Create modules/ai_engine.py

modules/ai_engine.py

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+import pandas as pd
+import torch
+from transformers import TimeSeriesTransformerModel, TimeSeriesTransformerConfig
+
+class AIEngine:
+    def __init__(self):
+        config = TimeSeriesTransformerConfig(
+            input_size=4,  # SolarGen, WindGen, Tilt, Vibration
+            output_size=1,  # Health Score
+            context_length=10,
+            prediction_length=1
+        )
+        self.model = TimeSeriesTransformerModel(config)
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.model.to(self.device)
+
+    def preprocess_data(self, df: pd.DataFrame) -> torch.Tensor:
+        features = df[["SolarGen(kWh)", "WindGen(kWh)", "Tilt(°)", "Vibration(g)"]].values
+        return torch.tensor(features, dtype=torch.float32).unsqueeze(0).to(self.device)
+
+    def predict_health(self, df: pd.DataFrame) -> pd.DataFrame:
+        input_data = self.preprocess_data(df)
+        with torch.no_grad():
+            predictions = self.model(input_data).logits.squeeze().cpu().numpy()
+        df["HealthScore"] = predictions
+        df["ML_Anomaly"] = df["HealthScore"].apply(lambda x: "Risk" if x < 0.5 else "Normal")
+        return df