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_pokemon1.keras" model = tf.keras.models.load_model(model_path) # 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 = ['Lickitung', 'Nidoking', 'Pikachu'] 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(), live=True, examples=["images/01.jpg", "images/02.jpg", "images/03.jpg"], description="A simple mlp classification model for image classification using the mnist dataset.") iface.launch()