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"metadata": {
"id": "e3o47QUAMyw5"
},
"source": [
"# 🧠 MindScan — Notebook 2: XLM-RoBERTa + Full Comparison\n",
"### NCI H9DAI Research Project 2026\n",
"\n",
"---\n",
"\n",
"## Prerequisites\n",
"- ✅ Notebook 1 must be fully complete (all 18 pkl files in Drive)\n",
"- ✅ **T4 GPU must be enabled** — Runtime → Change runtime type → T4 GPU\n",
"- ✅ Your 3 CSV datasets must be uploaded to Colab again\n",
"\n",
"## What this notebook does\n",
"1. Trains **XLM-RoBERTa** on each of the 3 datasets independently\n",
"2. Saves each transformer model to Drive immediately after training\n",
"3. Runs the **full 15-model comparison** (4 classical + XLM-RoBERTa per dataset)\n",
"4. Builds and tests `predict_all(text)` — the function the UI calls\n",
"\n",
"## Structure — run ONE section at a time\n",
"Each XLM-RoBERTa section is fully independent. If Section 2 crashes, Sections 1 and 3 are unaffected. Come back, restart, and rerun only what failed.\n",
"\n",
"| Section | Time on T4 | Output |\n",
"|---|---|---|\n",
"| Section 0 — Setup | 2 min | Libraries + Drive mount |\n",
"| Section 1 — XLM-RoBERTa D1 | ~10 min | `xlmr_d1_final/` in Drive |\n",
"| Section 2 — XLM-RoBERTa D2 | ~8 min | `xlmr_d2_final/` in Drive |\n",
"| Section 3 — XLM-RoBERTa D3 | ~10 min | `xlmr_d3_final/` in Drive |\n",
"| Section 4 — Full Comparison | 5 min | Charts + master table |\n",
"| Section 5 — predict_all() | 5 min | Live demo of UI function |"
],
"id": "e3o47QUAMyw5"
},
{
"cell_type": "markdown",
"metadata": {
"id": "VOpZgXcjMyw-"
},
"source": [
"---\n",
"# ⚙️ SECTION 0 — Setup\n",
"\n",
"**Run this every time you open the notebook**, even if you're continuing from a previous session."
],
"id": "VOpZgXcjMyw-"
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "0sGxGfVdMyw-",
"outputId": "839b8855-bcea-4688-9b47-a6d7b6f4d439"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"✅ GPU detected: Tesla T4, 15360 MiB\n"
]
}
],
"source": [
"# Check GPU is available — MUST be T4 before continuing\n",
"import subprocess\n",
"result = subprocess.run(['nvidia-smi', '--query-gpu=name,memory.total',\n",
" '--format=csv,noheader'], capture_output=True, text=True)\n",
"if result.returncode == 0:\n",
" print(f'✅ GPU detected: {result.stdout.strip()}')\n",
"else:\n",
" print('❌ NO GPU DETECTED')\n",
" print(' Go to Runtime → Change runtime type → T4 GPU → Save')\n",
" print(' Then re-run this cell.')"
],
"id": "0sGxGfVdMyw-"
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "DkegHL3vMyw_",
"outputId": "9ef77245-a4ce-450a-cad1-eb7902c998fd"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"✅ Libraries installed!\n"
]
}
],
"source": [
"!pip install -q transformers datasets accelerate scikit-learn xgboost imbalanced-learn\n",
"!pip install -q matplotlib seaborn pandas numpy joblib\n",
"print('✅ Libraries installed!')"
],
"id": "DkegHL3vMyw_"
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "eYXS4QCtMyxA",
"outputId": "48e98cdb-5bf7-4c11-ffe8-8081a4cc7a13"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"✅ Imports complete. Using device: cuda\n"
]
}
],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import re, string, os, joblib, json, warnings, shutil\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"import torch\n",
"from torch import nn\n",
"from transformers import (\n",
" AutoTokenizer,\n",
" AutoModelForSequenceClassification,\n",
" TrainingArguments,\n",
" Trainer\n",
")\n",
"from datasets import Dataset as HFDataset\n",
"\n",
"from sklearn.preprocessing import LabelEncoder\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.utils.class_weight import compute_class_weight\n",
"from sklearn.metrics import (\n",
" classification_report, confusion_matrix,\n",
" f1_score, accuracy_score, cohen_kappa_score\n",
")\n",
"\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"print(f'✅ Imports complete. Using device: {device}')"
],
"id": "eYXS4QCtMyxA"
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "wgNdamuFMyxA",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "63145a5d-531c-4658-f8d2-15819b948d52"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Mounted at /content/drive\n",
"✅ All Notebook 1 files found in Drive.\n",
