Update application code

app.py

@@ -13,13 +13,18 @@ model = load_learner(
 EXAMPLES_PATH = Path("./examples")
 labels = model.dls.vocab
 
+with open("article.md") as f:
+    article = f.read()
+
 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()],
+    "aricle": article,
     "interpretation": "default",
     "layout": "horizontal",
     "theme": "default",
+    # Audio from validation file
+    "examples": ["000003.ogg", "000032.ogg", "000038.ogg", "000050.ogg", "000103.ogg"],
 }
 
 N_FFT = 2048

article.md

@@ -0,0 +1,32 @@
+During first lesson of Practical Deep Learning for Coders course, Jeremy had mentioned how using simple computer vision model by being a bit creative we can build a state of the art model to classify audio with same image classification model. I was curious on how I can train an music classifier, as I have never worked on audio data before.
+
+
+[You can find how I trained this music genre classification using fast.ai](https://kurianbenoy.com/ml-blog/fastai/fastbook/2022/05/01/AudioCNNDemo.html).
+
+## Dataset
+
+1. [The competition data](https://www.kaggle.com/competitions/kaggle-pog-series-s01e02/data)
+2. [Image data generated from converting audio to melspectograms in form of images](https://www.kaggle.com/datasets/dienhoa/music-genre-spectrogram-pogchamps)
+
+
+## Training
+
+Fast.ai was used to train this classifier with a ResNet50 vision learner for 10 epochs.
+
+epoch 	train_loss 	valid_loss 	error_rate 	time
+0 	2.869285 	2.171426 	0.616428 	01:43
+epoch 	train_loss 	valid_loss 	error_rate 	time
+0 	2.312176 	1.843815 	0.558654 	02:07
+1 	2.102361 	1.719162 	0.539061 	02:08
+2 	1.867139 	1.623988 	0.527003 	02:08
+3 	1.710557 	1.527913 	0.507661 	02:07
+4 	1.629478 	1.456836 	0.479779 	02:05
+5 	1.519305 	1.433036 	0.474253 	02:05
+6 	1.457465 	1.379757 	0.464456 	02:05
+7 	1.396283 	1.369344 	0.457925 	02:05
+8 	1.359388 	1.367973 	0.453655 	02:05
+9 	1.364363 	1.368887 	0.456167 	02:04
+
+## Examples
+
+The example images provided in the demo are from the validation data from Kaggle competition data, which was not used during training.