import gradio as gr
import numpy as np
import cv2
from tensorflow.keras.models import load_model

model = load_model("saved_models/model.h5")

def predict_digit(image):
    img = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    img = cv2.resize(img, (28, 28))
    img = cv2.bitwise_not(img)
    _, img = cv2.threshold(img, 100, 255, cv2.THRESH_BINARY)

    img = img.astype("float32") / 255.0
    img = np.expand_dims(img, axis=-1)
    img = np.expand_dims(img, axis=0)

    prediction = model.predict(img)
    digit = np.argmax(prediction)
    confidence = np.max(prediction) * 100

    return f"🔢 Predicted Digit: {digit}\n📊 Confidence: {confidence:.2f}%"

gr.Interface(
    fn=predict_digit,
    inputs=gr.Image(shape=(200, 200), tool="editor", label="Draw a digit"),
    outputs="text",
    title="🧠 Handwritten Digit Recognizer",
    description="Draw a digit (0–9) and let the model predict it"
).launch()