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   "cell_type": "markdown",
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    "tags": []
   },
   "source": [
    "# Bi-LSTM Emotion Classification"
   ]
  },
  {
   "cell_type": "code",
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   "id": "15aa58bc",
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    "execution": {
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   "outputs": [],
   "source": [
    "!pip install -q wandb huggingface_hub transformers torch scikit-learn pandas numpy"
   ]
  },
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   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.optim import AdamW\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from transformers import AutoTokenizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import f1_score\n",
    "import wandb\n",
    "from huggingface_hub import login\n",
    "from kaggle_secrets import UserSecretsClient\n",
    "\n",
    "# Secrets\n",
    "user_secrets = UserSecretsClient()\n",
    "wandb.login(key=user_secrets.get_secret(\"wandb_api_key\"))\n",
    "login(token=user_secrets.get_secret(\"hf_api_key\"))\n",
    "\n",
    "# Config\n",
    "PROJECT = \"emotion-classification-dl\"\n",
    "RUN_NAME = \"simple-bilstm-scratch\"\n",
    "MODEL_NAME = \"emotion-classifier-bilstm\"\n",
    "LABELS = [\"anger\", \"fear\", \"joy\", \"sadness\", \"surprise\"]\n",
    "MAX_LEN = 100\n",
    "BATCH_SIZE = 32\n",
    "EPOCHS = 10\n",
    "LR = 1e-3\n",
    "EMBED_DIM = 100\n",
    "HIDDEN_DIM = 128\n",
    "DROPOUT = 0.3\n",
    "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# Seed\n",
    "torch.manual_seed(42)\n",
    "np.random.seed(42)"
   ]
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   "source": [
    "class EmotionDS(Dataset):\n",
    "    def __init__(self, df, tokenizer):\n",
    "        self.texts = df['text'].tolist()\n",
    "        self.labels = df[LABELS].values.astype(np.float32)\n",
    "        self.tokenizer = tokenizer\n",
    "\n",
    "    def __len__(self): return len(self.texts)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        enc = self.tokenizer(self.texts[idx], truncation=True, padding='max_length', max_length=MAX_LEN, return_tensors='pt')\n",
    "        return {\n",
    "            'input_ids': enc['input_ids'].squeeze(0),\n",
    "            'labels': torch.tensor(self.labels[idx])\n",
    "        }\n",
    "\n",
    "class BiLSTM(nn.Module):\n",
    "    def __init__(self, vocab_size):\n",
    "        super().__init__()\n",
    "        self.embedding = nn.Embedding(vocab_size, EMBED_DIM)\n",
    "        self.lstm = nn.LSTM(EMBED_DIM, HIDDEN_DIM, batch_first=True, bidirectional=True, dropout=DROPOUT, num_layers=2)\n",
    "        self.fc = nn.Linear(HIDDEN_DIM * 2, len(LABELS))\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.embedding(x)\n",
    "        _, (hidden, _) = self.lstm(x)\n",
    "        # Concat forward and backward hidden states from last layer\n",
    "        x = torch.cat((hidden[-2], hidden[-1]), dim=1)\n",
    "        return self.fc(x)"
   ]
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   "source": [
    "def train():\n",
    "    # Data\n",
    "    df = pd.read_csv(\"/kaggle/input/2025-sep-dl-gen-ai-project/train.csv\")\n",
    "    if \"text\" not in df.columns: df = df.rename(columns={\"comment_text\": \"text\"}) # Handle column name\n",
    "    \n",
    "    train_df, val_df = train_test_split(df, test_size=0.2, random_state=42)\n",
    "    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
    "    \n",
    "    train_loader = DataLoader(EmotionDS(train_df, tokenizer), batch_size=BATCH_SIZE, shuffle=True)\n",
    "    val_loader = DataLoader(EmotionDS(val_df, tokenizer), batch_size=BATCH_SIZE)\n",
    "    \n",
    "    # Model\n",
    "    model = BiLSTM(tokenizer.vocab_size).to(DEVICE)\n",
    "    optimizer = AdamW(model.parameters(), lr=LR)\n",
    "    criterion = nn.BCEWithLogitsLoss()\n",
    "    \n",
    "    # WandB\n",
    "    wandb.init(project=PROJECT, name=RUN_NAME)\n",
    "    \n",
    "    best_f1 = 0\n",
    "    for epoch in range(EPOCHS):\n",
    "        model.train()\n",
    "        train_loss = 0\n",
    "        for batch in train_loader:\n",
    "            optimizer.zero_grad()\n",
    "            loss = criterion(model(batch['input_ids'].to(DEVICE)), batch['labels'].to(DEVICE))\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            train_loss += loss.item()\n",
    "            \n",
    "        # Validation\n",
    "        model.eval()\n",
    "        preds, targets = [], []\n",
    "        with torch.no_grad():\n",
    "            for batch in val_loader:\n",
    "                logits = model(batch['input_ids'].to(DEVICE))\n",
    "                preds.append(torch.sigmoid(logits).cpu().numpy())\n",
    "                targets.append(batch['labels'].cpu().numpy())\n",
    "        \n",
    "        preds = np.vstack(preds)\n",
    "        targets = np.vstack(targets)\n",
    "        f1 = f1_score(targets, (preds >= 0.5).astype(int), average='macro')\n",
    "        \n",
    "        print(f\"Epoch {epoch+1}: Loss {train_loss/len(train_loader):.4f}, Val F1 {f1:.4f}\")\n",
    "        wandb.log({\"loss\": train_loss/len(train_loader), \"val_f1\": f1})\n",
    "        \n",
    "        if f1 > best_f1:\n",
    "            best_f1 = f1\n",
    "            torch.save(model.state_dict(), \"model.pth\")\n",
    "            \n",
    "    wandb.finish()\n",
    "    return best_f1\n",
    "\n",
    "if os.path.exists(\"/kaggle/input/2025-sep-dl-gen-ai-project/train.csv\"):\n",
    "    train()"
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    "# Upload\n",
    "if os.path.exists(\"model.pth\"):\n",
    "    api = HfApi()\n",
    "    repo_id = f\"{api.whoami()['name']}/{MODEL_NAME}\"\n",
    "    create_repo(repo_id, exist_ok=True)\n",
    "    api.upload_file(path_or_fileobj=\"model.pth\", path_in_repo=\"pytorch_model.bin\", repo_id=repo_id)\n",
    "    print(f\"Uploaded to https://huggingface.co/{repo_id}\")"
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