Update app.py

app.py

@@ -11,17 +11,8 @@ pipe3 = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusio
 pipe3.to("cuda" if torch.cuda.is_available() else "cpu")
 
 def audio_to_image(audio):
-    try:
-        # code sample from onl;ine 
-        if isinstance(audio, tuple):
-            # If Gradio provides (sample rate, numpy array), save it as a temporary file
-            sr, audio_data = audio
-            with tempfile.NamedTemporaryFile(suffix=".wav") as temp_audio_file:
-                librosa.output.write_wav(temp_audio_file.name, audio_data, sr)
-                transcription = pipe1(temp_audio_file.name)
-        else:
-            # If Gradio provides a file path, use it directly
-            transcription = pipe1(audio)
+    
+        transcription = pipe1(audio)
 
         transcription_text = transcription['text']
         
@@ -32,9 +23,6 @@ def audio_to_image(audio):
         image = pipe3(prompt).images[0]
         
         return image
-    except Exception as e:
-        print(f"Error during processing: {e}")
-        return None
-
+    
 demo = gr.Interface(fn=audio_to_image, inputs=gr.Audio(), outputs="image")
 demo.launch(share=True)