#from unicodedata import numeric
import gradio as gr
import pandas as pd
import numpy as np
from joblib import load

def predict_price(vol_moving_avg, adj_close_rolling_med):
    
    try:
        vol_moving_avg = float(vol_moving_avg)
        adj_close_rolling_med = float(adj_close_rolling_med)
    except Exception:
        return "Invalid input format. Please provide a numeric value."
    
    # Load the ML model and scaler
    model = load("model.joblib")
    scaler = load('scaler.joblib')

    # Create dict array from parameters
    data={
       "vol_moving_avg":[vol_moving_avg],
       "adj_close_rolling_med":[adj_close_rolling_med]
    }

    xin=pd.DataFrame(data)
    X = scaler.transform(xin)
    price=model.predict(X)
    return int(price[0])

ui=gr.Interface(
    fn=predict_price,
    inputs=[ 
        gr.components.Textbox(placeholder="vol_moving_avg", label="Volume Moving Avg"),
        gr.components.Textbox(placeholder="adj_close_rolling_med", label="Adj Close Rolling Mean")
    ],
    title="Risk Thinking Volume Predictor",
    outputs=[gr.components.Textbox(label="Volume")]
)

if __name__=="__main__":
    ui.launch()