import gradio as gr
import math
import numpy as np
from joblib import load

# Load model once
model = load('model3.joblib')

def model2(data):
    input_array = np.array([data]).reshape(1, -1)
    prediction = model.predict(input_array)[0]
    return prediction

def process_input(num_str):
    if len(num_str) != 6 or not num_str.isdigit():
        return "Input must be a 6-digit number."

    # Create 2-digit windows
    windows = [int(num_str[i:i+2]) for i in range(len(num_str)-1)]

    # Divide by 3π
    divisor = 3 * math.pi
    normalized = [x / divisor for x in windows]

    # Get predictions
    preds = [model2(norm) for norm in normalized]

    # Compute errors
    errors = [preds[i] - windows[i+1] for i in range(4)]

    # Combined error and average
    combined_error = sum(errors)
    avg_error = combined_error / len(errors)

    # Find 2 nearest to avg_error
    distances = [abs(e - avg_error) for e in errors]
    nearest_indices = sorted(range(len(distances)), key=lambda i: distances[i])[:2]
    nearest_values = [errors[i] for i in nearest_indices]

    # Mean of nearest + target
    all_three = nearest_values + [avg_error]
    mean_val = np.mean(all_three)

    # Adjust pred4 with all errors
    pred4 = preds[3]
    ads_list = [(pred4 + err if avg_error > 0 else pred4 - err) for err in errors]

    # Multiply by tau
    ads2 = np.array(ads_list, dtype=np.float32) * math.tau

    # Extract digit before decimal
    digit_before_decimal = [int(str(int(x))[-1]) for x in ads2]

    return digit_before_decimal

# Gradio Interface
iface = gr.Interface(
    fn=process_input,
    inputs=gr.Textbox(label="Enter a 6-digit number"),
    outputs=gr.Textbox(label="Digit Before Decimal from Adjusted List")
)

iface.launch()