Update app.py

app.py

@@ -1,35 +1,35 @@
-import gradio as gr
-import tensorflow as tf
-import numpy as np
-from PIL import Image
-model_path = "Pokemon_transfer_learning.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)
-    
-    # Because the output layer was dense(0) without an activation function, we need to apply sigmoid to get the probability
-    # we could also change the output layer to dense(1, activation='sigmoid')
-    prediction = np.round(prediction, 2)
-        # Separate the probabilities for each class
-    p_abra = prediction[0][0]  # Probability for class 'abra'
-    p_beedrill = prediction[0][1]   # Probability for class 'moltres'
-    p_sandshrew = prediction[0][2]    # Probability for class 'zapdos'
-    return {'abra': p_abra, 'beedrill':  p_beedrill, 'sandshrew': p_sandshrew}
-# Create the Gradio interface
-input_image = gr.Image()
-iface = gr.Interface(
-    fn=predict_pokemon,
-    inputs=input_image, 
-    outputs=gr.Label(),
-    examples=["images/abra1.png", "images/abra2.jpg", "images/abra3.png", "images/beedrill1.png", "images/beedrill2.png", "images/beedrill3.jpg", "images/sandshrew1.png", "images/sandshrew2.jpg", "images/sandshrew3.png"],   
-    description="A simple mlp classification model for image classification using the mnist dataset.")
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+model_path = "Pokemon_transfer_learning.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)
+    
+    # Because the output layer was dense(0) without an activation function, we need to apply sigmoid to get the probability
+    # we could also change the output layer to dense(1, activation='sigmoid')
+    prediction = np.round(prediction, 2)
+        # Separate the probabilities for each class
+    p_abra = prediction[0][0]  # Probability for class 'abra'
+    p_beedrill = prediction[0][1]   # Probability for class 'beedrill'
+    p_sandshrew = prediction[0][2]    # Probability for class 'sandshrew'
+    return {'abra': p_abra, 'beedrill':  p_beedrill, 'sandshrew': p_sandshrew}
+# Create the Gradio interface
+input_image = gr.Image()
+iface = gr.Interface(
+    fn=predict_pokemon,
+    inputs=input_image, 
+    outputs=gr.Label(),
+    examples=["images/abra1.png", "images/abra2.jpg", "images/abra3.png", "images/beedrill1.png", "images/beedrill2.png", "images/beedrill3.jpg", "images/sandshrew1.png", "images/sandshrew2.jpg", "images/sandshrew3.png"],   
+    description="A simple mlp classification model for image classification using the mnist dataset.")
 iface.launch(share=True)