Upload app.py

app.py

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+import streamlit as st
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+import cv2
+from PIL import Image, ImageOps
+
+# Load the trained model
+model = load_model('digit_recognizer_model.h5')
+
+# Streamlit app title
+st.title("Handwritten Digit Recognizer")
+
+# Instructions
+st.write("Draw a digit below and click 'Predict' to see the model's prediction.")
+
+# Create a canvas component
+from streamlit_drawable_canvas import st_canvas
+
+# Set up the canvas
+canvas_result = st_canvas(
+    fill_color="black",  # Drawing background color
+    stroke_width=10,
+    stroke_color="white",
+    background_color="black",
+    height=280,
+    width=280,
+    drawing_mode="freedraw",
+    key="canvas",
+)
+
+# Predict button
+if st.button('Predict'):
+    if canvas_result.image_data is None:
+        st.write("Please draw a digit first!")
+    else:
+        # Convert the canvas image to grayscale
+        img = cv2.cvtColor(canvas_result.image_data.astype('uint8'), cv2.COLOR_BGR2GRAY)
+        
+        # Resize to 28x28 pixels, the input size for the model
+        img_resized = cv2.resize(img, (28, 28))
+        
+        # Invert the image (white background, black digit)
+        img_resized = cv2.bitwise_not(img_resized)
+        
+        # Normalize the image
+        img_resized = img_resized / 255.0
+        
+        # Reshape for the model: (1, 28, 28, 1)
+        img_resized = img_resized.reshape(1, 28, 28, 1)
+        
+        # Predict the digit
+        prediction = model.predict(img_resized)
+        predicted_digit = np.argmax(prediction)
+        
+        # Display the prediction
+        st.write(f"Predicted Digit: {predicted_digit}")
+
+# Clear button
+if st.button('Clear'):
+    st.experimental_rerun()