Update app.py

app.py

@@ -6,18 +6,29 @@ 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']
+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)
+# Load the model once at the beginning
+model_path = "saved_model.h5"  # Update with your model path
+model = load_model(model_path)
 
+# Define the prediction function
+def predict_image(image):
+    # Convert to grayscale
     img = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2GRAY)
+    
+    # Invert the image colors (black background with white letter)
+    img = cv2.bitwise_not(img)
+    
+    # Resize the image to the size expected by the model
     img = cv2.resize(img, (32, 32))
+    
+    # Reshape and normalize the image
     img = img.reshape(1, 32, 32, 1)
     img = img.astype('float32') / 255.0
 
+    # Predict the character
     pred = model.predict(img)
     predicted_label = arabic_chars[np.argmax(pred)]
 
@@ -29,8 +40,8 @@ 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
+    stroke_color="#FFFFFF",  # Stroke color (white)
+    background_color="#000000",  # Canvas background color (black)
     update_streamlit=True,
     height=400,
     width=400,
@@ -44,16 +55,10 @@ if canvas_result.image_data is not None:
 
     # Convert the canvas image data to a PIL image
     image = Image.fromarray(canvas_result.image_data.astype('uint8'), 'RGBA').convert('RGB')
-    st.image(image)
-
-    # Save the image to a temporary file
-    temp_image_path = "temp_drawing.png"
-    image.save(temp_image_path)
+    #st.image(image)
 
     # Predict the character
-    model_path = "saved_model.h5"  # Update with your model path
-    predicted_char = predict_image(image, model_path)
+    predicted_char = predict_image(image)
 
     # Display the predicted character
     st.subheader(f"Predicted Character: {predicted_char}")
-