Update app.py

app.py

@@ -1,19 +1,58 @@
 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
 
-st.title("Canvas Drawing App")
+# 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=10,  # Stroke width
+    stroke_width=12,  # Stroke width
     stroke_color="#000000",  # Stroke color
     background_color="#ffffff",  # Canvas background color
     update_streamlit=True,
-    height=500,
-    width=500,
+    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}")
+