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| from transformers import pipeline | |
| classifier = pipeline("image-classification", model="Thogmey/Chess-model") | |
| import gradio as gr | |
| import numpy as np | |
| # Function to classify images into 7 classes | |
| def image_classifier(inp): | |
| # Dummy classification logic | |
| # Generating random confidence scores for each class | |
| confidence_scores = np.random.rand(6) | |
| # 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 = ['Bishop', 'King', 'Knight', 'Pawn', 'Queen', 'Rook'] | |
| result = {classes[i]: confidence_scores[i] for i in range(6)} | |
| return result | |
| # Creating Gradio interface | |
| demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label") | |
| demo.launch() | |