app.py

import streamlit as st
import numpy as np
from PIL import Image
from tensorflow.keras.models import load_model

# Load trained model (placed in same directory as app.py)
@st.cache_resource  # cache so model loads only once
def load_cnn_model():
    return load_model("mnist_cnn.h5")

model = load_cnn_model()

st.title("🖊️ Handwritten Digit Recognition")
st.write("Upload an image of a digit (0–9) and the model will predict it.")

uploaded_file = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"])

if uploaded_file is not None:
    # Convert to grayscale and resize
    img = Image.open(uploaded_file).convert('L')
    img = img.resize((28,28))

    # Preprocess
    img_array = np.array(img) / 255.0
    img_array = img_array.reshape(1,28,28,1)

    # Predict
    pred = model.predict(img_array)
    pred_label = np.argmax(pred)

    # Show results
    st.image(img, caption=f"Predicted Digit: {pred_label}", width=150)
    st.write("Prediction Probabilities:", pred)