app.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
from PIL import Image

# Define the list of Arabic characters
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']

# Define the prediction function
def predict_image(image, model_path):
    model = load_model(model_path)

    img = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2GRAY)
    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

# Streamlit app
st.title("Arabic Character Recognition App")

canvas_result = st_canvas(
    fill_color="rgba(255, 165, 0, 0.3)",  # Filled color
    stroke_width=12,  # Stroke width
    stroke_color="#000000",  # Stroke color
    background_color="#ffffff",  # Canvas background color
    update_streamlit=True,
    height=400,
    width=400,
    drawing_mode="freedraw",
    key="canvas",
)

if canvas_result.image_data is not None:
    # Display the drawn image
    st.image(canvas_result.image_data)

    # Convert the canvas image data to a PIL image
    image = Image.fromarray(canvas_result.image_data.astype('uint8'), 'RGBA').convert('RGB')

    # Save the image to a temporary file
    temp_image_path = "temp_drawing.png"
    image.save(temp_image_path)

    # Predict the character
    model_path = "path_to_your_model.h5"  # Update with your model path
    predicted_char = predict_image(image, model_path)

    # Display the predicted character
    st.subheader(f"Predicted Character: {predicted_char}")