app.py

import gradio as gr
from transformers import pipeline

classifier = pipeline("image-classification", model="deomdell/Bhutanese-religious-artefacts-model")

import numpy as np

def image_classifier(inp):
  confidence_scores = np.random.rand(7)
  confidence_scores /= np.sum(confidence_scores)
  classes = ['chem', 'cymbal', 'dorji', 'drilbu', 'dungkar', 'maney', 'phurpa']
  result = {classes[i]:confidence_scores[i] for i in range(7)}
  return result

demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label")
demo.launch(share=True)