app.py

import streamlit as st
import torch
import torchaudio
import tempfile
from pydub import AudioSegment
from transformers import AutoFeatureExtractor, Wav2Vec2ForSequenceClassification

@st.cache_resource
def load_model():
    extractor = AutoFeatureExtractor.from_pretrained("superb/wav2vec2-base-superb-er")
    model = Wav2Vec2ForSequenceClassification.from_pretrained("superb/wav2vec2-base-superb-er")
    model.eval()
    return extractor, model

def convert_to_wav(uploaded_file):
    audio = AudioSegment.from_file(uploaded_file)
    audio = audio.set_frame_rate(16000).set_channels(1)
    temp_path = tempfile.mktemp(suffix=".wav")
    audio.export(temp_path, format="wav")
    return temp_path

def get_emotion_label(logits):
    emotions = ["angry", "happy", "neutral", "sad"]
    scores = torch.softmax(logits, dim=0).tolist()
    top_idx = scores.index(max(scores))
    return emotions[top_idx], scores

def analyze_emotion(audio_path):
    extractor, model = load_model()
    waveform, sr = torchaudio.load(audio_path)

    # 💡 Trim audio to 30 seconds max to avoid slowdowns
    max_duration_sec = 30
    max_samples = sr * max_duration_sec
    if waveform.size(1) > max_samples:
        waveform = waveform[:, :max_samples]

    duration_sec = waveform.size(1) / sr

    # Run model
    inputs = extractor(waveform[0].numpy(), sampling_rate=16000, return_tensors="pt")
    with torch.no_grad():
        logits = model(**inputs).logits[0]

    emotion, scores = get_emotion_label(logits)
    return emotion.capitalize(), scores, duration_sec

# Streamlit UI
st.set_page_config(page_title="🎧 Audio Emotion Detector", layout="centered")
st.title("🎧 Audio Emotion Analysis (Wav2Vec2)")

uploaded_file = st.file_uploader("Upload an MP3 or WAV audio file", type=["mp3", "wav"])

if uploaded_file:
    st.audio(uploaded_file, format='audio/wav')
    with st.spinner("Analyzing emotion..."):
        wav_path = convert_to_wav(uploaded_file)
        emotion, scores, duration_sec = analyze_emotion(wav_path)

        st.subheader("⏱ Audio Info:")
        st.write(f"Duration analyzed: **{duration_sec:.2f} seconds**")

        st.subheader("🧠 Detected Emotion:")
        st.markdown(f"**{emotion}**")

        st.subheader("🎯 Confidence Scores:")
        emotions = ["angry", "happy", "neutral", "sad"]
        for i, label in enumerate(emotions):
            st.write(f"- **{label.capitalize()}**: {scores[i]*100:.2f}%")