add thumbnail, clean up description

README.md

@@ -1,6 +1,7 @@
 ---
 title: Carnatic Raga Classifier
 emoji: 📈
+thumbnail: site/logo.jpeg
 colorFrom: pink
 colorTo: green
 sdk: gradio

about.md

@@ -1,9 +1,11 @@
 ### About the Classifier
-The classifier is a [convolutional neural network](https://en.wikipedia.org/wiki/Convolutional_neural_network) trained on over 10,000 hours of Carnatic audio sourced from this incredible [YouTube collection](https://ramanarunachalam.github.io/Music/Carnatic/carnatic.html).
+The classifier is a [convolutional neural network](https://en.wikipedia.org/wiki/Convolutional_neural_network) trained on over 10,000 hours of Carnatic audio sourced from this incredible [YouTube collection](https://ramanarunachalam.github.io/Music/Carnatic/carnatic.html). 
 ### Key Features:
 - Can identify **150 ragas** most commonly found on YouTube
-- Does not require any information about the **shruthi (tonic pitch)** of the recording.
-- **Compatible** with male/female vocal or instrumental recordings.
+- Does not require any information about the **shruthi (tonic pitch)** of the recording
+- **Compatible** with male/female vocal or instrumental recordings
+
+For those who are interested, the inference code and model checkpoints are available at the 'Files' tab in the header.
 
 ### Interpreting the Classifier:
 We can gain an intuitive sense for what the classifier has learned. Here is a [t-SNE](https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding) projection of the hidden activations averaged per ragam. Each point is a ragam, and relative distances between the points indicate the degree to which the classifier thinks the ragas are similar. Each ragam is color coded by the [melakartha chakra](https://en.wikipedia.org/wiki/Melakarta#Chakras) it belongs to. We observe that the classifier has learned to a representation that roughly corresponds to these chakras! 

app.py

@@ -37,13 +37,13 @@ if __name__ == '__main__':
         with gr.Tab("Classifier"):
             gr.Interface(
             title="Carnatic Raga Classifier",
-            description="**Welcome!** This is a deep-learning based raga classifier. Upload or record an audio clip to test it out. Provide at least 30 seconds of audio for best results. Wait for the audio waves to appear (and stay) before clicking Submit! \n",
+            description="**Welcome!** This app uses AI to recognize Carnatic ragas. Upload or record an audio clip to test it out. Provide at least 30 seconds of audio for best results. Wait for the audio waves to appear and remain before clicking Submit! \n",
             article = "**Get in Touch:** Feel free to reach out to [me](https://sanjeevraja.com/) via email (sanjeevr AT berkeley DOT edu) with any questions or feedback! ",
-            fn=evaluator.inference,
             inputs=[
                 gr.Slider(minimum = 1, maximum = 150, value = 5, label = "Number of displayed ragas", info = "Choose number of top predictions to display"), 
                 gr.Audio()
                 ],
+            fn=evaluator.inference,
             outputs="label",
             allow_flagging = False
             )