Prediction and Polypharmacy System\")\n",
        "    st.markdown(\"Enter patient information to receive medication recommendations, disease prediction, and polypharmacy risk assessment.\")\n",
        "\n",
        "    try:\n",
        "        # Load model and resources\n",
        "        with st.spinner(\"Loading medical model and resources...\"):\n",
        "            resources = load_model_and_resources()\n",
        "\n",
        "        # Create two columns for input form\n",
        "        col1, col2 = st.columns(2)\n",
        "\n",
        "        # Patient information inputs\n",
        "        with col1:\n",
        "            st.subheader(\"Patient Information\")\n",
        "            age = st.number_input(\"Age\", min_value=1, max_value=120, value=45)\n",
        "            gender = st.selectbox(\"Gender\", options=[\"Male\", \"Female\", \"Other\"])\n",
        "            blood_group = st.selectbox(\"Blood Group\", options=[\"A+\", \"A-\", \"B+\", \"B-\", \"AB+\", \"AB-\", \"O+\", \"O-\"])\n",
        "            weight = st.number_input(\"Weight (kg)\", min_value=1.0, max_value=300.0, value=70.0, step=0.1)\n",
        "\n",
        "        with col2:\n",
        "            st.subheader(\"Symptoms Information\")\n",
        "            symptoms = st.text_area(\"Symptoms (separate with semicolons)\",\n",
        "                                   value=\"Headache; Fever; Fatigue\")\n",
        "            severity = st.text_area(\"Symptom Severity (format: 'Symptom:Score; Symptom:Score')\",\n",
        "                                   value=\"Headache:3; Fever:2; Fatigue:2\")\n",
        "\n",
        "        # Submit button\n",
        "        if st.button(\"Generate Health Profile\", type=\"primary\"):\n",
        "            with st.spinner(\"Analyzing patient data and generating health profile...\"):\n",
        "                # Prepare patient data\n",
        "                patient_data = {\n",
        "                    'age': age,\n",
        "                    'gender': gender,\n",
        "                    'blood_group': blood_group,\n",
        "                    'weight': weight,\n",
        "                    'symptoms': symptoms,\n",
        "                    'severity': severity\n",
        "                }\n",
        "\n",
        "                # Get prediction\n",
        "                prediction = predict_patient_health_profile(patient_data, resources)\n",
        "\n",
        "                # Display results in three columns\n",
        "                st.subheader(\"🔍 Health Profile Analysis Results\")\n",
        "\n",
        "                col1, col2, col3 = st.columns([1, 1, 1])\n",
        "\n",
        "                # Column 1: Disease information\n",
        "                with col1:\n",
        "                    st.markdown(\"### 🦠 Disease Prediction\")\n",
        "                    st.markdown(f\"**Predicted Disease**: {prediction['predicted_disease']}\")\n",
        "\n",
        "                    with st.expander(\"Disease Causes\"):\n",
        "                        st.write(prediction['disease_causes'])\n",
        "\n",
        "                    with st.expander(\"Prevention Methods\"):\n",
        "                        st.write(prediction['disease_prevention'])\n",
        "\n",
        "                # Column 2: Medication recommendations\n",
        "                with col2:\n",
        "                    st.markdown(\"### 💊 Medication Recommendations\")\n",
        "                    for i, med in enumerate(prediction['medications']):\n",
        "                        st.markdown(f\"**{i+1}. {med['medication']}** (Confidence: {med['confidence']:.2f})\")\n",
        "                        med_details = f\"\"\"\n",
        "                        - **Dosage:** {med['dosage']}\n",
        "                        - **Frequency:** {med['frequency']}\n",
        "                        - **Instructions:** {med['instruction']}\n",
        "                        - **Duration:** {med['duration']}\n",
        "                        \"\"\"\n",
        "                        st.markdown(med_details)\n",
        "                        st.divider()\n",
        "\n",
        "                # Column 3: Risk assessment and health tips\n",
        "                with col3:\n",
        "                    st.markdown(\"### ⚠️ Polypharmacy Assessment\")\n",
        "                    risk_color = \"green\" if prediction['polypharmacy_risk'] == \"Low\" else \\\n",
