app update

app.py

@@ -3,6 +3,8 @@ from st_audiorec import st_audiorec
 import matplotlib.pyplot as plt
 import sounddevice as sd
 import numpy as np
+import pandas as pd
+
 
 # Session state
 if 'text' not in st.session_state:
@@ -40,33 +42,43 @@ with st.expander('About this App'):
     how much they are snoring during their sleep.
     ''')
 
-# col1, col2, col3 = st.columns(3)
-# col1.button('Start', on_click=start_monitoring)
-# col3.button('Stop', on_click=stop_monitoring)
-# record_button = col3.button('Monitor')
+
+
 wav_audio_data = st_audiorec()
 if wav_audio_data is not None:
     np_wav = np.frombuffer(wav_audio_data, dtype=np.int16)
-    time_axis = np.linspace(0, len(np_wav) / 16000, num=len(np_wav))
 
-    plt.figure(figsize=(10, 4))
-    plt.plot(time_axis, np_wav)
-    plt.title("Original Waveform")
-    plt.xlabel("Time (s)")
-    plt.ylabel("Amplitude")
+    # PERCENTAGE OF SNORING PLOT
+
+
+    model = np.random.rand(4, 5)
+
+    snore = 0
+    other = 0
+
+    for row in model:
+        for element in row:
+            if element > 0.5:
+                snore += 1
+            else:
+                other += 1
+
+    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 "Snore" and "Other"')
+    plt.xlim(0, 100)
+
+    for i, percentage in enumerate(percentages):
+        plt.text(percentage, i, f' {percentage:.2f}%', va='center')
+
     st.pyplot(plt)
-# if wav_audio_data is not None:
-#     st.audio(wav_audio_data, format=FORMAT, sample_rate=16000)
     
-    # audio_chunk = stream.read(FRAMES_PER_BUFFER)
-    # audio_data.append(np.frombuffer(audio_chunk, dtype=np.int16))
-
-    # if monitoring and len(audio_chunk) >= RATE // FRAMES_PER_BUFFER:
-    #     audio_data_chunk = np.concatenate(audio_chunk[:RATE//FRAMES_PER_BUFFER])
-    #     print("PROCESSING BY MODEL GOES HERE")
-    #     # if model is not None:
-    #     #     input_tensor = torch.tensor(audio_data_chunk)
-    #     #     with torch.no_grad():
-    #     #         output = model(input_tensor)
-        
-    #     audio_chunk = audio_chunk[1:]
+