First commit on huggingface

app.py

@@ -0,0 +1,56 @@
+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(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)
+        # 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()

requirements.txt

@@ -0,0 +1,3 @@
+tensorflow==2.10.0
+keras==2.10.0
+gradio==3.41.2