Create app.py file

app.py

@@ -0,0 +1,44 @@
+import gradio
+import torchaudio
+from fastai.vision.all import *
+from fastai.learner import load_learner
+from torchvision.utils import save_image
+from huggingface_hub import hf_hub_download
+
+
+model = load_learner(
+    hf_hub_download("kurianbenoy/music_genre_classification_baseline", "model.pkl")
+)
+
+EXAMPLES_PATH = Path("./examples")
+labels = model.dls.vocab
+
+interface_options = {
+    "title": "Music Genre Classification",
+    "description": "A simple baseline model for classifying music genres with fast.ai on [Kaggle competition data](https://www.kaggle.com/competitions/kaggle-pog-series-s01e02/data)",
+    "examples": [f"{EXAMPLES_PATH}/{f.name}" for f in EXAMPLES_PATH.iterdir()],
+    "interpretation": "default",
+    "layout": "horizontal",
+    "theme": "default",
+}
+
+
+def predict(img):
+    img = PILImage.create(img)
+    _pred, _pred_w_idx, probs = model.predict(img)
+    labels_probs = {labels[i]: float(probs[i]) for i, _ in enumerate(labels)}
+    return labels_probs
+
+
+demo = gradio.Interface(
+    fn=predict,
+    inputs=gradio.inputs.Image(shape=(512, 512)),
+    outputs=gradio.outputs.Label(num_top_classes=5),
+)
+
+launch_options = {
+    "enable_queue": True,
+    "share": False,
+}
+
+demo.launch(**launch_options)