import streamlit as st
import cv2
from streamlit_drawable_canvas import st_canvas
from keras.models import load_model
import numpy as np

# Sidebar controls
st.sidebar.title("Canvas Settings")
drawing_mode = st.sidebar.selectbox("Drawing tool:", ("freedraw", "line", "rect", "circle", "transform"))
stroke_width = st.sidebar.slider("Stroke width: ", 1, 25, 10)
stroke_color = st.sidebar.color_picker("Stroke color hex: ", "#000000")  # black
bg_color = st.sidebar.color_picker("Background color hex: ", "#FFFFFF")  # white
bg_image = st.sidebar.file_uploader("Background image:", type=["png", "jpg"])
realtime_update = st.sidebar.checkbox("Update in realtime", True)

# Load model with caching
@st.cache_resource
def load_mnist_model():
    return load_model("mnist_model.keras")

model = load_mnist_model()

st.title("🖌️ Mindist: Draw a Number, Predict Instantly")

# Create a two-column layout
col1, col2 = st.columns([1, 1])

with col1:
    st.subheader("Draw Here 👇")
    canvas_result = st_canvas(
        fill_color="rgba(255, 165, 0, 0.3)",
        stroke_width=stroke_width,
        stroke_color=stroke_color,
        background_color=bg_color,
        update_streamlit=realtime_update,
        height=280,
        width=280,
        drawing_mode=drawing_mode,
        key="canvas",
    )

with col2:
    if canvas_result.image_data is not None:
        st.subheader("Original Drawing")
        st.image(canvas_result.image_data, use_column_width=True)

# Below the two columns: Show preprocessing and prediction
if canvas_result.image_data is not None:
    st.markdown("---")
    st.subheader("Preprocessed Image & Prediction")
    
    img = cv2.cvtColor(canvas_result.image_data.astype("uint8"), cv2.COLOR_RGBA2GRAY)
    img = 255 - img  # Invert colors
    img_resized = cv2.resize(img, (28, 28))
    img_normalized = img_resized / 255.0
    final_img = img_normalized.reshape(1, 28, 28, 1)
    
    col3, col4 = st.columns([1, 1])
    with col3:
        st.image(img_resized, caption="28x28 Preprocessed", clamp=True, channels="GRAY")
    with col4:
        prediction = model.predict(final_img)
        predicted_digit = np.argmax(prediction)
        st.markdown(f"### 🧠 Predicted Digit: **{predicted_digit}**")