Update app.py

app.py

@@ -1,10 +1,17 @@
 import torch
 from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
 
-# Load the model and tokenizer from the Hugging Face Model Hub
+# Get user input for the text
+text = input("Enter a sentence to classify the sound: ")
+
+# Get the name of the sound classification model
 model_name = "BrendaTellez/SoundClassification"
-model = Wav2Vec2ForCTC.from_pretrained(model_name, use_auth_token=True)
-tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_name, use_auth_token=True)
+print("Using sound classification model:", model_name)
+
+# Load the model and tokenizer from the Hugging Face Model Hub
+api_token = "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"
+model = Wav2Vec2ForCTC.from_pretrained(model_name, use_auth_token=True, token=api_token)
+tokenizer = Wav2Vec2Tokenizer.from_pretrained(model_name, use_auth_token=True, token=api_token)
 
 # Load the audio file
 audio_file = "../input/environmental-sound-classification-50/audio/audio/44100/"
@@ -19,4 +26,5 @@ with torch.no_grad():
 predicted_class_id = torch.argmax(logits, dim=-1)
 predicted_class_label = tokenizer.decode(predicted_class_id[0])
 
-print(predicted_class_label)
+# Print the predicted class label for the user to see
+print("The predicted sound class for the sentence", text, "is:", predicted_class_label)