Update app.py with transformer embeddings and prediction pipeline
Browse files
app.py
CHANGED
@@ -75,6 +75,7 @@ def predict_with_gpflow(model, X):
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# Get predictions
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#predict_fn = model.predict_f_compiled
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predict_fn = model.signatures["serving_default"]
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#mean, variance = predict_fn(Xnew=X_tensor)
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mean = result["output_0"].numpy() # Adjust output key names if needed
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variance = result["output_1"].numpy()
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# Get predictions
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#predict_fn = model.predict_f_compiled
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predict_fn = model.signatures["serving_default"]
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+
result = predict_fn(Xnew=X_tensor) # Pass Xnew explicitly
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#mean, variance = predict_fn(Xnew=X_tensor)
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mean = result["output_0"].numpy() # Adjust output key names if needed
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variance = result["output_1"].numpy()
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