" Transformer models will be saved to: /content/drive/MyDrive/MindScan_Models/transformers\n"
]
}
],
"source": [
"# ─────────────────────────────────────────────────────────────────\n",
"# MOUNT GOOGLE DRIVE — loads classical results, saves transformer files\n",
"# ─────────────────────────────────────────────────────────────────\n",
"from google.colab import drive\n",
"drive.mount('/content/drive')\n",
"\n",
"CLASSICAL_DIR = '/content/drive/MyDrive/MindScan_Models/classical'\n",
"TRANSFORMER_DIR = '/content/drive/MyDrive/MindScan_Models/transformers'\n",
"os.makedirs(TRANSFORMER_DIR, exist_ok=True)\n",
"\n",
"# Verify classical models are present from Notebook 1\n",
"expected = ['tfidf_d1.pkl','le_d1.pkl','tfidf_d2.pkl','le_d2.pkl','tfidf_d3.pkl','le_d3.pkl']\n",
"missing = [f for f in expected if not os.path.exists(os.path.join(CLASSICAL_DIR, f))]\n",
"\n",
"if missing:\n",
" print(f'❌ Missing files from Notebook 1: {missing}')\n",
" print(' Complete Notebook 1 first before running this notebook.')\n",
"else:\n",
" print('✅ All Notebook 1 files found in Drive.')\n",
" print(f' Transformer models will be saved to: {TRANSFORMER_DIR}')"
],
"id": "wgNdamuFMyxA"
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "ZAR_9OVyMyxB",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "cd8b72b3-b16b-4929-807e-8e5cf7d42ffb"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"✅ Classical results loaded from Drive:\n",
" Dataset Model Accuracy Macro F1 Cohen's Kappa\n",
" D1 Depression Type Logistic Regression 0.9152 0.9179 0.8971\n",
" D1 Depression Type Random Forest 0.9089 0.9129 0.8891\n",
" D1 Depression Type SVM 0.9236 0.9269 0.9072\n",
" D1 Depression Type XGBoost 0.9176 0.9217 0.9000\n",
"D2 Binary Depression Logistic Regression 0.9889 0.9839 0.9678\n",
"D2 Binary Depression Random Forest 0.9918 0.9880 0.9760\n",
"D2 Binary Depression SVM 0.9709 0.9589 0.9177\n",
"D2 Binary Depression XGBoost 0.9927 0.9895 0.9789\n",
" D3 Suicide Risk Logistic Regression 0.9318 0.9318 0.8636\n",
" D3 Suicide Risk Random Forest 0.8800 0.8800 0.7600\n",
" D3 Suicide Risk SVM 0.9368 0.9368 0.8736\n",
" D3 Suicide Risk XGBoost 0.9162 0.9162 0.8324\n",
"\n",
"✅ All shared functions ready. Model: xlm-roberta-base\n"
]
}
],
"source": [
"# ─────────────────────────────────────────────────────────────────\n",
"# SHARED FUNCTIONS — same clean_text and evaluate from Notebook 1\n",
"# ─────────────────────────────────────────────────────────────────\n",
"\n",
"def clean_text(text):\n",
" text = str(text).lower()\n",
" text = re.sub(r'http\\S+|www\\S+|https\\S+', '', text)\n",
" text = re.sub(r'@\\w+', '', text)\n",
" text = re.sub(r'#', '', text)\n",
" text = text.translate(str.maketrans('', '', string.punctuation))\n",
" text = re.sub(r'\\s+', ' ', text).strip()\n",
" return text\n",
"\n",
"\n",
"def evaluate_transformer(name, y_true, y_pred, label_names, ds_key, results_store):\n",
" \"\"\"\n",
" Same evaluation function as Notebook 1 but saves confusion matrix\n",
" to the transformers folder in Drive.\n",
" \"\"\"\n",
" acc = accuracy_score(y_true, y_pred)\n",
" macro = f1_score(y_true, y_pred, average='macro')\n",
" kappa = cohen_kappa_score(y_true, y_pred)\n",
"\n",
" results_store[name] = {\n",
" 'accuracy': round(acc, 4),\n",
" 'macro_f1': round(macro, 4),\n",
" 'kappa': round(kappa, 4)\n",
" }\n",
"\n",
" print(f'\\n{\"=\"*55}')\n",
" print(f' {name} [{ds_key}]')\n",
" print(f'{\"=\"*55}')\n",
" print(f' Accuracy : {acc*100:.2f}%')\n",
" print(f' Macro F1 : {macro:.4f} ← main metric')\n",
" print(f\" Cohen's Kappa: {kappa:.4f}\")\n",
" print(f'{\"=\"*55}\\n')\n",
" print(classification_report(y_true, y_pred, target_names=label_names, digits=3))\n",
"\n",
" cm = confusion_matrix(y_true, y_pred)\n",
" plt.figure(figsize=(max(6, len(label_names)*1.4), max(5, len(label_names)*1.1)))\n",
" sns.heatmap(cm, annot=True, fmt='d',\n",
" xticklabels=label_names, yticklabels=label_names,\n",
" cmap='Purples', linewidths=0.4)\n",
" plt.title(f'Confusion Matrix — {name} [{ds_key}]', fontsize=12, fontweight='bold')\n",
" plt.ylabel('Actual'); plt.xlabel('Predicted')\n",
" plt.xticks(rotation=30, ha='right')\n",
" plt.tight_layout()\n",
