Delete app.py

app.py

@@ -1,61 +0,0 @@
-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()