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import os
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing import image
import gradio as gr
# Paintings' authors considered for this version
artists = ['botero',
'davinci',
'elgreco',
'melzi',
'michelangelo',
'modigliani',
'picasso',
'rembrandt',
'rubens',
'vermeer']
model = keras.models.load_model('models/model_2023-08-29T1856_ep40_bz32_img224_nc10.h5')
description = 'Welcome!!! This app was built based on Gradio. The aim of this App is to predict the author of a painting. In this first version of the App, we only considered 10 authors [botero, davinci, elgreco, melzi, michelangelo,modigliani, picasso, rembrandt, rubens, vermeer]'
def predicting_author(input):
try:
if input is None:
return 'Please upload an image'
x = image.img_to_array(input)
x = np.expand_dims(x, axis=0)
x = x.astype('float32') / 255.0
prediction = model.predict(x)
class_probabilities = prediction[0]
results = {artists[i]: float(class_probabilities[i]) for i in range(len(artists))}
return results
except Exception as e:
print("An error occurred:", e)
# Print traceback to see more details
import traceback
traceback.print_exc()
return "An error occurred"
demo = gr.Interface(
title='Predicting paintings authors',
description=description,
fn=predicting_author,
inputs=gr.Image(shape=(224, 224)),
outputs=gr.Label(num_top_classes=3),
examples=['./test_images/image1.jpg', './test_images/image2.jpg', './test_images/image3.jpg']
)
demo.launch() |