test app

.gitignore

@@ -0,0 +1,2 @@
+.ipynb_checkpoints
+*~

.ipynb_checkpoints/gradio_artist_classifier-checkpoint.py

@@ -10,14 +10,62 @@ import matplotlib.pyplot as plt
 import matplotlib.image as mpimg
 import seaborn as sns
 
+import json
+import skimage.io
+from loguru import logger
+from huggingface_hub import from_pretrained_keras
 import gradio as gr
 import tensorflow as tf
 tfk = tf.keras
 
 from gradcam_utils import get_img_4d_array, make_gradcam_heatmap, align_image_with_heatmap
 
-def greet(name):
-    return "Hello " + name + "!!"
+# ---------- Settings ----------
+ARTIST_META = 'artist.json'
+TREND_META = 'trend.json'
+EXAMPLES = ['monet.jpg']
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+# ---------- Logging ----------
+logger.add('app.log', mode='a')
+logger.info('============================= App restarted =============================')
+
+# ---------- Model ----------
+logger.info('loading models...')
+artist_model = from_pretrained_keras("jkang/drawing-artist-classifier")
+trend_model = from_pretrained_keras("jkang/drawing-artistic-trend-classifier")
+logger.info('both models loaded')
+
+def load_image_as_array(image_file):
+    img = skimage.io.imread(image_file, as_gray=False, plugin='matplotlib')
+    if (img.shape[-1] > 3) & (remove_alpha_channel):  # if RGBA
+        img = img[..., :-1]
+    return img
+
+def load_image_as_tensor(image_file):
+    img = tf.io.read_file(image_file)
+    img = tf.io.decode_jpeg(img, channels=3)
+    return img
+
+def predict(input_image):
+    img_3d_array = load_image_as_array(input_image)
+    img_4d_tensor = load_image_as_tensor(input_image)
+    logger.info(f'--- {input_image} loaded')
+    
+    artist_model(img_4d_tensor);
+    trend_model(img_4d_tensor);
+    
+    return img_3d_array
+
+iface = gr.Interface(
+    predict,
+    title='Predict Artist and Artistic Trend of Drawings 🎨👨🏻‍🎨 (prototype)',
+    description='Upload a drawing and the model will predict how likely it seems given 10 artists and their trend/style',
+    inputs=[
+        gr.inputs.Image(label='Upload a drawing/image', type='file')
+    ],
+    outputs=[
+        gr.outputs.Image(label='Prediction')
+    ],
+    examples=EXAMPLES,
+)
+iface.launch(debug=True, enable_queue=True)

.ipynb_checkpoints/requirements-checkpoint.txt

@@ -1,5 +1,9 @@
 gradio==2.7.0
+huggingface_hub==0.4.0
+loguru==0.5.3
 matplotlib==3.5.1
 numpy==1.22.0
+scikit_image==0.19.1
 seaborn==0.11.2
+scikit-image==0.19.1
 tensorflow==2.7.0

artist.json

@@ -0,0 +1,12 @@
+{
+  "claude_monet": 0,
+  "henri_matisse": 1,
+  "jean_michel_basquiat": 2,
+  "keith_haring": 3,
+  "pablo_picasso": 4,
+  "pierre_augste_renoir": 5,
+  "rene_magritte": 6,
+  "roy_richtenstein": 7,
+  "vincent_van_gogh": 8,
+  "wassily_kandinsky": 9
+}

gradio_artist_classifier.py

@@ -10,14 +10,62 @@ import matplotlib.pyplot as plt
 import matplotlib.image as mpimg
 import seaborn as sns
 
+import json
+import skimage.io
+from loguru import logger
+from huggingface_hub import from_pretrained_keras
 import gradio as gr
 import tensorflow as tf
 tfk = tf.keras
 
 from gradcam_utils import get_img_4d_array, make_gradcam_heatmap, align_image_with_heatmap
 
-def greet(name):
-    return "Hello " + name + "!!"
+# ---------- Settings ----------
+ARTIST_META = 'artist.json'
+TREND_META = 'trend.json'
+EXAMPLES = ['monet.jpg']
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+# ---------- Logging ----------
+logger.add('app.log', mode='a')
+logger.info('============================= App restarted =============================')
+
+# ---------- Model ----------
+logger.info('loading models...')
+artist_model = from_pretrained_keras("jkang/drawing-artist-classifier")
+trend_model = from_pretrained_keras("jkang/drawing-artistic-trend-classifier")
+logger.info('both models loaded')
+
+def load_image_as_array(image_file):
+    img = skimage.io.imread(image_file, as_gray=False, plugin='matplotlib')
+    if (img.shape[-1] > 3) & (remove_alpha_channel):  # if RGBA
+        img = img[..., :-1]
+    return img
+
+def load_image_as_tensor(image_file):
+    img = tf.io.read_file(image_file)
+    img = tf.io.decode_jpeg(img, channels=3)
+    return img
+
+def predict(input_image):
+    img_3d_array = load_image_as_array(input_image)
+    img_4d_tensor = load_image_as_tensor(input_image)
+    logger.info(f'--- {input_image} loaded')
+    
+    artist_model(img_4d_tensor);
+    trend_model(img_4d_tensor);
+    
+    return img_3d_array
+
+iface = gr.Interface(
+    predict,
+    title='Predict Artist and Artistic Trend of Drawings 🎨👨🏻‍🎨 (prototype)',
+    description='Upload a drawing and the model will predict how likely it seems given 10 artists and their trend/style',
+    inputs=[
+        gr.inputs.Image(label='Upload a drawing/image', type='file')
+    ],
+    outputs=[
+        gr.outputs.Image(label='Prediction')
+    ],
+    examples=EXAMPLES,
+)
+iface.launch(debug=True, enable_queue=True)

requirements-dev.txt

@@ -0,0 +1,9 @@
+gradio==2.7.0
+huggingface_hub==0.4.0
+loguru==0.5.3
+matplotlib==3.5.1
+numpy==1.22.0
+scikit_image==0.19.1
+seaborn==0.11.2
+scikit-image==0.19.1
+tensorflow==2.7.0

requirements.txt

@@ -1,5 +1,9 @@
 gradio==2.7.0
+huggingface_hub==0.4.0
+loguru==0.5.3
 matplotlib==3.5.1
 numpy==1.22.0
+scikit_image==0.19.1
 seaborn==0.11.2
+scikit-image==0.19.1
 tensorflow==2.7.0

trend.json

@@ -0,0 +1,10 @@
+{
+  "cubism": 0,
+  "expressionism": 1,
+  "fauvisme": 2,
+  "graffitiart": 3,
+  "impressionism": 4,
+  "popart": 5,
+  "post_impressionism": 6,
+  "surrealism": 7
+}