cache model

app.py

@@ -34,6 +34,7 @@ def demo_predict(df_row):
     outputs = [{"Emotion": config.id2label[i], "Score": round(score * 100, 3)} for i, score in enumerate(scores)]
     return outputs
 
+@st.cache(allow_output_mutation=True)
 def cache_model():
     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
     model_name_or_path = 'khizon/greek-speech-emotion-classifier-demo'

download_dataset.py

@@ -26,12 +26,10 @@ if __name__ == '__main__':
         name = str(path).split("/")[-1]
         label = str(path).split('/')[-2]
         path = os.path.join("data", "aesdd", label, name)
-        print(path)
 
         try:
             # There are some broken files
             s = torchaudio.load(path)
-            print(s)
             data.append({
                 "name": name,
                 "path": path,