Update app.py

app.py

@@ -1,38 +1,8 @@
-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']
-)
+model = from_pretrained_keras("keras-io/mobile-vit-xxs")
+prediction = model.predict(image)
+prediction = tf.squeeze(tf.round(prediction))
+print(f'The image is a {classes[(np.argmax(prediction))]}!')
 
-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()
+# The image is a sunflower!