app.py

import gradio as gr
from torchvision.models import resnet50, ResNet50_Weights
import torch.nn as nn
import torch
import numpy as np
from explain import Explainer

@staticmethod
def create_model_from_checkpoint():
    # Loads a model from a checkpoint
    model = resnet50()
    model.fc = nn.Linear(model.fc.in_features, 3)
    model.load_state_dict(torch.load("best_model"))
    model.eval()
    return model

model = create_model_from_checkpoint()
labels = [ "benign", "malignant", "normal" ]

def predict(img):
    explainer = Explainer(model, img, labels)
    shap_img = explainer.shap()
    return [explainer.confidences, shap_img]

ui = gr.Interface(fn=predict, 
                inputs=gr.Image(type="pil"),
                outputs=[gr.Label(num_top_classes=3), gr.Image(type="pil")],
                examples=["benign (52).png", "benign (243).png", "malignant (127).png", "malignant (201).png", "normal (81).png", "normal (101).png"]).launch()
ui.launch(share=True)