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 = "pokemon-model_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 150x150
+    image = np.array(image)
+    image = np.expand_dims(image, axis=0)  # Expand dimensions to create batch size of 1
+
+    # Predict
+    prediction = model.predict(image)
+    
+    # Assuming the model's output layer uses softmax activation and there are three outputs
+    prediction = prediction.flatten()
+    predictions = np.round(prediction, 2)  # Flatten the predictions and round them
+
+    # Separate the probabilities for each class
+    p_clefairy = predictions[0]  # Probability for Clefairy
+    p_snorlax = predictions[1]   # Probability for Snorlax
+    p_squirtle = predictions[2]  # Probability for Squirtle
+
+    return {
+        'clefairy': p_clefairy,
+        'snorlax': p_snorlax,
+        'squirtle': p_squirtle
+    }
+
+# Create the Gradio interface
+input_image = gr.Image()
+iface = gr.Interface(
+    fn=predict_pokemon,
+    inputs=input_image, 
+    outputs=gr.Label(),
+    examples=[r"images\squirtle\0.png", r"images\squirtle\00000003.png", r"images\snorlax\00000003.png", 
+              r"images\squirtle\4.png", r"images\snorlax\00000017.png", 
+              r"images\snorlax\4eb284a359474f0cb43ffe82d03abbe9.jpg", 
+              r"images\clefairy\5fb558d9c96e4e469100636eb6c8627e.jpg", 
+              r"images\clefairy\00000024.png", r"images\clefairy\00000103.jpg"],
+    description="A simple mlp classification model for image classification using the mnist dataset.")
+iface.launch()