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inference.py

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+import pickle
+import pandas as pd
+
+# Load the ARIMA model
+with open("arima_model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+def predict_stock(input_data):
+    """
+    input_data: dictionary with structure like {"feature": value}
+    For ARIMA, usually you just forecast next n steps.
+    """
+    # Example: forecasting next n steps
+    n_steps = input_data.get("steps", 5)
+    forecast = model.forecast(steps=n_steps)
+    return forecast.tolist()
+
+# Example usage
+if __name__ == "__main__":
+    data = {"steps": 10}
+    print(predict_stock(data))