dd

test.py

@@ -1,33 +0,0 @@
-from huggingface_hub import hf_hub_download
-import tensorflow as tf
-from tensorflow import keras
-
-# Replace 'your-username/your-model-name' with your actual Hugging Face model repository ID.
-repo_id = "your-username/your-model-name"
-# Replace 'my_keras_model.keras' with the name of the file you uploaded.
-filename = "my_keras_model.keras"
-
-# Download the model file from the Hugging Face Hub.
-model_path = hf_hub_download(repo_id=repo_id, filename=filename)
-
-# Load the model using Keras's built-in function.
-# The 'safe_mode=False' argument is often necessary when loading models saved from older TensorFlow versions
-# or if the model contains custom layers.
-model = keras.models.load_model(model_path, safe_mode=False)
-
-# Now you can use the loaded model for inference.
-# Example: Load a single MNIST test image and make a prediction.
-(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
-x_test = x_test.astype("float32") / 255.0
-x_test = tf.expand_dims(x_test, -1)
-image_to_predict = x_test[0:1]
-
-# Get the model's prediction.
-predictions = model.predict(image_to_predict)
-
-# Print the predicted class (the one with the highest probability).
-predicted_class = tf.argmax(predictions[0]).numpy()
-print(f"Predicted class: {predicted_class}")
-
-# Display the model summary to confirm it's loaded correctly.
-model.summary()