update

app.py

@@ -13,103 +13,94 @@ from scipy.io import wavfile
 model = torch.jit.load("snorenetv1_small.ptl")
 model.eval()
 
-# Session state
-if 'text' not in st.session_state:
-    st.session_state['text'] = 'Listening...'
-    st.session_state['run'] = False
 
 # Audio parameters
-st.sidebar.header('Audio Parameters')
-
-FRAMES_PER_BUFFER = int(st.sidebar.text_input('Frames per buffer', 3200))
-FORMAT = 'audio/wav'
-CHANNELS = 1
-RATE = int(st.sidebar.text_input('Rate', 16000))
-
-# Open an audio stream
-
-monitoring = False
-audio_data = []
-
-def start_monitoring():
-    global monitoring
-    st.session_state['run'] = True
-    monitoring = True
-
-def stop_monitoring():
-    global monitoring
-    st.session_state['run'] = False
-    monitoring = False
-
-st.title('🎙️ Real-Time Snore Detection App')
-
-with st.expander('About this App'):
-    st.markdown('''
-    This streamlit app from Hypermind Labs Helps users detect
-    how much they are snoring during their sleep.
-    ''')
-
-
-
-wav_audio_data = st_audiorec()
-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)
-    sr, waveform = wavfile.read('output.wav')
-    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)
+st.sidebar.markdown(
+    """
+    <div align="justify">
+        <h4>ABOUT</h4>
+        <p>Transform your sleep experience with the cutting-edge Snore Detector by Hypermind Labs!
+        Discover the power to monitor and understand your nighttime sounds like never before.
+        Take control of your sleep quality and uncover the secrets of your peaceful slumber with our innovative app.</p>
+    </div>
+    """,
+    unsafe_allow_html=True,
+)
+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)
+
+    with open("saved_file.wav", "wb") as f:
+        f.write(upload_file.getvalue())
+
+# 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)
+
+sr, waveform = wavfile.read('saved_file.wav')
+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)