Update app.py

app.py

@@ -6,7 +6,7 @@ import transformers
 
 from datasets import load_dataset
 import torch
- from transformers import pipeline
+from transformers import pipeline
 
 def predict(speech):
  # load model and tokenizer
@@ -14,9 +14,9 @@ def predict(speech):
    ds = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
    audio_file = ds[0]["audio"]["path"]
    audio_classifier = pipeline(
-    task="audio-classification", model="ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"
-    preds = audio_classifier(audio_file)
-    preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
+   task="audio-classification", model="ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"
+   preds = audio_classifier(audio_file)
+   preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
    return preds
    
 demo = gr.Interface(fn=predict, inputs='texts'  outputs="texts")