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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}")
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