big update

app.py

@@ -5,7 +5,7 @@ import sounddevice as sd
 import numpy as np
 import pandas as pd
 import torch
-import torchaudio
+# import torchaudio
 import wave
 import io
 from scipy.io import wavfile
@@ -15,7 +15,11 @@ import time
 # MODEL LOADING and INITIALISATION
 model = torch.jit.load("snorenetv1_small.ptl")
 model.eval()
-
+endReached = False
+snore = 0
+other = 0
+s=0
+n=16000
 
 # Audio parameters
 
@@ -32,86 +36,47 @@ st.sidebar.markdown(
 )
 st.title('Real-Time Snore Detection App 😴')
 
-upload_file = st.file_uploader("Upload wav file", type=["wav"])
-if upload_file is not None:
-    file_details = {
-        "Filename": upload_file.name,
-        "Filesize":f"{upload_file.size / 1024:.2f} KB",
-        "File Type": upload_file.type,
-    }
-    st.write("File Details:", file_details)
-
-
-# wav_audio_data = None
-# if wav_audio_data is not None:
-#     data = np.frombuffer(wav_audio_data, dtype=np.int16)
-#     st.write(len(data))
-#     duration = len(data)//110000
-#     num_of_samples = len(data)
-#     sample_rate = num_of_samples // duration
-#     # data = np.array(wav_audio_data, dtype=float)
-#     max_abs_value = np.max(np.abs(data))
-#     np_array = (data/max_abs_value) * 32767
-#     scaled_data = np_array.astype(np.int16).tobytes()
-#     with io.BytesIO() as fp, wave.open(fp, mode="wb") as waveobj:
-#         waveobj.setnchannels(1)
-#         waveobj.setframerate(96000)
-#         waveobj.setsampwidth(2)
-#         waveobj.setcomptype("NONE", "NONE")
-#         waveobj.writeframes(scaled_data)
-#         wav_make = fp.getvalue()
-    
-    # with open("output.wav", 'wb') as wav_file:
-    #     wav_file.write(wav_make)
-    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)
+uploaded_file = st.file_uploader("Upload Sample", type=["wav"])
+if uploaded_file is not None:
+    st.write("Saving File.....")
+    audio = pydub.AudioSegment.from_wav(uploaded_file)
+    audio.export(uploaded_file.name, format='wav')
+    time.sleep(2)
+    st.write("File Saved!")
+
+    st.write("Analysing...")
+    while(endReached==False):
+        sr, waveform = wavfile.read(uploaded_file.name)
+        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
+if endReached == True:
+    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)