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app.py

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+import torch
+import torch.nn as nn
+from fastapi import FastAPI
+import numpy as np
+
+# Định nghĩa lại mô hình
+class TransformerModel(nn.Module):
+    def __init__(self, input_dim, d_model=64, nhead=4, num_layers=2):
+        super(TransformerModel, self).__init__()
+        self.input_fc = nn.Linear(input_dim, d_model)
+        self.transformer = nn.TransformerEncoder(
+            nn.TransformerEncoderLayer(d_model, nhead, batch_first=True), num_layers
+        )
+        self.fc_signal = nn.Linear(d_model, 2)
+        self.fc_tp = nn.Linear(d_model, 1)
+
+    def forward(self, x):
+        x = self.input_fc(x)
+        x = self.transformer(x)
+        signal = torch.softmax(self.fc_signal(x[:, -1, :]), dim=-1)
+        tp = self.fc_tp(x[:, -1, :])
+        return signal, tp
+
+# Khởi tạo mô hình
+input_dim = 7
+model = TransformerModel(input_dim)
+model.load_state_dict(torch.load("tradingbot_model.pth"))
+model.eval()
+
+app = FastAPI()
+
+@app.post("/predict")
+async def predict(inputs: list):
+    inputs = torch.FloatTensor(inputs).unsqueeze(1)  # Thêm chiều batch
+    with torch.no_grad():
+        signal_prob, tp_pred = model(inputs)
+        signal = torch.argmax(signal_prob).item()
+        tp = tp_pred.item()
+    return {"signal": signal, "tp": tp}