Deploy NBA predictor model

model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ae3e35b43ef43691a02474f97e68538a6b3bef0a2b0d6c68666eb1ce7292426f
+size 3351857

model.py

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+# model.py
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class LSTMModel(nn.Module):
+    def __init__(self, input_size):
+        super(LSTMModel, self).__init__()
+        hidden1 = 64
+        hidden2 = 256
+        self.lstm1 = nn.LSTM(input_size=input_size, hidden_size=hidden1, batch_first=True, dropout=0.2, bidirectional=True)
+        self.ln1 = nn.LayerNorm(hidden1 * 2)
+        self.lstm2 = nn.LSTM(input_size=hidden1 * 2, hidden_size=hidden2, batch_first=True, dropout=0.2, bidirectional=True)
+        self.ln2 = nn.LayerNorm(hidden2 * 2)
+        self.fc = nn.Linear(hidden2 * 2, 1)
+
+    def forward(self, x):
+        x, _ = self.lstm1(x)
+        x = self.ln1(x)
+        x = F.relu(x)
+        x, _ = self.lstm2(x)
+        x = self.ln2(x)
+        x = F.relu(x)
+        out = self.fc(x[:, -1, :])
+        return out

requirements.txt

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+torch
+gradio
+numpy
+scikit-learn    
+pandas