Spaces:
Runtime error
Runtime error
| import numpy as np | |
| import gradio as gr | |
| """An example of generating a gif explanation for an image of my dog.""" | |
| import argparse | |
| import os | |
| from os.path import exists, dirname | |
| import sys | |
| import flask | |
| parent_dir = dirname(os.path.abspath(os.getcwd())) | |
| sys.path.append(parent_dir) | |
| from bayes.explanations import BayesLocalExplanations, explain_many | |
| from bayes.data_routines import get_dataset_by_name | |
| from bayes.models import * | |
| from image_posterior import create_gif | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--cred_width", type=float, default=0.1) | |
| parser.add_argument("--save_loc", type=str, required=True) | |
| parser.add_argument("--n_top_segs", type=int, default=5) | |
| parser.add_argument("--n_gif_images", type=int, default=20) | |
| # app = flask.Flask(__name__, template_folder="./") | |
| IMAGE_NAME = "imagenet_diego" | |
| BLENHEIM_SPANIEL_CLASS = 156 | |
| def get_image_data(): | |
| """Gets the image data and model.""" | |
| puppy_image = get_dataset_by_name(IMAGE_NAME, get_label=False) | |
| model_and_data = process_imagenet_get_model(puppy_image) | |
| return puppy_image, model_and_data | |
| def segmentation_generation(image_name, c_width, n_top, n_gif_imgs): | |
| cred_width = c_width | |
| n_top_segs = n_top | |
| n_gif_images = n_gif_imgs | |
| puppy_image, model_and_data = get_image_data() | |
| # Unpack datax | |
| xtest = model_and_data["xtest"] | |
| ytest = model_and_data["ytest"] | |
| segs = model_and_data["xtest_segs"] | |
| get_model = model_and_data["model"] | |
| label = model_and_data["label"] | |
| # Unpack instance and segments | |
| instance = xtest[0] | |
| segments = segs[0] | |
| # Get wrapped model | |
| cur_model = get_model(instance, segments) | |
| # Get background data | |
| xtrain = get_xtrain(segments) | |
| prediction = np.argmax(cur_model(xtrain[:1]), axis=1) | |
| assert prediction == BLENHEIM_SPANIEL_CLASS, f"Prediction is {prediction} not {BLENHEIM_SPANIEL_CLASS}" | |
| # Compute explanation | |
| exp_init = BayesLocalExplanations(training_data=xtrain, | |
| data="image", | |
| kernel="lime", | |
| categorical_features=np.arange(xtrain.shape[1]), | |
| verbose=True) | |
| rout = exp_init.explain(classifier_f=cur_model, | |
| data=np.ones_like(xtrain[0]), | |
| label=BLENHEIM_SPANIEL_CLASS, | |
| cred_width=cred_width, | |
| focus_sample=False, | |
| l2=False) | |
| # Create the gif of the explanation | |
| return create_gif(rout['blr'], segments, instance, n_gif_images, n_top_segs) | |
| def image_mod(image): | |
| return image.rotate(45) | |
| if __name__ == "__main__": | |
| inp = gr.inputs.Image(label="Input Image", type="pil") | |
| out = gr.outputs.HTML(label="Output Video") | |
| iface = gr.Interface( | |
| segmentation_generation, | |
| [ | |
| inp, | |
| gr.inputs.Slider(minimum=0.01, maximum=0.8, step=0.001, default=0.1, label="cred_width", optional=False), | |
| gr.inputs.Slider(minimum=1, maximum=10, step=1, default=5, label="n_top_segs", optional=False), | |
| gr.inputs.Slider(minimum=10, maximum=50, step=1, default=20, label="n_gif_images", optional=False), | |
| ], | |
| outputs=out, | |
| examples=[["./imagenet_diego.png", 0.05, 7, 50]] | |
| ) | |
| iface.launch() | |
| # app.run(host='0.0.0.0', port=int(os.environ.get('PORT', 7860))) |