v1

app.py

@@ -22,7 +22,6 @@ import torchaudio.transforms as T
 n_fft = 1024
 win_length = None
 hop_length = 32
-
 # Input tensor shape was ([1,16000])
 class SnoreNet(nn.Module):
     def __init__(self):
@@ -43,12 +42,9 @@ class SnoreNet(nn.Module):
         output = self.fc2(output)
         output = torch.abs(self.logs1(output))
         return output
-    
 model = SnoreNet()
 model.load_state_dict(torch.load('snoreNetv1.pt'))
 model.eval()
-
-
 # Audio parameters
 def process_data(waveform_chunks):
     snore = 0
@@ -64,7 +60,6 @@ def process_data(waveform_chunks):
         else:
             snore += 1
     return snore, other
-
 st.sidebar.markdown(
     """
     <div align="justify">
@@ -77,7 +72,6 @@ st.sidebar.markdown(
     unsafe_allow_html=True,
 )
 st.title('Real-Time Snore Detection App 😴')
-
 uploaded_file = st.file_uploader("Upload Sample", type=["wav"])
 if uploaded_file is not None:
     st.write("Analsysing...")
@@ -87,34 +81,17 @@ if uploaded_file is not None:
     num_chunks = len(audio_array) // chunk_size
     waveform_chunks = np.array_split(audio_array[:num_chunks * chunk_size], num_chunks)
     snore, other = process_data(waveform_chunks)
-
     total = snore + other
     snore_percentage = (snore / total) * 100
     other_percentage = (other / total) * 100
-
-    categories = ["Snore", "Other"]
-    percentages = [snore_percentage, other_percentage]
-
-    st.write(f'Snore Percentage: {snore_percentage}')
+    categories = ["Snore"]
+    percentages = [snore_percentage]
     plt.figure(figsize=(8, 4))
-    plt.barh(categories, percentages, color=['#ff0033', '#00ffee'])
+    plt.barh(categories, percentages, color=['#ff0033'])
     plt.xlabel('Percentage')
     plt.title('Percentage of Snoring')
     plt.xlim(0, 100)
-
     for i, percentage in enumerate(percentages):
         plt.text(percentage, i, f' {percentage:.2f}%', va='center')
     st.write("DONE")
-    st.pyplot(plt)
-    
-
-    # # PERCENTAGE OF SNORING PLOT
-
-    
-    
-
-
-
-
-
-
+    st.pyplot(plt)