Update app.py

app.py

@@ -1,31 +1,14 @@
-import librosa
 import gradio as gr
 import numpy as np
 import librosa
-import soundfile as sf
 
-def scale_minmax(X, min=0.0, max=1.0):
-    X_std = (X - X.min()) / (X.max() - X.min())
-    X_scaled = X_std * (max - min) + min
-    return X_scaled
-def greet(name):
-    print("np array" , name)
-    #step 0  revert the things done to get back to the raw spectrogram
-    # i
-    #img = np.flip(img, axis=0) # put low frequencies at the bottom in image
-    img = (255-name) *-1
-    img = scale_minmax(img, 0, 1).astype(np.float64) 
-   
-   # step1 - converting a wav file to numpy array and then converting that to mel-spectrogram
-    S_inv = librosa.feature.inverse.mel_to_audio(img)
-  
-#Export wav
-    
-# step4 - save it as a wav file
-    return   (16000,S_inv)
-    #mel -> audio
+# import the librosa library for converting a Mel spectrogram image to audio
 
-iface = gr.Interface(fn=greet, inputs=gr.Image(label="Mel spectrogram", image_mode="L"), outputs=gr.Audio(label="Audio"))
-iface.launch()
+def mel_to_audio(mel_spectrogram):
+  # convert the Mel spectrogram image to audio using librosa
+  audio = librosa.feature.inverse.mel_to_audio(mel_spectrogram)
+  return audio
 
-#Read melpectogram of npy file
+# create the gradio app
+app = gr.Interface(mel_to_audio, "image", "audio")
+app.launch()