Update app.py

app.py

@@ -1,43 +1,42 @@
-import gradio as gr
-import tensorflow as tf
-print(tf.__version__)
-import numpy as np
-from PIL import Image
- 
-model_path = "pokemon_transferlearning.keras"
-model = tf.keras.models.load_model(model_path)
- 
-# Define the core prediction function
-def predict_pokemon(image):
-    # Preprocess image
-    print(type(image))
-    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
-    image = image.resize((150, 150)) #resize the image to 28x28 and converts it to gray scale
-    image = np.array(image)
-    image = np.expand_dims(image, axis=0) # same as image[None, ...]
-   
-    # Predict
-    prediction = model.predict(image)
- 
-    # No need to apply sigmoid, as the output layer already uses softmax
-    # Convert the probabilities to rounded values
-    prediction = np.round(prediction, 2)
- 
-    # Separate the probabilities for each class
-    p_charmander = prediction[0][0]  # Probability for class 'charmander'
-    p_mewto = prediction[0][1]   # Probability for class 'mewto'
-    p_squirtle = prediction[0][2]    # Probability for class 'squirtle'
- 
-    return {'charmander': p_charmander, 'mewto': p_mewto, 'squirtle': p_squirtle}
- 
- 
-# Create the Gradio interface
-input_image = gr.Image()
-iface = gr.Interface(
-    fn=predict_pokemon,
-    inputs=input_image,
-    outputs=gr.Label(),
-    examples=["images/charmander1.png", "images/charmander2.png", "images/charmander3.jpg", "images/mewto.jpg", "images/mewto2.jpg", "images/mewto4.png", "images/squirtle1.png", "images/squirtle2.jpg", "images/squirtle3.jpg"],  
-    description="TEST.")
- 
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+ 
+model_path = "pokemon_transferlearning.keras"
+model = tf.keras.models.load_model(model_path)
+ 
+# Define the core prediction function
+def predict_pokemon(image):
+    # Preprocess image
+    print(type(image))
+    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
+    image = image.resize((150, 150)) #resize the image to 28x28 and converts it to gray scale
+    image = np.array(image)
+    image = np.expand_dims(image, axis=0) # same as image[None, ...]
+   
+    # Predict
+    prediction = model.predict(image)
+ 
+    # No need to apply sigmoid, as the output layer already uses softmax
+    # Convert the probabilities to rounded values
+    prediction = np.round(prediction, 2)
+ 
+    # Separate the probabilities for each class
+    p_charmander = prediction[0][0]  # Probability for class 'charmander'
+    p_mewto = prediction[0][1]   # Probability for class 'mewto'
+    p_squirtle = prediction[0][2]    # Probability for class 'squirtle'
+ 
+    return {'charmander': p_charmander, 'mewto': p_mewto, 'squirtle': p_squirtle}
+ 
+ 
+# Create the Gradio interface
+input_image = gr.Image()
+iface = gr.Interface(
+    fn=predict_pokemon,
+    inputs=input_image,
+    outputs=gr.Label(),
+    examples=["images/charmander1.png", "images/charmander2.png", "images/charmander3.jpg", "images/mewto.jpg", "images/mewto2.jpg", "images/mewto4.png", "images/squirtle1.png", "images/squirtle2.jpg", "images/squirtle3.jpg"],  
+    description="TEST.")
+ 
 iface.launch()