" chart_path = os.path.join(TRANSFORMER_DIR, f'cm_{ds_key}_XLM-RoBERTa.png')\n",
" plt.savefig(chart_path, dpi=120, bbox_inches='tight')\n",
" plt.show()\n",
"\n",
"\n",
"def save_transformer(model, tokenizer_obj, folder_name):\n",
" \"\"\"\n",
" Saves XLM-RoBERTa model + tokenizer to Drive.\n",
" Saves to both local and Drive.\n",
" \"\"\"\n",
" local_path = f'./{folder_name}'\n",
" drive_path = os.path.join(TRANSFORMER_DIR, folder_name)\n",
"\n",
" model.save_pretrained(local_path)\n",
" tokenizer_obj.save_pretrained(local_path)\n",
"\n",
" if os.path.exists(drive_path):\n",
" shutil.rmtree(drive_path)\n",
" shutil.copytree(local_path, drive_path)\n",
"\n",
" files = os.listdir(local_path)\n",
" size_mb = sum(os.path.getsize(os.path.join(local_path,f))\n",
" for f in files) / 1024 / 1024\n",
" print(f'✅ Saved {folder_name}/ → Drive ({size_mb:.0f} MB, {len(files)} files)')\n",
"\n",
"\n",
"# Global store for all results (classical + transformer)\n",
"ALL_RESULTS = {}\n",
"\n",
"# Load classical results from Notebook 1\n",
"classical_csv = os.path.join(CLASSICAL_DIR, 'classical_results.csv')\n",
"if os.path.exists(classical_csv):\n",
" classical_df = pd.read_csv(classical_csv)\n",
" print('✅ Classical results loaded from Drive:')\n",
" print(classical_df.to_string(index=False))\n",
"else:\n",
" classical_df = None\n",
" print('⚠️ classical_results.csv not found — run Notebook 1 first.')\n",
"\n",
"MODEL_NAME = 'xlm-roberta-base'\n",
"print(f'\\n✅ All shared functions ready. Model: {MODEL_NAME}')"
],
"id": "ZAR_9OVyMyxB"
},
{
"cell_type": "markdown",
"metadata": {
"id": "-L6O0WSvMyxB"
},
"source": [
"---\n",
"# 🤖 SECTION 1 — XLM-RoBERTa: Dataset 1 (Depression Type)\n",
"\n",
"**What is XLM-RoBERTa and why is it better than word2vec?**\n",
"\n",
"The base paper (Tumaliuan et al. 2024) used **word2vec** — a 2013 algorithm that gives every word one fixed vector regardless of context. `bank` always means the same thing whether in `river bank` or `bank account`.\n",
"\n",
"XLM-RoBERTa (2019) creates **contextual embeddings** — the same word gets a different vector depending on its surrounding words. For depression detection this matters critically:\n",
"- `I can't sleep` vs `I can sleep fine` — same words, opposite meaning\n",
"- `I'm not fine` vs `I'm fine` — XLM-RoBERTa understands negation, word2vec does not\n",
"\n",
"It was pre-trained on 2.5TB of text across 100 languages. We **fine-tune** it — meaning we take those pre-learned language patterns and train them further on our specific depression dataset.\n",
"\n",
"**Upload `dataset.csv` before running this section.**"
],
"id": "-L6O0WSvMyxB"
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "xx4mqz_RMyxB",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "96cbbd15-0411-4db8-8716-2be1dc969471"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"D1 loaded: 14,983 rows\n",
"Label encoder loaded: ['atypical', 'bipolar', 'major depressive', 'no', 'postpartum', 'psychotic']\n",
"Split: 11986 train / 2997 test\n",
"✅ D1 data ready for XLM-RoBERTa.\n"
]
}
],
"source": [
"# ─────────────────────────────────────────────────────────────────\n",
"# SECTION 1 STEP 1 — Load D1 data + label encoder from Drive\n",
"# ─────────────────────────────────────────────────────────────────\n",
"df1 = pd.read_csv('DA_1_DB.csv')\n",
"df1 = df1.dropna(subset=['Tweets','Labels'])\n",
"df1 = df1[df1['Labels'].str.strip() != ''].reset_index(drop=True)\n",
"print(f'D1 loaded: {len(df1):,} rows')\n",
"\n",
"# Load the label encoder saved in Notebook 1\n",
"le1 = joblib.load(os.path.join(CLASSICAL_DIR, 'le_d1.pkl'))\n",
"print(f'Label encoder loaded: {list(le1.classes_)}')\n",
"\n",
"# Clean and encode\n",
"df1['clean'] = df1['Tweets'].apply(clean_text)\n",
"df1['label_enc'] = le1.transform(df1['Labels'])\n",
"\n",
"X1 = df1['clean']\n",
"y1 = df1['label_enc']\n",
"\n",
"# Same 80/20 split — MUST use same random_state=42 as Notebook 1\n",
"# so test set is identical and results are directly comparable\n",
"X1_tr, X1_te, y1_tr, y1_te = train_test_split(\n",
" X1, y1, test_size=0.2, stratify=y1, random_state=42\n",
")\n",
"print(f'Split: {len(X1_tr)} train / {len(X1_te)} test')\n",
"print('✅ D1 data ready for XLM-RoBERTa.')"
