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import gradio as gr
import tensorflow as tf
import numpy as np
from PIL import Image
model_path = "chess-predict-model_transferlearning.keras"
model = tf.keras.models.load_model(model_path)
# Define the core prediction function
def predict_figure(image):
# Preprocess image
print(type(image))
image = Image.fromarray(image.astype('uint8')) # Convert numpy array to PIL image
image = image.resize((150, 150)) #resize the image to 150x150
image = np.array(image)
image = np.expand_dims(image, axis=0) # same as image[None, ...]
# Predict
prediction = model.predict(image)
# Apply softmax to get probabilities for each class
prediction = tf.nn.softmax(prediction)
# Create a dictionary with the probabilities for each Pokemon
bishop = np.round(float(prediction[0][0]), 2)
king = np.round(float(prediction[0][1]), 2)
knight = np.round(float(prediction[0][2]), 2)
pawn = np.round(float(prediction[0][3]), 2)
queen = np.round(float(prediction[0][4]), 2)
rook = np.round(float(prediction[0][5]), 2)
return {'Bishop': bishop, 'King': king, 'Knight': knight, 'Pawn': pawn, 'Queen': queen, 'Rook': rook}
input_image = gr.Image()
iface = gr.Interface(
fn=predict_figure,
inputs=input_image,
outputs=gr.Label(),
description="A simple mlp classification model for image classification using the mnist dataset.")
iface.launch(share=True)
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