import gradio as gr
import pandas as pd
import joblib
from huggingface_hub import hf_hub_download

# Download model + columns from your repo
model_path = hf_hub_download(repo_id="MAZEN00/Player", filename="modeel.joblib")
columns_path = hf_hub_download(repo_id="MAZEN00/Player", filename="columns.pkl")

# Load them
model = joblib.load(model_path)
model_columns = joblib.load(columns_path)

# Prediction function
def predict_player(player: dict):
    try:
        print("Received:", player)   # <--- log input
        df = pd.DataFrame([player])
        df = df[model_columns]
        prediction = model.predict(df)
        print("Prediction:", prediction)  # <--- log output
        return float(prediction[0])
    except Exception as e:
        print("Error:", e)  # <--- log errors
        return {"error": str(e)}


# Gradio Interface
iface = gr.Interface(
    fn=predict_player,
    inputs=gr.JSON(label="Player Features"),
    outputs=gr.Number(label="Predicted Rating"),
    title="⚽ Player Rating Predictor",
    description="Enter player features (JSON) to get predicted rating."
)

# Hugging Face Spaces expects launch like this
if __name__ == "__main__":
    iface.launch(server_name="0.0.0.0", server_port=7860)