Add application file

.gitattributes

@@ -25,3 +25,11 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/example_1.jpg filter=lfs diff=lfs merge=lfs -text
+examples/example_2.jpg filter=lfs diff=lfs merge=lfs -text
+examples/example_3.jpg filter=lfs diff=lfs merge=lfs -text
+examples/example_4.jpg filter=lfs diff=lfs merge=lfs -text
+examples/example_5.jpg filter=lfs diff=lfs merge=lfs -text
+examples/example_6.jpg filter=lfs diff=lfs merge=lfs -text
+examples/example_7.jpg filter=lfs diff=lfs merge=lfs -text
+examples/example_9.jpg filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,42 @@
+import math
+import numpy as np
+import pandas as pd
+
+import gradio as gr
+from huggingface_hub import from_pretrained_fastai
+
+
+def get_x(x):
+    return pascal_source/"train"/f'{x[0]}'
+
+def get_y(x):
+    return x[1].split(' ')
+
+pascal_source = '.'
+EXAMPLES_PATH = Path('./examples')
+repo_id = "hugginglearners/identify-pascal"
+
+learner = from_pretrained_fastai(repo_id)
+labels = learner.dls.vocab
+
+def infer(img):
+    img = PILImage.create(img)
+    _pred, _pred_w_idx, probs = learner.predict(img)
+    # gradio doesn't support tensors, so converting to float
+    labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}
+    return labels_probs
+    # return f"This grapevine leave is {_pred} with {100*probs[torch.argmax(probs)].item():.2f}% probability"
+
+# get the inputs
+inputs = gr.inputs.Image(shape=(192, 192))
+
+# the app outputs two segmented images
+output = gr.outputs.Label(num_top_classes=3)
+# it's good practice to pass examples, description and a title to guide users
+title = 'Multilabel Image classification'
+description = 'Detect which type of object appearing in the image'
+article = "Author: <a href=\"https://huggingface.co/geninhu\">Nhu Hoang</a>. "
+examples = [f'{EXAMPLES_PATH}/{f.name}' for f in EXAMPLES_PATH.iterdir()]
+
+gr.Interface(infer, inputs, output, examples= examples, allow_flagging='never',
+             title=title, description=description, article=article, live=False).launch(enable_queue=True, debug=False, inbrowser=False)