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 = "pokemons-model_transferlearning.keras"
+model = tf.keras.models.load_model(model_path)
+ 
+
+def predict_pokemons(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, ...]
+
+    prediction = model.predict(image)
+ 
+
+    # Convert the probabilities to rounded values
+    prediction = np.round(prediction, 2)
+ 
+    # Separate the probabilities for each class
+    p_bulbasaur = prediction[0][0]  
+    p_dratini = prediction[0][1]   
+    p_gengar = prediction[0][2]    
+ 
+    return {'Bulbasaur':  p_bulbasaur, 'Dratini': p_dratini, 'Gengar': p_gengar}
+ 
+ 
+input_image = gr.Image()
+iface = gr.Interface(
+    fn=predict_pokemons,
+    inputs=input_image,
+    outputs=gr.Label(),
+    examples=["images/bulbasaur1.png", "images/bulbasaur2.png", "images/dratini1.png", "images/dratini2.png", "images/dratini3.png", "images/gengar1.png", "images/gengar2.png", "images/gengar3.png"],
+    description="TEST.")
+ 
+iface.launch()