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| import numpy as np | |
| import tensorflow as tf | |
| import gradio as gr | |
| from tensorflow.keras.optimizers import Adam | |
| from huggingface_hub import from_pretrained_keras | |
| reloaded_model = from_pretrained_keras('ShaharAdar/best-model-try') | |
| reloaded_model.compile(optimizer=Adam(0.00001), | |
| loss='categorical_crossentropy', | |
| metrics=['accuracy'] | |
| ) | |
| def classify_image(image): | |
| # Resize the image to 224x224 as expected by your model | |
| image = tf.image.resize(image, (224, 224)) | |
| # Add a batch dimension and make prediction | |
| image = tf.expand_dims(image, 0) # model expects a batch of images | |
| preds = reloaded_model.predict(image) | |
| # Assuming the output is a softmax layer, get the predicted class index | |
| predicted_class = tf.argmax(preds, axis=1).numpy()[0] | |
| # Optionally, convert class index to label if you have a mapping | |
| labels = ['Clams', 'Corals', 'Crabs', 'Dolphin', 'Eel', 'Fish', | |
| 'Jelly Fish', 'Lobster', 'Nudibranchs', 'Octopus', 'Otter', | |
| 'Penguin', 'Puffers', 'Sea Rays', 'Sea Urchins', 'Seahorse', | |
| 'Seal', 'Sharks', 'Shrimp', 'Squid', 'Starfish', | |
| 'Turtle_Tortoise', 'Whale'] # example labels | |
| return labels[predicted_class] | |
| import gradio as gr | |
| # Define the interface | |
| iface = gr.Interface(fn=classify_image, inputs="image", outputs="text") | |
| # Launch the application | |
| iface.launch() |