],
"id": "xx4mqz_RMyxB"
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "w79JIP6AMyxC",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 350,
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},
"outputId": "424f1a7c-bfbe-45f0-8814-47b09b22726c"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Loading tokeniser...\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"config.json: 0%| | 0.00/615 [00:00"
],
"text/html": [
"\n",
" \n",
" \n",
"
\n",
" [2250/2250 10:18, Epoch 3/3]\n",
"
\n",
" \n",
" \n",
" | Epoch | \n",
" Training Loss | \n",
" Validation Loss | \n",
"
\n",
" \n",
" \n",
" \n",
" | 1 | \n",
" 0.247780 | \n",
" 0.245682 | \n",
"
\n",
" \n",
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\n",
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"metadata": {}
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{
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"data": {
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],
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},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"=======================================================\n",
" XLM-RoBERTa [D1]\n",
"=======================================================\n",
" Accuracy : 90.52%\n",
" Macro F1 : 0.9117 ← main metric\n",
" Cohen's Kappa: 0.8852\n",
"=======================================================\n",
"\n",
" precision recall f1-score support\n",
"\n",
" atypical 0.997 0.987 0.992 396\n",
" bipolar 0.923 0.961 0.942 489\n",
"major depressive 0.752 0.841 0.794 504\n",
" no 0.995 0.967 0.981 397\n",
" postpartum 0.947 0.866 0.905 749\n",
" psychotic 0.857 0.855 0.856 462\n",
"\n",
" accuracy 0.905 2997\n",
" macro avg 0.912 0.913 0.912 2997\n",
" weighted avg 0.910 0.905 0.906 2997\n",
"\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Writing model shards: 0%| | 0/1 [00:00"
],
"text/html": [
"\n",
" \n",
" \n",
"
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" [774/774 07:38, Epoch 3/3]\n",
"
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" \n",
" \n",
" | Epoch | \n",
" Training Loss | \n",
" Validation Loss | \n",
"
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" \n",
" \n",
" \n",
" | 1 | \n",
" 0.053907 | \n",
" 0.007455 | \n",
"
\n",
" \n",
" | 2 | \n",
" 0.016556 | \n",
" 0.004306 | \n",
"
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" \n",
" | 3 | \n",
" 0.000532 | \n",
" 0.001125 | \n",
"
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" \n",
"
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"metadata": {}
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{
"output_type": "display_data",
"data": {
"text/plain": [
"Writing model shards: 0%| | 0/1 [00:00"
],
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},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"=======================================================\n",
" XLM-RoBERTa [D2]\n",
"=======================================================\n",
" Accuracy : 99.95%\n",
" Macro F1 : 0.9993 ← main metric\n",
" Cohen's Kappa: 0.9986\n",
"=======================================================\n",
"\n",
" precision recall f1-score support\n",
"\n",
" 0 0.999 1.000 1.000 1600\n",
" 1 1.000 0.998 0.999 463\n",
"\n",
" accuracy 1.000 2063\n",
" macro avg 1.000 0.999 0.999 2063\n",
"weighted avg 1.000 1.000 1.000 2063\n",
"\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Writing model shards: 0%| | 0/1 [00:00"
],
"text/html": [
"\n",
" \n",
" \n",
"
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" [7500/7500 42:22, Epoch 3/3]\n",
"
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" \n",
" \n",
" | Epoch | \n",
" Training Loss | \n",
" Validation Loss | \n",
"
\n",
" \n",
" \n",
" \n",
" | 1 | \n",
" 0.171100 | \n",
" 0.123076 | \n",
"
\n",
" \n",
" | 2 | \n",
" 0.127004 | \n",
" 0.090145 | \n",
"
\n",
" \n",
" | 3 | \n",
" 0.056179 | \n",
" 0.110482 | \n",
"
\n",
" \n",
"
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},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Writing model shards: 0%| | 0/1 [00:00"
],
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"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"=======================================================\n",
" XLM-RoBERTa [D3]\n",
"=======================================================\n",
" Accuracy : 98.10%\n",
" Macro F1 : 0.9810 ← main metric\n",
" Cohen's Kappa: 0.9620\n",
"=======================================================\n",
"\n",
" precision recall f1-score support\n",
"\n",
" non-suicide 0.980 0.982 0.981 5000\n",
" suicide 0.982 0.980 0.981 5000\n",
"\n",
" accuracy 0.981 10000\n",
" macro avg 0.981 0.981 0.981 10000\n",
"weighted avg 0.981 0.981 0.981 10000\n",
"\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Writing model shards: 0%| | 0/1 [00:00"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Chart saved: /content/drive/MyDrive/MindScan_Models/transformers/full_comparison.png\n"
]
}
],
"source": [
"# ─────────────────────────────────────────────────────────────────\n",
"# SECTION 4.2 — Full comparison chart (all 5 models, all 3 datasets)\n",
"#\n",
"# This is the headline chart for your paper and presentation.\n",
"# XLM-RoBERTa bar is purple, classical models are grey/blue/teal.\n",
"# Red dashed line = Tumaliuan et al. 2024 benchmark (F1=0.81).\n",
"# ─────────────────────────────────────────────────────────────────\n",
"\n",
"model_order = ['Logistic Regression','Random Forest','SVM','XGBoost','XLM-RoBERTa']\n",
"model_colors = {\n",
" 'Logistic Regression': '#94a3b8',\n",
" 'Random Forest': '#64748b',\n",
" 'SVM': '#6c8fff',\n",
" 'XGBoost': '#2dd4bf',\n",
" 'XLM-RoBERTa': '#a78bfa',\n",
"}\n",
"\n",
"datasets = master['Dataset'].unique()\n",
"fig, axes = plt.subplots(1, 3, figsize=(18, 6))\n",
