|
import os |
|
import numpy as np |
|
import tensorflow as tf |
|
from tensorflow import keras |
|
from tensorflow.keras.preprocessing import image |
|
import gradio as gr |
|
|
|
|
|
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(image): |
|
|
|
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) |
|
|
|
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() |
