diff --git a/rewrite_training.py b/rewrite_training.py
new file mode 100644
index 0000000..8e46a5e
--- /dev/null
+++ b/rewrite_training.py
@@ -0,0 +1,112 @@
+import os
+import polars as pl
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from concurrent.futures import ProcessPoolExecutor
+from tqdm import tqdm
+
+# --- CONFIGURATION ---
+BASE_PATH = "asl_kaggle"
+TARGET_FRAMES = 22
+# Hand landmarks + Lip landmarks (approximate indices for high-value face points)
+LIPS = [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, 308, 324, 318, 402, 317, 14, 87, 178, 88, 95]
+HANDS = list(range(468, 543))
+SELECTED_INDICES = LIPS + HANDS
+NUM_FEATS = len(SELECTED_INDICES) * 3  # X, Y, Z for each selected point
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+print(f"Using device: {device}")
+
+# --- DATA PROCESSING ---
+
+def load_kaggle_metadata(base_path):
+    return pl.read_csv(os.path.join(base_path, "train.csv"))
+
+
+def load_and_preprocess(path, base_path=BASE_PATH, target_frames=TARGET_FRAMES):
+    parquet_path = os.path.join(base_path, path)
+    df = pl.read_parquet(parquet_path)
+
+    # 1. Spatial Normalization (Nose Anchor)
+    anchors = (
+        df.filter((pl.col("type") == "face") & (pl.col("landmark_index") == 0))
+        .select([pl.col("frame"), pl.col("x").alias("nx"), pl.col("y").alias("ny"), pl.col("z").alias("nz")])
+    )
+
+    processed = (
+        df.join(anchors, on="frame", how="left")
+        .with_columns([
+            (pl.col("x") - pl.col("nx")).fill_null(0.0),
+            (pl.col("y") - pl.col("ny")).fill_null(0.0),
+            (pl.col("z") - pl.col("nz")).fill_null(0.0),
+        ])
+        .sort(["frame", "type", "landmark_index"])
+    )
+
+    # 2. Reshape & Feature Selection
+    # Get unique frames and total landmarks (543)
+    raw_tensor = processed.select(["x", "y", "z"]).to_numpy().reshape(-1, 543, 3)
+
+    # Slice to keep only Hands and Lips
+    reduced_tensor = raw_tensor[:, SELECTED_INDICES, :]
+
+    # 3. Temporal Normalization (Resample to fixed frame count)
+    curr_len = reduced_tensor.shape[0]
+    indices = np.linspace(0, curr_len - 1, num=target_frames).round().astype(int)
+    return reduced_tensor[indices]
+
+
+# --- MODEL ARCHITECTURE ---
+
+class ASLClassifier(nn.Module):
+    def __init__(self, num_classes, target_frames=TARGET_FRAMES, num_feats=NUM_FEATS):
+        super().__init__()
+        self.conv1 = nn.Conv1d(num_feats, 256, kernel_size=3, padding=1)
+        self.bn1 = nn.BatchNorm1d(256)
+        self.conv2 = nn.Conv1d(256, 512, kernel_size=3, padding=1)
+        self.bn2 = nn.BatchNorm1d(512)
+        self.pool = nn.MaxPool1d(2)
+        self.dropout = nn.Dropout(0.5)
+        self.fc = nn.Linear(512, num_classes)
+
+    def forward(self, x):
+        # x: (Batch, Frames, Selected_Landmarks, 3)
+        x = x.view(x.shape[0], x.shape[1], -1)  # Flatten landmarks/coords
+        x = x.transpose(1, 2)  # (Batch, Features, Time)
+
+        x = F.relu(self.bn1(self.conv1(x)))
+        x = self.pool(x)
+        x = F.relu(self.bn2(self.conv2(x)))
+        x = self.pool(x)
+
+        x = F.adaptive_avg_pool1d(x, 1).squeeze(-1)
+        x = self.dropout(x)
+        return self.fc(x)
+
+
+# --- EXECUTION ---
+
+if __name__ == "__main__":
+    asl_data = load_kaggle_metadata(BASE_PATH)
+
+    # Optimization: Process 100 samples to get a feel for the shape/speed
+    # Using multiprocessing to avoid the slow single-thread loop
+    paths = asl_data["path"].to_list()
+
+    print(f"Processing {len(paths)} files in parallel...")
+    with ProcessPoolExecutor() as executor:
+        results = list(tqdm(executor.map(load_and_preprocess, paths), total=len(paths)))
+
+    # Stack into one giant Torch tensor
+    dataset_tensor = torch.tensor(np.array(results), dtype=torch.float32)
+    print(f"Final Tensor Shape: {dataset_tensor.shape}")
+    # Shape: (100, 22, 96, 3) -> (Batch, Time, Landmarks, Coords)
+
+    # Initialize Model
+    num_unique_signs = asl_data["sign"].n_unique()
+    model = ASLClassifier(num_classes=num_unique_signs)
+    model.to(device)
+    # Test pass
+    output = model(dataset_tensor)
+    print(f"Model Output Shape: {output.shape}")  # (100, 250)
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