big update

app.py

@@ -10,6 +10,7 @@ import wave
 import io
 from scipy.io import wavfile
 import pydub
+import time
 
 # MODEL LOADING and INITIALISATION
 model = torch.jit.load("snorenetv1_small.ptl")
@@ -62,46 +63,55 @@ if upload_file is not None:
     
     # with open("output.wav", 'wb') as wav_file:
     #     wav_file.write(wav_make)
-    audio = pydub.AudioSegment.from_wav(upload_file)
-    audio.export(upload_file.name, format='wav')
-    sr, waveform = wavfile.read(upload_file.name)
-    snore = 0
-    other = 0
-    s=0
-    n=16000
-    endReached = False
-
-    while(endReached==False):
-        input_tensor = torch.tensor(waveform[s:n]).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
-
-    # PERCENTAGE OF SNORING PLOT
-
-    total = snore + other
-    snore_percentage = (snore / total) * 100
-    other_percentage = (other / total) * 100
-
-    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.pyplot(plt)
+    file_uploaded = False
+    if file_uploaded == False:
+        st.write("Uploading File.....")
+        audio = pydub.AudioSegment.from_wav(upload_file)
+        file_uploaded = True
+        time.sleep(2)
+        st.write("File Uploaded!")
+    
+    if file_uploaded == True:
+        st.write("Analysing...")
+        audio.export(upload_file.name, format='wav')
+        sr, waveform = wavfile.read(upload_file.name)
+        snore = 0
+        other = 0
+        s=0
+        n=16000
+        endReached = False
+
+        while(endReached==False):
+            input_tensor = torch.tensor(waveform[s:n]).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
+
+        # PERCENTAGE OF SNORING PLOT
+
+        total = snore + other
+        snore_percentage = (snore / total) * 100
+        other_percentage = (other / total) * 100
+
+        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)