Update app.py

app.py

@@ -12,16 +12,17 @@ import accelerate
 # steamlit setup
 st.set_page_config(page_title="Sentiment Analysis on Your Cantonese Song",)
 st.header("Cantonese Song Sentiment Analyzer")
-input_file = st.file_uploader("upload a song in mp3 format", type="mp3") # upload song
-if input_file is not None:
-    st.write("File uploaded successfully!")
-    st.write(input_file)
-else:
-    st.write("No file uploaded.")
+#input_file = st.file_uploader("upload a song in mp3 format", type="mp3") # upload song
+#if input_file is not None:
+    #st.write("File uploaded successfully!")
+    #st.write(input_file)
+#else:
+    #st.write("No file uploaded.")
 button_click = st.button("Run Analysis", type="primary")
 
 # load song
-output_file = ""
+input_file = os.path.isfile("test1.mp3")
+output_file = os.path.isdir("")
 
 # preprocess and crop audio file
 def audio_preprocess():
@@ -33,15 +34,15 @@ def audio_preprocess():
     start_time = 60000  # e.g. 30 seconds, 30000
     end_time = 110000  # e.g. 40 seconds, 40000
 
-    audio = AudioSegment.from_file('/ISOM5240_Group25/vocals.wav')
+    audio = AudioSegment.from_file('/test1/vocals.wav')
     cropped_audio = audio[start_time:end_time]
-    cropped_audio.export('/ISOM5240_Group25/cropped_vocals.wav', format='wav') # save vocal audio file
+    cropped_audio.export('/cropped_vocals.wav', format='wav') # save vocal audio file
 
 
 # ASR transcription
 def asr_model():
     # load audio file
-    y, sr = librosa.load('/ISOM5240_Group25/cropped_vocals.wav', sr=16000)
+    y, sr = librosa.load('/cropped_vocals.wav', sr=16000)
 
     # ASR model
     MODEL_NAME = "RexChan/ISOM5240-whisper-small-zhhk_1"