app.py

import gradio as gr
import torch
import librosa
from transformers import Wav2Vec2FeatureExtractor, Wav2Vec2ForSequenceClassification

MODEL_NAME = "ameliabb0913/emotion-classifier1"
processor = Wav2Vec2FeatureExtractor.from_pretrained(MODEL_NAME)
model = Wav2Vec2ForSequenceClassification.from_pretrained(MODEL_NAME, trust_remote_code=True)
model.eval()


# Emotion labels (based on the dataset used to train the model)
id2label = {
    0: "Neutral",
    1: "Happy",
    2: "Sad",
    3: "Angry",
    4: "Fearful",
    5: "Disgusted",
    6: "Surprised"
}

emotion_emojis = {
    "Neutral": "😐",
    "Happy": "😊",
    "Sad": "😢",
    "Angry": "😠",
    "Fearful": "😨",
    "Disgusted": "🤢",
    "Surprised": "😲"
}



# Function to classify emotions from audio
def classify_emotion(audio_file):
    # Load and process audio
    speech, sr = librosa.load(audio_file, sr=16000)
    inputs = processor(
        speech,
        sampling_rate=16000,
        return_tensors="pt",
        padding="max_length",
        truncation=True,
        max_length=48000  # You can adjust this depending on expected audio length
        )


    # Get predictions
    with torch.no_grad():
        logits = model(**inputs).logits
    predicted_class_id = torch.argmax(logits, dim=-1).item()
    
    # Convert class ID to emotion label
    predicted_emotion = id2label.get(predicted_class_id, "Unknown")
    emoji = emotion_emojis.get(predicted_emotion, "❓")

    return f"Predicted Emotion: {predicted_emotion} {emoji}"


# Gradio Interface
interface = gr.Interface(
    fn=classify_emotion,
    inputs=gr.Audio(type="filepath"),
    outputs="text",
   title="🎧 Speak Your Emotion | AI Emotion Detector",
description=(
    "🎤 Upload a voice clip or speak into the mic — this AI will identify the **emotion** in your voice!\n\n"
    "**Supported 8 Emotions**: Neutral, Happy, Sad, Angry, Fearful, Disgusted, Surprised."
))


# Launch the app
if __name__ == "__main__":
    interface.launch()