Update app.py

app.py

@@ -45,7 +45,26 @@ WIN_SIZE = 1024
 WINDOW_TYPE = "hann"
 FEATURE = "mel"
 FMIN = 0
+def preprocess_and_return_spectrogram_from_signal(signal):
+    # Plot the spectrogram
+    plt.figure(figsize=(10, 4))
+    D = librosa.amplitude_to_db(np.abs(librosa.stft(signal)), ref=np.max)
+    librosa.display.specshow(D, y_axis="linear")
+    plt.colorbar(format="%+2.0f dB")
+    plt.title("Linear-frequency power spectrogram")
+
+    # Create a BytesIO object to capture the image data
+    image_buffer = io.BytesIO()
+
+    # Save the spectrogram image to the BytesIO object
+    plt.savefig(image_buffer, format="png", bbox_inches="tight")
+    plt.close()
+
+    # Reset the BytesIO object's position to the beginning
+    image_buffer.seek(0)
 
+    # Return the BytesIO object
+    return image_buffer
 
 def preprocess_and_save_spectrogram_from_signal(signal, image_path):
     # Plot the spectrogram and save it
@@ -176,28 +195,15 @@ def model():
             if st.button('Predict'):
                 st.write('Predicting using', selected_model)
 
-                # get current time
-                now = datetime.now()
-
-                # Generate a random number (e.g., a 6-digit number)
-                random_number = random.randint(100000, 999999)
-
-                # Create a new file name with the random number
-                file_name = f'input_{random_number}.png'
+               
+                
 
-                # Define the full path to the image
-                image_path = fr'C:\Users\jaini\PycharmProjects\nlp\DL\temp_images\{file_name}'
-
-                # Call the `saveMel` function with the updated image_path
-                # plot_spectrogram_and_save(y, image_path)
-
-                preprocess_and_save_spectrogram_from_signal(y, image_path)
-                # preprocess_and_save_spectrogram_from_signal_v2(y, image_path)
-                image_ = Image.open(image_path)
-                st.image(image_, caption='Specrogram', use_column_width=True)
 
+                image_buffer = preprocess_and_return_spectrogram_from_signal(y)
+                image = Image.open(image_buffer)
+                st.image(image, caption='Spectrogram', use_column_width=True)
+                
                 # Load the selected model
-
                 model_path = os.path.join('model', selected_model)
                 model = tf.keras.models.load_model(model_path)