big update

app.py

@@ -45,17 +45,22 @@ if uploaded_file is not None:
     time.sleep(2.5)
     audio, sample_rate = librosa.load(uploaded_file, sr=None)
     waveform = audio
-    while(endReached == False):
-        input_tensor = torch.tensor(waveform[s:n]).unsqueeze(0).to(torch.float32)
+    # Set the chunk size
+    chunk_size = 16000
+
+    # Calculate the number of chunks
+    num_chunks = len(waveform) // chunk_size
+
+    # Reshape the waveform into chunks
+    waveform_chunks = np.array_split(waveform[:num_chunks * chunk_size], num_chunks)
+    
+    for chunk in waveform_chunks:
+        input_tensor = torch.tensor(chunk).unsqueeze(0).to(torch.float32)
         result = model(input_tensor)
         if np.abs(result[0][0]) > np.abs(result[0][1]):
             other += 1
         else:
             snore += 1
-        s += 16000
-        n += 16000
-        if(n>=len(waveform)):
-            endReached = True
 
     total = snore + other
     snore_percentage = (snore / total) * 100
@@ -64,16 +69,17 @@ if uploaded_file is not None:
     categories = ["Snore", "Other"]
     percentages = [snore_percentage, other_percentage]
 
-    plt.figure(figsize=(8, 4))
-    plt.barh(categories, percentages, color=['#ff0033', '#00ffee'])
-    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)
+    st.write(f'Snore Percentage: {snore_percentage}')
+    # plt.figure(figsize=(8, 4))
+    # plt.barh(categories, percentages, color=['#ff0033', '#00ffee'])
+    # 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