Update app.py

app.py

@@ -1,4 +1,5 @@
 import gradio as gr
+from gradio_client import Client
 from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
 import torch
 import librosa
@@ -8,6 +9,8 @@ processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-large-960h")
 model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h")
 
 def transcribe_speech(audio_path):
+    if audio_path is None:
+        raise gr.Error("No audio file provided.")
     speech, _ = librosa.load(audio_path, sr=16000)
     input_values = processor(speech, return_tensors="pt", padding="longest").input_values
     with torch.no_grad():
@@ -17,6 +20,8 @@ def transcribe_speech(audio_path):
     return transcription[0]
 
 def get_dreamtalk(image_in, speech):
+    if image_in is None or speech is None:
+        raise gr.Error("Image or speech input is missing.")
     try:
         client = Client("https://fffiloni-dreamtalk.hf.space/")
         result = client.predict(
@@ -31,6 +36,8 @@ def get_dreamtalk(image_in, speech):
         raise gr.Error(f"Error in get_dreamtalk: {str(e)}")
 
 def pipe(text, voice, image_in):
+    if text is None or voice is None or image_in is None:
+        raise gr.Error("All inputs (text, voice, image) are required.")
     try:
         speech = transcribe_speech(voice)
         video = get_dreamtalk(image_in, speech)