import gradio as gr
from xgboost import XGBRegressor
import numpy as np

# --- Load your trained model ---
model = XGBRegressor()
model.load_model("2context7columns.json")

# --- Function to create sliding windows ---
def create_sliding_windows(input_str, window_size=2):
    windows = [input_str[i:i+window_size] for i in range(len(input_str)-window_size+1)]
    # Convert to float by prepending 0.
    return [float("0." + w) for w in windows]

# --- Function for Gradio ---
def predict_final_answer(input_str):
    input_str = input_str.strip()
    if len(input_str) != 7 or not input_str.isdigit():
        return "Error: Input must be exactly 7 digits."
    
    # Create sliding windows
    features = create_sliding_windows(input_str, window_size=2)
    
    if len(features) != 6:
        return "Error: Number of features must be 6 for prediction."
    
    # Convert to 2D array for XGBoost
    features_array = np.array(features).reshape(1, -1)
    
    # Predict tier7
    prediction = model.predict(features_array)
    ans = f"{prediction[0]:.9f}"

    return ans

# --- Gradio Interface ---
iface = gr.Interface(
    fn=predict_final_answer,
    inputs=gr.Textbox(label="Enter 7-digit string"),
    outputs=gr.Textbox(label="Final Answer"),
    title="Tier7 Predictor",
    description="Enter a 7-digit string to predict tier7 value (returns final digits)."
)

iface.launch()