| 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() |