| import gradio as gr | |
| import tensorflow as tf | |
| from PIL import Image | |
| import numpy as np | |
| # Load your custom regression model | |
| model_path = "trainpokemon_model_transferlearning.weights.h5" | |
| model_path = "pokemon_model_transferlearning.keras" | |
| #model.load_weights(model_path) | |
| model = tf.keras.models.load_model(model_path) | |
| labels = ['Ditto','Venomoth','Venusaur'] | |
| # Define regression function | |
| def predict_regression(image): | |
| # Preprocess image | |
| image = Image.fromarray(image.astype('uint8')) # Convert numpy array to PIL image | |
| image = image.resize((150, 150))#.convert('L') #resize the image to 28x28 and converts it to gray scale | |
| image = np.array(image) | |
| print(image.shape) | |
| # Predict | |
| prediction = model.predict(image[None, ...]) # Assuming single regression value | |
| confidences = {labels[i]: np.round(float(prediction[0][i]), 2) for i in range(len(labels))} | |
| return confidences | |
| # Create Gradio interface | |
| input_image = gr.Image() | |
| output_text = gr.Textbox(label="Predicted Pokemon") | |
| interface = gr.Interface(fn=predict_regression, | |
| inputs=input_image, | |
| outputs=gr.Label(), | |
| examples=["pokemons/train/Ditto/00000008.jpg", "images/Venomoth.jpeg", "images/Venusaur.jpeg"], | |
| description="A simple mlp classification model for image classification using the mnist dataset.") | |
| interface.launch() | |