app.py

from transformers import pipeline

classifier = pipeline("image-classification", model="KayDee03/Fruits-model")

import numpy as np

# Function to classify images into 4 classes
def image_classifier(inp):
    confidence_scores = np.random. rand(4)
    confidence_scores /= np.sum(confidence_scores)
    classes = ['Avocado', 'Banana', 'Guava', 'Mango']
    result = {classes[i]: confidence_scores[i] for i in range(4)}
    return result

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