import gradio as gr
import joblib
import pandas as pd

model = joblib.load("spotify_tree_model.pkl")

def predict_hit(danceability, energy, loudness, speechiness, acousticness,
                instrumentalness, liveness, valence, tempo):

    data = pd.DataFrame([[danceability, energy, loudness, speechiness,
                          acousticness, instrumentalness, liveness, valence, tempo]],
                        columns=['danceability', 'energy', 'loudness', 'speechiness',
                                 'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo'])

    pred = model.predict(data)[0]

    if pred == 1:
        result = "🔥 Hit Song!"
        confidence = 100.0
    else:
        result = "Not a Hit"
        confidence = 100.0

    return f"{result}\nConfidence: {confidence:.1f}%"

demo = gr.Interface(
    fn=predict_hit,
    inputs=[
        gr.Slider(0, 1, step=0.01, label="Danceability"),
        gr.Slider(0, 1, step=0.01, label="Energy"),
        gr.Slider(-60, 0, step=0.1, label="Loudness"),
        gr.Slider(0, 1, step=0.01, label="Speechiness"),
        gr.Slider(0, 1, step=0.01, label="Acousticness"),
        gr.Slider(0, 1, step=0.01, label="Instrumentalness"),
        gr.Slider(0, 1, step=0.01, label="Liveness"),
        gr.Slider(0, 1, step=0.01, label="Valence"),
        gr.Slider(50, 200, step=1, label="Tempo")
    ],
    outputs=gr.Textbox(label="Prediction"),
    title="Spotify Hit Predictor",
    description="Enter song audio features to predict if it will be a hit"
)

demo.launch()