Update app.py

app.py

@@ -1,8 +1,38 @@
+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
 
-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))]}!')
+reloaded_model = from_pretrained_keras('ShaharAdar/best-model-try')
+reloaded_model.compile(optimizer=Adam(0.00001),
+                       loss='categorical_crossentropy',
+                       metrics=['accuracy']
+)
 
-# The image is a sunflower!
+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()