import gradio as gr
import numpy as np
from audio_features import extract_features_with_time_series
from model_utils import load_model, prepare_input_for_model, interpret_prediction

MODEL_PATH = "best_audio_model.keras"

# Load the model once
model = load_model(MODEL_PATH)

def predict_audio(file_path):
    try:
        features = extract_features_with_time_series(file_path)
        if features is None:
            return {"Error": "Feature extraction failed. Please check the audio file."}

        input_data = prepare_input_for_model(features, model)
        raw_pred = model.predict(input_data)
        result = interpret_prediction(raw_pred)
        return result
    except Exception as e:
        return {"Error": str(e)}

iface = gr.Interface(
    fn=predict_audio,
    inputs=gr.Audio(type="filepath", label="Upload an audio file"),
    outputs=gr.Label(num_top_classes=2, label="Prediction"),
    title="Audio Deepfake Detection",
    description="Upload an audio clip to check if it's Real or Fake using your trained .keras model."
)

if __name__ == "__main__":
    iface.launch(server_name="0.0.0.0", server_port=7860)