"fig.suptitle('All 5 Models × 3 Datasets — Macro F1 Comparison\\n(purple = XLM-RoBERTa, red dashed = Tumaliuan 2024 benchmark)',\n",
" fontsize=13, fontweight='bold')\n",
"\n",
"for ax, ds in zip(axes, datasets):\n",
" ds_data = master[master['Dataset'] == ds].set_index('Model')\n",
" models = [m for m in model_order if m in ds_data.index]\n",
" f1s = [ds_data.loc[m,'Macro F1'] for m in models]\n",
" colors = [model_colors[m] for m in models]\n",
"\n",
" bars = ax.bar(range(len(models)), f1s, color=colors, edgecolor='none', width=0.6)\n",
"\n",
" # Value labels on bars\n",
" for bar, score in zip(bars, f1s):\n",
" ax.text(bar.get_x()+bar.get_width()/2, bar.get_height()+0.004,\n",
" f'{score:.3f}', ha='center', va='bottom', fontsize=9, fontweight='bold')\n",
"\n",
" # Benchmark line\n",
" ax.axhline(0.81, color='#ef4444', linestyle='--', linewidth=1.5,\n",
" alpha=0.7, label='Baseline (0.81)')\n",
"\n",
" ax.set_title(ds, fontsize=10, fontweight='bold')\n",
" ax.set_xticks(range(len(models)))\n",
" ax.set_xticklabels([m.replace(' ','\\n') for m in models], fontsize=8)\n",
" ax.set_ylabel('Macro F1')\n",
" ax.set_ylim(0, 1.08)\n",
" ax.grid(axis='y', alpha=0.25)\n",
" ax.legend(fontsize=8)\n",
"\n",
"plt.tight_layout()\n",
"chart_path = os.path.join(TRANSFORMER_DIR, 'full_comparison.png')\n",
"plt.savefig(chart_path, dpi=150, bbox_inches='tight')\n",
"plt.show()\n",
"print(f'Chart saved: {chart_path}')"
],
"id": "Q4b59Tr8MyxF"
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"id": "nfWYV1Y2MyxG",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 298
},
"outputId": "448f1278-80f8-4c77-f9c9-58207fa2debe"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
""
],
"image/png": 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},
"metadata": {}
}
],
"source": [
"# ─────────────────────────────────────────────────────────────────\n",
"# SECTION 4.3 — Cohen's Kappa comparison chart\n",
"#\n",
"# The assignment requires multiple metrics beyond just F1.\n",
"# This chart shows Cohen's Kappa — measures agreement beyond chance.\n",
"# A kappa of 0 means no better than random; 1.0 means perfect.\n",
"# ─────────────────────────────────────────────────────────────────\n",
"fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
"fig.suptitle(\"Cohen's Kappa — All 5 Models × 3 Datasets\",\n",
" fontsize=13, fontweight='bold')\n",
"\n",
"for ax, ds in zip(axes, datasets):\n",
" ds_data = master[master['Dataset'] == ds].set_index('Model')\n",
" models = [m for m in model_order if m in ds_data.index]\n",
" kappas = [ds_data.loc[m,\"Cohen's Kappa\"] for m in models]\n",
" colors = [model_colors[m] for m in models]\n",
"\n",
" bars = ax.bar(range(len(models)), kappas, color=colors, edgecolor='none', width=0.6)\n",
" for bar, k in zip(bars, kappas):\n",
" ax.text(bar.get_x()+bar.get_width()/2, bar.get_height()+0.004,\n",
" f'{k:.3f}', ha='center', fontsize=9, fontweight='bold')\n",
"\n",
" ax.set_title(ds, fontsize=10, fontweight='bold')\n",
" ax.set_xticks(range(len(models)))\n",
" ax.set_xticklabels([m.replace(' ','\\n') for m in models], fontsize=8)\n",
" ax.set_ylabel(\"Cohen's Kappa\")\n",
" ax.set_ylim(0, 1.05)\n",
" ax.grid(axis='y', alpha=0.25)\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig(os.path.join(TRANSFORMER_DIR, 'kappa_comparison.png'), dpi=150, bbox_inches='tight')\n",
"plt.show()"
],
"id": "nfWYV1Y2MyxG"
},
{
"cell_type": "markdown",
"metadata": {
"id": "iyYKrA40MyxG"
},
"source": [
"---\n",
"# 🔌 SECTION 5 — predict_all() — The UI Function\n",
"\n",
"This is the function the UI calls when a user submits text. It:\n",
"1. Runs through all 5 models on all 3 datasets (15 predictions total)\n",
"2. Returns every model's prediction + confidence\n",
"3. Identifies the winner per dataset (highest confidence)\n",
"4. Flags suicide risk if majority of D3 models agree\n",
"\n",
"**In the UI** — all 5 model results are shown side by side so users can see the model comparison simulation. The winner is highlighted with a star."
],
"id": "iyYKrA40MyxG"
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"id": "UAeNMf7IMyxG",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 234,
"referenced_widgets": [
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},
"outputId": "3f26f239-ef9a-4cfc-afcf-d522d098f3fe"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Loading classical models from Drive...\n",
"✅ Classical models loaded.\n",
"\n",
"Loading XLM-RoBERTa models...\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Loading weights: 0%| | 0/201 [00:00= 3\n",
"\n",
" return {\n",
" 'dataset1': {\n",
" 'task': 'Depression Type',\n",
" 'classes': list(le1.classes_),\n",
" 'models': d1,\n",
" 'winner_model': d1_win[0],\n",
" 'winner_prediction': d1_win[1]['label'],\n",
" 'winner_confidence': d1_win[1]['confidence'],\n",
" 'class_probs': d1['XLM-RoBERTa'].get('all_probs', {})\n",
" },\n",
" 'dataset2': {\n",
" 'task': 'Depressed?',\n",
" 'models': d2,\n",
" 'winner_model': d2_win[0],\n",
" 'winner_prediction': d2_win[1]['label'],\n",
" 'winner_confidence': d2_win[1]['confidence'],\n",
" },\n",
" 'dataset3': {\n",
" 'task': 'Suicide Risk',\n",
" 'models': d3,\n",
" 'winner_model': d3_win[0],\n",
" 'winner_prediction': d3_win[1]['label'],\n",
" 'winner_confidence': d3_win[1]['confidence'],\n",
" },\n",
" 'risk_flag': risk_flag,\n",
" 'suicide_votes': f'{suicide_count}/5 models flagged suicide risk',\n",
" 'winner_summary': {\n",
" 'depression_type': f\"{d1_win[1]['label']} ({d1_win[1]['confidence']*100:.1f}% — {d1_win[0]})\",\n",
" 'depressed': f\"{d2_win[1]['label']} ({d2_win[1]['confidence']*100:.1f}% — {d2_win[0]})\",\n",
" 'suicide_risk': f\"{d3_win[1]['label']} ({d3_win[1]['confidence']*100:.1f}% — {d3_win[0]})\",\n",
" }\n",
" }\n",
"\n",
"\n",
"print('✅ predict_all() ready!')"
