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| import gradio as gr | |
| import tensorflow as tf | |
| from tensorflow.compat.v2.experimental import dtensor | |
| import numpy as np | |
| from PIL import Image | |
| # Load pre-trained MobileNetV2 model | |
| model = tf.keras.applications.MobileNetV2(weights='imagenet') | |
| def predict_difficulty_score(image): | |
| # Load image and preprocess it for the model | |
| img = Image.fromarray(image.astype('uint8'), 'RGB') | |
| img = img.resize((224, 224)) | |
| img_array = tf.keras.preprocessing.image.img_to_array(img) | |
| img_array = tf.keras.applications.mobilenet_v2.preprocess_input(img_array[np.newaxis,...]) | |
| # Use the model to predict the image class probabilities | |
| preds = model.predict(img_array) | |
| # Get the index of the top predicted class | |
| class_idx = np.argmax(preds[0]) | |
| # Get the difficulty score based on the class index | |
| difficulty_score = round((class_idx / 999) * 99000) + 1000 | |
| # Return the difficulty score | |
| return difficulty_score | |
| # Create a Gradio interface | |
| inputs = gr.inputs.Image(shape=(224, 224)) | |
| outputs = gr.outputs.Textbox(label="Difficulty Score") | |
| interface = gr.Interface(fn=predict_difficulty_score, inputs=inputs, outputs=outputs, | |
| title="AI Art Difficulty Score", description="Upload an AI art image and get its difficulty score.") | |
| # Launch the interface | |
| interface.launch() | |