Update app.py

app.py

@@ -5,7 +5,17 @@ from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
 import tempfile
 
 analyzer = SentimentIntensityAnalyzer()
+from transformers import pipeline
 
+# Load models
+whisper = pipeline("automatic-speech-recognition", model="openai/whisper-small")
+emotion_model = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
+
+# Use Whisper to transcribe uploaded audio
+def analyze_audio(audio_file):
+    text = whisper(audio_file)["text"]
+    emotions = emotion_model(text)[0]
+    return text, sorted(emotions, key=lambda x: x['score'], reverse=True)
 def analyze_text(text):
     score = analyzer.polarity_scores(text)
     if score['compound'] >= 0.05: