pages/4_Writing.py

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


arabic_chars = ['alef','beh','teh','theh','jeem','hah','khah','dal','thal','reh','zain','seen','sheen',
                'sad','dad','tah','zah','ain','ghain','feh','qaf','kaf','lam','meem','noon','heh','waw','yeh']




def add_logo():
    st.markdown(
        """
        <style>
            [data-testid="stSidebarNav"] {
                /*background-image: url(http://placekitten.com/200/200);*/
                background-repeat: no-repeat;
                #padding-top: 120px;
                background-position: 20px 20px;
            }
            [data-testid="stSidebarNav"]::before {
                content: "MO3ALIMI sidebar";
                margin-left: 20px;
                margin-top: 20px;
                font-size: 29px;
                position: relative;
                top: 0px;
            }
        </style>
        """,
        unsafe_allow_html=True,
    )
add_logo()
def predict_image(image_path, model_path):
    model = load_model(model_path)

    img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
    img = cv2.resize(img, (32, 32))
    img = img.reshape(1, 32, 32, 1)
    img = img.astype('float32') / 255.0

    pred = model.predict(img)
    predicted_label = arabic_chars[np.argmax(pred)]

    return predicted_label

canvas_result = st_canvas(
    fill_color="rgba(255, 255, 255, 0.3)",  # Filled color (white)
    stroke_width=30,  # Stroke width
    stroke_color="#FFFFFF",  # Stroke color (white)
    background_color="#000000",  # Canvas background color (black)
    update_streamlit=True,
    height=400,
    width=400,
    drawing_mode="freedraw",
    key="canvas",
)

if st.button("Predict"):
    if canvas_result.image_data is not None:
        image = canvas_result.image_data
        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        image = cv2.resize(image, (32, 32))
        cv2.imwrite("temp_image.png", image)

        model_path = "saved_model.h5"  # Replace with the path to your trained model
        predicted_label = predict_image("temp_image.png", model_path)

        st.write(f"Predicted Character: {predicted_label}")
    else:
        st.write("Please draw something on the canvas.")