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
+
+#!pip install tensorflow tensorflow-datasets gradio pillow matplotlib
+
+model_path = "pokemon-model_transferlearning.keras"
+model = tf.keras.models.load_model(model_path)
+
+from PIL import Image
+import numpy as np
+import tensorflow as tf
+
+# Define the core prediction function
+def predict_pokemon(image):
+    # Preprocess image
+    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
+    image = image.resize((150, 150)) # Resize the image to 150x150
+    image = np.array(image)
+    image = np.expand_dims(image, axis=0) # Add batch dimension
+    
+    # Predict
+    prediction = model.predict(image)
+    
+    # Apply softmax to get probabilities for each class
+    probabilities = tf.nn.softmax(prediction)
+    
+    # Map probabilities to Pokemon classes
+    pokemon_classes = ['Articuno', 'Bulbasaur', 'Charmander']  
+    probabilities_dict = {pokemon_class: round(float(probability), 2) for pokemon_class, probability in zip(pokemon_classes, probabilities[0])}
+    
+    return probabilities_dict
+
+# Create the Gradio interface
+input_image = gr.Image()
+iface = gr.Interface(
+    fn=predict_pokemon,
+    inputs=input_image, 
+    outputs=gr.Label(),
+    examples=["images/01.jpg", "images/02.png", "images/03.png", "images/04.jpg", "images/06.png", "images/06.png"],   
+    description="A simple mlp classification model for image classification using the mnist dataset.")
+iface.launch(share=True)