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import gradio as gr
from PIL import Image
import numpy as np
from tensorflow.keras.preprocessing import image as keras_image
from tensorflow.keras.applications.xception import preprocess_input
from tensorflow.keras.models import load_model
# Load your trained model
model = load_model('/home/user/app/xception_model.h5') # Ensure this path is correct
def predict_character(img):
img = Image.fromarray(img.astype('uint8'), 'RGB') # Ensure the image is in RGB format
img = img.resize((299, 299)) # Resize the image to the required size for Xception
img_array = keras_image.img_to_array(img) # Convert the image to an array
img_array = np.expand_dims(img_array, axis=0) # Expand dimensions to match the model input
img_array = preprocess_input(img_array) # Preprocess the input for Xception
prediction = model.predict(img_array) # Make a prediction with the model
classes = ['bishop', 'knight', 'rook'] # Specific character names
return {classes[i]: float(prediction[0][i]) for i in range(3)} # Return the prediction
# Define the Gradio interface
interface = gr.Interface(fn=predict_character,
inputs="image", # Simplified input type
outputs="label", # Simplified output type
title="Chess Piece Classifier",
description="Upload an image of a chess piece to classify it as a bishop, knight, or rook.")
# Launch the interface
interface.launch()
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