app.py

from transformers import pipeline

import gradio as gr
import numpy as np

# Function to classify images into 7 classes
def image_classifier(inp):
  confidence_scores = np.random.rand(5)
  confidence_scores /= np.sum(confidence_scores)
  classes =   ['crocus', 'daffodil', 'daisy', 'dandelion', 'fritillary']
  result = {classes[i]: confidence_scores[i] for i in range(5)}
  return result

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