],
"id": "SgAN-wBHMyxH"
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"id": "O8q8ZD8YMyxI",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "8892e5d2-ed80-4f0a-d937-936abad54c79"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
"SAMPLE 1: I been going through depression after having my baby. My body and mind have...\n",
"━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
"\n",
" WINNERS (highest confidence per dataset):\n",
" depression_type : postpartum (99.9% — XGBoost)\n",
" depressed : 1 (100.0% — XLM-RoBERTa)\n",
" suicide_risk : non-suicide (99.6% — XLM-RoBERTa)\n",
"\n",
" All 5 models — Depression Type:\n",
" Logistic Regression : postpartum 95.8%\n",
" Random Forest : postpartum 75.7%\n",
" SVM : postpartum 82.8%\n",
" XGBoost : postpartum 99.9% ★ WINNER\n",
" XLM-RoBERTa : postpartum 99.7%\n",
"\n",
" All 5 models — Depressed?:\n",
" Logistic Regression : 1 92.2%\n",
" Random Forest : 1 69.7%\n",
" SVM : 1 95.6%\n",
" XGBoost : 1 99.9%\n",
" XLM-RoBERTa : 1 100.0% ★ WINNER\n",
"\n",
" All 5 models — Suicide Risk:\n",
" Logistic Regression : non-suicide 51.4%\n",
" Random Forest : non-suicide 68.3%\n",
" SVM : suicide 62.1%\n",
" XGBoost : non-suicide 72.2%\n",
" XLM-RoBERTa : non-suicide 99.6% ★ WINNER\n",
"\n",
" Suicide risk: ✓ No flag (1/5 models flagged suicide risk)\n",
"\n",
" XLM-RoBERTa probability breakdown (Depression Type):\n",
" postpartum 99.7% ███████████████████████████\n",
" bipolar 0.1% \n",
" major depressive 0.1% \n",
" psychotic 0.1% \n",
" atypical 0.0% \n",
" no 0.0% \n",
"\n",
"━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
"SAMPLE 2: The universe is sending me signals I am NOT supposed to follow. It's the ps...\n",
"━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
"\n",
" WINNERS (highest confidence per dataset):\n",
" depression_type : psychotic (99.4% — XLM-RoBERTa)\n",
" depressed : 0 (100.0% — XLM-RoBERTa)\n",
" suicide_risk : non-suicide (99.5% — XLM-RoBERTa)\n",
"\n",
" All 5 models — Depression Type:\n",
" Logistic Regression : psychotic 32.3%\n",
" Random Forest : no 31.7%\n",
" SVM : psychotic 26.0%\n",
" XGBoost : psychotic 91.6%\n",
" XLM-RoBERTa : psychotic 99.4% ★ WINNER\n",
"\n",
" All 5 models — Depressed?:\n",
" Logistic Regression : 0 85.2%\n",
" Random Forest : 0 82.7%\n",
" SVM : 0 94.4%\n",
" XGBoost : 0 99.2%\n",
" XLM-RoBERTa : 0 100.0% ★ WINNER\n",
"\n",
" All 5 models — Suicide Risk:\n",
" Logistic Regression : non-suicide 75.0%\n",
" Random Forest : non-suicide 87.0%\n",
" SVM : non-suicide 62.5%\n",
" XGBoost : non-suicide 87.1%\n",
" XLM-RoBERTa : non-suicide 99.5% ★ WINNER\n",
"\n",
" Suicide risk: ✓ No flag (0/5 models flagged suicide risk)\n",
"\n",
" XLM-RoBERTa probability breakdown (Depression Type):\n",
" psychotic 99.4% ███████████████████████████\n",
" postpartum 0.2% \n",
" major depressive 0.2% \n",
" no 0.1% \n",
" atypical 0.1% \n",
" bipolar 0.1% \n",
"\n",
"━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
"SAMPLE 3: I can't take this anymore. I feel completely worthless. Everyone would be b...\n",
"━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
"\n",
" WINNERS (highest confidence per dataset):\n",
" depression_type : postpartum (81.4% — XLM-RoBERTa)\n",
" depressed : 0 (100.0% — XLM-RoBERTa)\n",
" suicide_risk : suicide (98.7% — Logistic Regression)\n",
"\n",
" All 5 models — Depression Type:\n",
" Logistic Regression : postpartum 45.4%\n",
" Random Forest : postpartum 39.7%\n",
" SVM : postpartum 31.8%\n",
" XGBoost : postpartum 77.3%\n",
" XLM-RoBERTa : postpartum 81.4% ★ WINNER\n",
"\n",
" All 5 models — Depressed?:\n",
" Logistic Regression : 0 69.2%\n",
" Random Forest : 0 80.0%\n",
" SVM : 0 77.4%\n",
" XGBoost : 0 99.6%\n",
" XLM-RoBERTa : 0 100.0% ★ WINNER\n",
