Upload app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+model_path = "Xception.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_aloevera = prediction[0][0]  # Probability for class 'abra'
+    P_curcuma = prediction[0][1]   # Probability for class 'beedrill'
+    p_guava = prediction[0][2]    # Probability for class 'sandshrew'
+    return {'aloevera': P_aloevera, 'curcuma':  P_curcuma, 'guava': p_guava}
+# Create the Gradio interface
+input_image = gr.Image()
+iface = gr.Interface(
+    fn=predict_pokemon,
+    inputs=input_image, 
+    outputs=gr.Label(),
+    examples=["images/aloevera0.jpg", "images/curcuma51.jpg", "images/guava10.jpg"],   
+    description="A simple mlp classification model for image classification using the mnist dataset.")
+iface.launch(share=True)