svarshas/audio-sentiment-classification-project

1# frontend/app.py2from flask import Flask, render_template, request3import torch4import librosa5from cnn_model import CNNEmotionClassifier, extract_features_from_waveform6 7# Flask app8app = Flask(__name__)9 10# Device11device = torch.device("cuda" if torch.cuda.is_available() else "cpu")12 13# Load CNN model14model = CNNEmotionClassifier()15model_path = "best_cnn_model.pt"16model.load_state_dict(torch.load(model_path, map_location=device))17model.to(device)18model.eval()19 20# Labels21LABELS = ["neutral", "calm", "happy", "sad", "angry", "fearful", "disgust", "surprised"]22 23# Flask routes24@app.route("/", methods=["GET", "POST"])25def index():26    result = None27    if request.method == "POST":28        if "file" in request.files and request.files["file"].filename != "":29            file = request.files["file"]30 31            import tempfile32            with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp:33                file.save(tmp.name)34                y, sr = librosa.load(tmp.name, sr=None)35                x = extract_features_from_waveform(y,sr)36 37            with torch.no_grad():38                logits = model(x)39                pred_idx = torch.argmax(logits, dim=1).item()40                result = LABELS[pred_idx]41 42    return render_template("index.html", result=result)43 44# Run app45if __name__ == "__main__":46    app.run(debug=True)47