app.py

import gradio as gr
from model import model_classification
import torch,os

path = 'efficient_cat_dog.pth'
class_names = ['cat','dog']


model,transforms = model_classification()
model.load_state_dict(torch.load(path,map_location=torch.device('cpu')))


def predict(img):

    img = transforms(img).unsqueeze(0)

    model.eval()

    with torch.inference_mode():
        logits = model(img)

    pred_probs = torch.softmax(logits,dim=1)

    pred_label_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}


    return pred_label_and_probs


title = 'Cat and Dog classification'
description = 'An EfficientNetB0 feature extractor computert vision model to classify the cats and dogs'

example_list = [["examples/" + example] for example in os.listdir("examples")]


demo = gr.Interface(fn=predict,
                    inputs=gr.Image(type='pil'),
                    outputs=gr.Label(num_top_classes=2,label='Predictions'),
                    title=title,
                    examples=example_list,
                    description=description,

                             )



demo.launch(share=True)