Update app.py

app.py

@@ -1,25 +1,30 @@
 import streamlit as st
 import torch
 import torchaudio
-from utils import load_model, preprocess_audio, predict
 import joblib
+from model import YourCNNModelClass
+from utils import load_model, preprocess_audio, predict
 
-# Load label encoder
-labels = joblib.load("label_encoder.joblib")
+# Load label encoder and get classes
+label_encoder = joblib.load("label_encoder.joblib")
+labels = label_encoder.classes_
 
-# Load model
-from model import YourCNNModelClass
+# Load PyTorch model with correct number of classes
 model = load_model("animal-sound-cnn.pth", YourCNNModelClass, num_classes=len(labels))
 
 # Streamlit UI
-st.title("Animal Sound Classifier 🎵🐾")
-uploaded_file = st.file_uploader("Upload a WAV file", type=["wav"])
+st.title("Animal Sound Classification")
+
+uploaded_file = st.file_uploader("Upload a WAV audio file", type=["wav"])
 
 if uploaded_file:
     with open("temp.wav", "wb") as f:
         f.write(uploaded_file.read())
 
+    st.audio("temp.wav")
+
+    # Preprocess audio and predict
     input_tensor = preprocess_audio("temp.wav")
-    prediction = predict(model, input_tensor, labels)
+    pred_label = predict(model, input_tensor, labels)
 
-    st.success(f"Predicted Animal Sound: **{prediction}**")
+    st.write(f"Predicted animal: **{pred_label}**")