        "                                \"orange\" if prediction['polypharmacy_risk'] == \"Medium\" else \"red\"\n",
        "                    st.markdown(f\"**Risk Level**: <span style='color:{risk_color};font-weight:bold;'>{prediction['polypharmacy_risk']}</span>\",\n",
        "                                unsafe_allow_html=True)\n",
        "                    st.markdown(f\"**Recommendation**: {prediction['polypharmacy_recommendation']}\")\n",
        "\n",
        "                    st.markdown(\"### 🌿 Personalized Health Tips\")\n",
        "                    st.info(prediction['personalized_health_tips'])\n",
        "\n",
        "                # Display medication probabilities\n",
        "                st.subheader(\"Medication Confidence Scores\")\n",
        "                med_names = list(prediction['medication_probabilities'].keys())\n",
        "                med_probs = list(prediction['medication_probabilities'].values())\n",
        "\n",
        "                import matplotlib.pyplot as plt\n",
        "                fig, ax = plt.subplots()\n",
        "                y_pos = np.arange(len(med_names))\n",
        "                ax.barh(y_pos, med_probs, align='center')\n",
        "                ax.set_yticks(y_pos)\n",
        "                ax.set_yticklabels(med_names)\n",
        "                ax.invert_yaxis()  # Labels read top-to-bottom\n",
        "                ax.set_xlabel('Confidence Score')\n",
        "                ax.set_title('Top Medication Recommendations')\n",
        "\n",
        "                st.pyplot(fig)\n",
        "\n",
        "    except Exception as e:\n",
        "        st.error(f\"An error occurred: {str(e)}\")\n",
        "        st.error(\"Please make sure all model files are correctly placed in the 'streamlit_model' directory\")\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    main()\n",
        "'''\n",
        "\n",
        "# Create directory for model artifacts\n",
        "import os\n",
        "if not os.path.exists(\"streamlit_model\"):\n",
        "    os.makedirs(\"streamlit_model\")\n",
        "\n",
        "# Save model state dictionary separately (smaller file)\n",
        "torch.save(model.state_dict(), 'streamlit_model/model_state_dict.pt')\n",
        "\n",
        "# Save model configuration separately\n",
        "model_config = {\n",
        "    'model_name': model_name,\n",
        "    'num_medications': num_medication_classes,\n",
        "    'num_polypharmacy_classes': num_polypharmacy_classes,\n",
        "    'num_disease_classes': num_disease_classes\n",
        "}\n",
        "with open('streamlit_model/model_config.json', 'w') as f:\n",
        "    import json\n",
        "    json.dump(model_config, f)\n",
        "\n",
        "# Save encoders\n",
        "with open('streamlit_model/label_encoders.pkl', 'wb') as f:\n",
        "    pickle.dump({\n",
        "        'mlb': mlb,\n",
        "        'le_risk': le_risk,\n",
        "        'le_disease': le_disease\n",
        "    }, f)\n",
        "\n",
        "# Save dictionaries for lookups\n",
        "lookup_data = {\n",
        "    'disease_causes_dict': disease_causes_dict,\n",
        "    'disease_prevention_dict': disease_prevention_dict,\n",
        "    'polypharmacy_recommendation_dict': polypharmacy_recommendation_dict,\n",
        "    'health_tips_dict': health_tips_dict\n",
        "}\n",
        "with open('streamlit_model/lookup_data.pkl', 'wb') as f:\n",
        "    pickle.dump(lookup_data, f)\n",
        "\n",
        "# Write to file\n",
        "with open('app.py', 'w') as f:\n",
        "    f.write(streamlit_code)\n",
        "\n",
        "print(\"Streamlit app file created as 'app.py'\")\n",
        "print(\"Model artifacts saved in 'streamlit_model' directory\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rAU2msOcwZ-B",
        "outputId": "a04acf47-e736-4560-ae17-57a334f3992b"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Creating Streamlit deployment file...\n",
            "Streamlit app file created as 'app.py'\n",
            "Model artifacts saved in 'streamlit_model' directory\n"
          ]
        }
      ]
    }
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                  "=== Health Profile Prediction Results ===\n",
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                  "     Frequency: Consult doctor\n",
                  "     Instructions: Consult doctor\n",
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