app.py

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()