Fix Gradio and dependency compatibility

.gitattributes

@@ -1,35 +1,35 @@
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-*tfevents* filter=lfs diff=lfs merge=lfs -text
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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz 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

README.md

@@ -1,13 +1,13 @@
----
-title: Multi Object Classification
-emoji: 🕵️‍♂️
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 3.0.24
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Multi Object Classification
+emoji: 🕵️‍♂️
+colorFrom: yellow
+colorTo: green
+sdk: gradio
+sdk_version: 5.29.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,43 +1,32 @@
-import math
-import numpy as np
-import pandas as pd
-
-import gradio as gr
-from huggingface_hub import from_pretrained_fastai
-from fastai.vision.all import *
-
-
-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/multi-object-classification"
-
-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)
+from pathlib import Path
+
+import gradio as gr
+from fastai.vision.all import *
+from huggingface_hub import from_pretrained_fastai
+
+EXAMPLES_PATH = Path("./examples")
+repo_id = "hugginglearners/multi-object-classification"
+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)
+    return {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}
+
+
+examples = [str(path) for path in EXAMPLES_PATH.iterdir()] if EXAMPLES_PATH.exists() else None
+
+demo = gr.Interface(
+    infer,
+    gr.Image(type="pil", height=192, width=192),
+    gr.Label(num_top_classes=3),
+    examples=examples,
+    flagging_mode="never",
+    title="Multilabel Image classification",
+    description="Detect which type of object appears in the image.",
+    article='Author: <a href="https://huggingface.co/geninhu">Nhu Hoang</a>.',
+    live=False,
+)
+demo.queue().launch(debug=False, inbrowser=False)

requirements.txt

@@ -1,4 +1,2 @@
-fastai==2.7.4
-huggingface_hub[fastai]
-fastcore>=1.3.27
-gradio
+fastai>=2.7.18,<3
+huggingface_hub[fastai]>=0.23