Add custom handler and requirements

handler.py

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+import torch
+from chronos import ChronosPipeline
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        self.pipeline = ChronosPipeline.from_pretrained(
+            path,
+            device_map="auto",
+            torch_dtype=torch.float32,
+        )
+
+    def __call__(self, data):
+        inputs = data.get("inputs", [])
+        parameters = data.get("parameters", {})
+        prediction_length = parameters.get("prediction_length", 8)
+
+        if isinstance(inputs[0], list):
+            closes = [candle[3] for candle in inputs]
+        else:
+            closes = inputs
+
+        context = torch.tensor([closes], dtype=torch.float32)
+        forecast = self.pipeline.predict(context, prediction_length=prediction_length, num_samples=20)
+        median_forecast = forecast.median(dim=1).values[0].tolist()
+
+        return {"predictions": median_forecast}

requirements.txt

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+torch
+chronos-forecasting