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import gradio as gr
import numpy as np
# Function to classify images into 5 classes
def image_classifier(inp):
# Dummy classification logic
# Generating random confidence scores for each class
confidence_scores = np.random.rand(5)
# Normalizing confidence scores to sum up to 1
confidence_scores /= np.sum(confidence_scores)
# Creating a dictionary with class labels and corresponding confidence scores
classes = ['bike', 'cars', 'cats', 'dogs', 'flowers']
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() |