import gradio as gr
from huggingface_hub import hf_hub_download
import joblib
import numpy as np

# Load model from Hub
model_repo = "leorigasaki54/earthquake-magnitude-predictor"
model = joblib.load(hf_hub_download(repo_id=model_repo, filename="rf_earthquake_mag.joblib"))

def predict(feature1, feature2, feature3, feature4, feature5):
    input_data = np.array([[feature1, feature2, feature3, feature4, feature5]])
    return model.predict(input_data)[0]

# Adjust labels/features based on your model's input requirements
inputs = [
    gr.Number(label="Feature 1 (e.g., Seismic Signal)"),
    gr.Number(label="Feature 2 (e.g., Depth)"),
    gr.Number(label="Feature 3 (e.g., Latitude)"),
    gr.Number(label="Feature 4 (e.g., Longitude)"),
    gr.Number(label="Feature 5 (e.g., Station Distance)")
]

gr.Interface(
    fn=predict,
    inputs=inputs,
    outputs=gr.Number(label="Predicted Magnitude"),
    title="Earthquake Magnitude Predictor",
    description="Predict earthquake magnitude using seismic data"
).launch()