import gradio as gr from PIL import Image import numpy as np from tensorflow.keras.preprocessing import image as keras_image from tensorflow.keras.applications.resnet50 import preprocess_input as resnet_preprocess_input from tensorflow.keras.applications.mobilenet_v2 import preprocess_input as mobilenet_preprocess_input from tensorflow.keras.models import load_model # Load your trained models resnet_model = load_model('/Users/beharademi/Documents/ZHAW/6. Sem LOKAL/KI/module exam/ademibeh/resnet_best_model.keras') # Update path mobilenet_model = load_model('/Users/beharademi/Documents/ZHAW/6. Sem LOKAL/KI/module exam/ademibeh/mobilenet_best_model.keras') # Update path def predict_comic_character(img, model_type): img = Image.fromarray(img.astype('uint8'), 'RGB') img = img.resize((224, 224)) # Resize the image to fit model input img_array = keras_image.img_to_array(img) img_array = np.expand_dims(img_array, axis=0) if model_type == 'ResNet50': img_array = resnet_preprocess_input(img_array) prediction = resnet_model.predict(img_array) elif model_type == 'MobileNetV2': img_array = mobilenet_preprocess_input(img_array) prediction = mobilenet_model.predict(img_array) else: return {"error": "Invalid model type selected"} classes = ['Superman', 'Batman', 'WonderWoman', 'Riddler', 'Spider-Man', 'Iron-Man', 'Hulk', 'The Joker', 'Magneto', 'Wolverine', 'Deadpool', 'Catwoman'] return {classes[i]: float(prediction[0][i]) for i in range(len(classes))} # Define the Gradio interface interface = gr.Interface( fn=predict_comic_character, inputs="image", outputs="label", title="Comic Character Classifier", description="Upload an image of a comic character and the classifier will predict the character.", ) # Launch the interface interface.launch()