app.py

from cgitb import enable
from pyexpat import model
from statistics import mode
import numpy as np
import gradio as gr

import argparse
import os
from os.path import exists, dirname
import sys
import json
import flask
from PIL import Image

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	


def get_image_data(inp_image):
    """Gets the image data and model."""
    image = get_dataset_by_name(inp_image, get_label=False)
    # print("image returned\n", image)
    model_and_data = process_imagenet_get_model(image)
    # print("model returned\n", model_and_data)
    return image, model_and_data


def segmentation_generation(image_name, c_width, n_top, n_gif_imgs):
    print("Inputs Received:", image_name, c_width, n_top, n_gif_imgs)    

    image, model_and_data = get_image_data(image_name)
    # print("model_and_data", model_and_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"]
    

    # if (image_name == 'imagenet_diego'):
    #     label = 156
    # elif (image_name == 'imagenet_french_bulldog'):
    #     label = 245

    # 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)
    # if image_name in ["imagenet_diego", "imagenet_french_bulldog"]:
    #     assert prediction == label, f"Prediction is {prediction} not {label}"

    # 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=int(prediction[0]),
                            cred_width=c_width,
                            focus_sample=False,
                            l2=False)

    # Create the gif of the explanation
    return create_gif(rout['blr'], image_name, segments, instance, prediction[0], n_gif_imgs, n_top)

if __name__ == "__main__":
    # gradio's image inputs look like this: <PIL.Image.Image image mode=RGB size=305x266 at 0x7F3D01C91FA0>
    # need to learn how to handle image inputs, or deal with file inputs or just file path strings
    inp = gr.inputs.Image(label="Input Image (Or select an example)", type="pil")
    out = [gr.outputs.HTML(label="Output GIF"), gr.outputs.Textbox(label="Prediction")]

    iface = gr.Interface(
        segmentation_generation, 
        [
            inp,
            gr.inputs.Slider(minimum=0.01, maximum=0.8, step=0.01, 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", 0.01, 7, 50], 
                  ["./imagenet_french_bulldog.jpg", 0.05, 5, 50]]
    )
    iface.launch(enable_queue=True)