Update app.py

app.py

@@ -3,14 +3,13 @@ from streamlit_drawable_canvas import st_canvas
 from keras.models import load_model
 import numpy as np
 import cv2
-import matplotlib.pyplot as plt
 
+# Page setup
 st.set_page_config(page_title="Digit Recognizer", layout="centered")
-
 st.markdown("<h1 style='text-align: center;'>🧠 Handwritten Digit Recognizer</h1>", unsafe_allow_html=True)
 st.markdown("---")
 
-# Sidebar
+# Sidebar settings
 st.sidebar.header("🛠 Drawing Settings")
 drawing_mode = st.sidebar.selectbox("Drawing tool:", ("freedraw", "line", "rect", "circle", "transform"))
 stroke_width = st.sidebar.slider("Stroke width: ", 1, 25, 10)
@@ -26,11 +25,11 @@ def load_mnist_model():
 
 model = load_mnist_model()
 
-# Canvas and Prediction
+# Layout columns
 col1, col2 = st.columns(2)
 
 with col1:
-    st.subheader("🖌️ Draw a Digit Below")
+    st.subheader("🖌️ Draw a Digit")
     canvas_result = st_canvas(
         fill_color="rgba(255, 165, 0, 0.3)",
         stroke_width=stroke_width,
@@ -45,21 +44,21 @@ with col1:
 
 with col2:
     if canvas_result.image_data is not None:
-        st.image(canvas_result.image_data, caption="🖼️ Your Drawing")
+        st.image(canvas_result.image_data, caption="Your Drawing")
 
         if st.button("Predict"):
+            # Preprocess image
             img = cv2.cvtColor(canvas_result.image_data.astype("uint8"), cv2.COLOR_RGBA2GRAY)
-            img = 255 - img  # Invert colors
+            img = 255 - img  # Invert
             img_resized = cv2.resize(img, (28, 28))
             img_normalized = img_resized / 255.0
             img_reshaped = img_normalized.reshape((1, 28, 28))
 
+            # Prediction
             prediction = model.predict(img_reshaped)
-            predicted_digit = np.argmax(prediction)
+            predicted_digit = int(np.argmax(prediction))
 
             st.success(f"✅ Predicted Digit: **{predicted_digit}**")
 
-            # Show probabilities
             st.subheader("📊 Confidence Scores")
             st.bar_chart(prediction[0])
-