Update src/emotion_detector.py

src/emotion_detector.py

@@ -1,18 +1,24 @@
-import streamlit as st
-from emotion_detector import detect_emotion
+# emotion_detector.py
+import torchaudio
+from speechbrain.inference.classifiers import AudioClassifier
 
-st.set_page_config(page_title="Real-Time Emotion to Emoji", page_icon="🎧")
-st.title("🎧 Real-Time Emotion to Emoji")
+# Load pretrained model
+classifier = AudioClassifier.from_hparams(
+    source="speechbrain/emotion-recognition-wav2vec2-IEMOCAP",
+    savedir="pretrained_models/emotion-recognition"
+)
 
-st.markdown("""
-Upload a 5-second **WAV** audio clip of your voice, and the AI will detect your emotion and show an emoji!
-""")
+EMOTION_EMOJIS = {
+    "angry": "😠",
+    "happy": "😄",
+    "neutral": "😐",
+    "sad": "😢",
+    "fearful": "😨"
+}
 
-uploaded_file = st.file_uploader("📤 Upload your 5-second WAV audio", type=["wav"])
-
-if uploaded_file is not None:
-    st.audio(uploaded_file, format='audio/wav')
-
-    with st.spinner("Detecting emotion..."):
-        emotion, emoji = detect_emotion(uploaded_file)
-        st.markdown(f"## {emoji} Emotion: **{emotion.title()}**")
+def detect_emotion(audio_file):
+    signal, fs = torchaudio.load(audio_file)
+    prediction = classifier.classify_file(audio_file)
+    emotion = prediction[3]  # This is the predicted label
+    emoji = EMOTION_EMOJIS.get(emotion.lower(), "❓")
+    return emotion, emoji