"\n",
" All 5 models — Suicide Risk:\n",
" Logistic Regression : suicide 98.7% ★ WINNER\n",
" Random Forest : suicide 72.0%\n",
" SVM : suicide 97.7%\n",
" XGBoost : suicide 97.1%\n",
" XLM-RoBERTa : suicide 97.7%\n",
"\n",
" Suicide risk: ⚠️ HIGH RISK (5/5 models flagged suicide risk)\n",
"\n",
" XLM-RoBERTa probability breakdown (Depression Type):\n",
" postpartum 81.4% ██████████████████████\n",
" major depressive 13.8% ███\n",
" psychotic 3.4% \n",
" bipolar 0.9% \n",
" no 0.3% \n",
" atypical 0.2% \n",
"\n",
"━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
"SAMPLE 4: Had an amazing hike today! The sunset was unreal. Feeling so grateful and a...\n",
"━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\n",
"\n",
" WINNERS (highest confidence per dataset):\n",
" depression_type : no (92.6% — XGBoost)\n",
" depressed : 0 (100.0% — XLM-RoBERTa)\n",
" suicide_risk : non-suicide (100.0% — XLM-RoBERTa)\n",
"\n",
" All 5 models — Depression Type:\n",
" Logistic Regression : no 59.8%\n",
" Random Forest : no 68.7%\n",
" SVM : no 38.8%\n",
" XGBoost : no 92.6% ★ WINNER\n",
" XLM-RoBERTa : postpartum 41.0%\n",
"\n",
" All 5 models — Depressed?:\n",
" Logistic Regression : 0 83.4%\n",
" Random Forest : 0 95.0%\n",
" SVM : 0 79.4%\n",
" XGBoost : 0 99.7%\n",
" XLM-RoBERTa : 0 100.0% ★ WINNER\n",
"\n",
" All 5 models — Suicide Risk:\n",
" Logistic Regression : non-suicide 74.8%\n",
" Random Forest : non-suicide 84.9%\n",
" SVM : suicide 51.2%\n",
" XGBoost : non-suicide 73.4%\n",
" XLM-RoBERTa : non-suicide 100.0% ★ WINNER\n",
"\n",
" Suicide risk: ✓ No flag (1/5 models flagged suicide risk)\n",
"\n",
" XLM-RoBERTa probability breakdown (Depression Type):\n",
" postpartum 41.0% ███████████\n",
" major depressive 38.0% ██████████\n",
" no 7.5% ██\n",
" psychotic 6.7% █\n",
" atypical 3.5% \n",
" bipolar 3.3% \n"
]
}
],
"source": [
"# ─────────────────────────────────────────────────────────────────\n",
"# SECTION 5.4 — Test predict_all() on 4 sample inputs\n",
"# ─────────────────────────────────────────────────────────────────\n",
"\n",
"SAMPLES = [\n",
" \"I been going through depression after having my baby. My body and mind have completely changed. I need time.\",\n",
" \"The universe is sending me signals I am NOT supposed to follow. It's the psychosis talking. I know. But it feels real.\",\n",
" \"I can't take this anymore. I feel completely worthless. Everyone would be better off without me. I've been thinking about ending it all.\",\n",
" \"Had an amazing hike today! The sunset was unreal. Feeling so grateful and alive.\"\n",
"]\n",
"\n",
"for i, text in enumerate(SAMPLES, 1):\n",
" print(f'\\n{\"━\"*65}')\n",
" print(f'SAMPLE {i}: {text[:75]}...' if len(text) > 75 else f'SAMPLE {i}: {text}')\n",
" print('━'*65)\n",
"\n",
" out = predict_all(text)\n",
"\n",
" print(f'\\n WINNERS (highest confidence per dataset):')\n",
" for k, v in out['winner_summary'].items():\n",
" print(f' {k:<18}: {v}')\n",
"\n",
" for ds_key, ds_label in [('dataset1','Depression Type'), ('dataset2','Depressed?'), ('dataset3','Suicide Risk')]:\n",
" print(f'\\n All 5 models — {ds_label}:')\n",
" for model, res in out[ds_key]['models'].items():\n",
" is_winner = model == out[ds_key]['winner_model']\n",
" star = ' ★ WINNER' if is_winner else ''\n",
" print(f' {model:<22}: {res[\"label\"]:<20} {res[\"confidence\"]*100:5.1f}%{star}')\n",
"\n",
" # Risk flag\n",
" flag = '⚠️ HIGH RISK' if out['risk_flag'] else '✓ No flag'\n",
" print(f'\\n Suicide risk: {flag} ({out[\"suicide_votes\"]})')\n",
"\n",
" # Class probability breakdown from XLM-RoBERTa\n",
" if out['dataset1']['class_probs']:\n",
" print(f'\\n XLM-RoBERTa probability breakdown (Depression Type):')\n",
" for cls, prob in sorted(out['dataset1']['class_probs'].items(), key=lambda x: -x[1]):\n",
" bar = '█' * int(prob * 28)\n",
" print(f' {cls:<22} {prob*100:5.1f}% {bar}')"
],
"id": "O8q8ZD8YMyxI"
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"id": "s4x3VmuDMyxI",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0cc334ab-7ec8-42dd-8aa4-72233798d43e"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"=== FINAL FILE CHECKLIST ===\n",
"\n",
"Classical models (Drive/MindScan_Models/classical/):\n",
" ✅ classical_comparison.png 0.1 MB\n",
" ✅ classical_results.csv 0.0 MB\n",
" ✅ cm_D1_Logistic_Regression.png 0.1 MB\n",
" ✅ cm_D1_Random_Forest.png 0.1 MB\n",
" ✅ cm_D1_SVM.png 0.1 MB\n",
" ✅ cm_D1_XGBoost.png 0.1 MB\n",
" ✅ cm_D2_Logistic_Regression.png 0.0 MB\n",
" ✅ cm_D2_Random_Forest.png 0.0 MB\n",
" ✅ cm_D2_SVM.png 0.0 MB\n",
" ✅ cm_D2_XGBoost.png 0.0 MB\n",
" ✅ cm_D3_Logistic_Regression.png 0.0 MB\n",
" ✅ cm_D3_Random_Forest.png 0.0 MB\n",
" ✅ cm_D3_SVM.png 0.0 MB\n",
" ✅ cm_D3_XGBoost.png 0.0 MB\n",
" ✅ eda_d1.png 0.1 MB\n",
" ✅ eda_d2.png 0.1 MB\n",
" ✅ eda_d3.png 0.1 MB\n",
" ✅ le_d1.pkl 0.0 MB\n",
" ✅ le_d2.pkl 0.0 MB\n",
" ✅ le_d3.pkl 0.0 MB\n",
" ✅ logistic_regression_d1.pkl 1.6 MB\n",
" ✅ logistic_regression_d2.pkl 0.1 MB\n",
" ✅ logistic_regression_d3.pkl 0.5 MB\n",
" ✅ random_forest_d1.pkl 240.4 MB\n",
" ✅ random_forest_d2.pkl 71.7 MB\n",
" ✅ random_forest_d3.pkl 333.9 MB\n",
" ✅ svm_d1.pkl 1.6 MB\n",
" ✅ svm_d2.pkl 0.1 MB\n",
" ✅ svm_d3.pkl 0.5 MB\n",
" ✅ tfidf_d1.pkl 1.3 MB\n",
" ✅ tfidf_d2.pkl 0.6 MB\n",
" ✅ tfidf_d3.pkl 2.2 MB\n",
" ✅ xgboost_d1.pkl 3.0 MB\n",
" ✅ xgboost_d2.pkl 0.4 MB\n",
" ✅ xgboost_d3.pkl 0.7 MB\n",
"\n",
"Transformer models (Drive/MindScan_Models/transformers/):\n",
" ✅ cm_D1_XLM-RoBERTa.png 0.1 MB\n",
" ✅ cm_D2_XLM-RoBERTa.png 0.0 MB\n",
" ✅ cm_D3_XLM-RoBERTa.png 0.0 MB\n",
" ✅ full_comparison.png 0.1 MB\n",
" ✅ full_results.csv 0.0 MB\n",
" ✅ kappa_comparison.png 0.1 MB\n",
" ✅ xlmr_d1_final/ (4 files, 1077 MB)\n",
" ✅ xlmr_d2_final/ (4 files, 1077 MB)\n",
" ✅ xlmr_d3_final/ (4 files, 1077 MB)\n",
"\n",
"=== NEXT STEP ===\n",
"1. Download MindScan_Models/ folder from Google Drive\n",
"2. Put it in your VS Code project folder\n",
"3. Build the Flask/FastAPI backend that calls predict_all()\n",
"4. Connect it to mental_health_ui.html\n",
"\n",
"✅ Notebook 2 complete!\n"
]
}
],
"source": [
"# ─────────────────────────────────────────────────────────────────\n",
"# SECTION 5.5 — Final file checklist + export summary\n",
"# ─────────────────────────────────────────────────────────────────\n",
"\n",
"print('=== FINAL FILE CHECKLIST ===')\n",
"print()\n",
"print('Classical models (Drive/MindScan_Models/classical/):')\n",
"for f in sorted(os.listdir(CLASSICAL_DIR)):\n",
" size = os.path.getsize(os.path.join(CLASSICAL_DIR, f)) / 1024 / 1024\n",
" print(f' ✅ {f:<42} {size:5.1f} MB')\n",
"\n",
"print()\n",
"print('Transformer models (Drive/MindScan_Models/transformers/):')\n",
"for item in sorted(os.listdir(TRANSFORMER_DIR)):\n",
" item_path = os.path.join(TRANSFORMER_DIR, item)\n",
" if os.path.isdir(item_path):\n",
" files = os.listdir(item_path)\n",
" size = sum(os.path.getsize(os.path.join(item_path,f))\n",
" for f in files) / 1024 / 1024\n",
" print(f' ✅ {item}/ ({len(files)} files, {size:.0f} MB)')\n",
" else:\n",
" size = os.path.getsize(item_path) / 1024 / 1024\n",
" print(f' ✅ {item:<42} {size:5.1f} MB')\n",
"\n",
"print()\n",
"print('=== NEXT STEP ===')\n",
"print('1. Download MindScan_Models/ folder from Google Drive')\n",
"print('2. Put it in your VS Code project folder')\n",
"print('3. Build the Flask/FastAPI backend that calls predict_all()')\n",
"print('4. Connect it to mental_health_ui.html')\n",
"print()\n",
"print('✅ Notebook 2 complete!')"
],
"id": "s4x3VmuDMyxI"
}
]
}