Create app.py

app.py

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+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)