| 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() |