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app.py

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+import os
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+
+import streamlit as st
+from streamlit_drawable_canvas import st_canvas
+from keras.models import load_model
+import numpy as np
+import cv2
+from PIL import Image, ImageOps
+
+# Custom styles
+st.markdown("""
+    <style>
+    .big-font {
+        font-size:40px !important;
+        font-weight: bold;
+        color: #5A189A;
+        text-align: center;
+    }
+    .result-box {
+        background-color: #F0EBF8;
+        border-radius: 10px;
+        padding: 20px;
+        text-align: center;
+        font-size: 24px;
+        color: #3C096C;
+        font-weight: bold;
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+# App title
+st.markdown('<p class="big-font">✍️ Handwritten Digit Recognizer</p>', unsafe_allow_html=True)
+st.markdown("### 🔢 Draw or Upload a digit and get it recognized by our ML model!")
+
+# Load model
+@st.cache_resource
+def load_mnist_model():
+    return load_model("final_model.keras")
+
+model = load_mnist_model()
+
+# Preprocessing
+def preprocess(img):
+    img = ImageOps.grayscale(img)
+    img = img.resize((200, 200))
+    img = np.array(img)
+    if np.mean(img) > 127:
+        img = 255 - img
+    _, img = cv2.threshold(img, 100, 255, cv2.THRESH_BINARY)
+    coords = cv2.findNonZero(img)
+    if coords is not None:
+        x, y, w, h = cv2.boundingRect(coords)
+        digit = img[y:y+h, x:x+w]
+    else:
+        return np.zeros((1, 28, 28), dtype="float32")
+    digit = cv2.resize(digit, (20, 20), interpolation=cv2.INTER_AREA)
+    digit = np.pad(digit, ((4, 4), (4, 4)), mode="constant", constant_values=0)
+    digit = digit.astype("float32") / 255.0
+    digit = digit.reshape(1, 28, 28)
+    return digit
+
+# Sidebar settings
+st.sidebar.title("🎨 Drawing Settings")
+mode = st.sidebar.selectbox("Drawing Tool", ("freedraw", "line"))
+stroke_width = st.sidebar.slider("Stroke Width", 5, 25, 15)
+stroke_color = st.sidebar.color_picker("Stroke Color", "#000000")
+bg_color = st.sidebar.color_picker("Background Color", "#FFFFFF")
+
+# Tabs for draw vs upload
+tab1, tab2 = st.tabs(["🖌️ Draw Digit", "📤 Upload Image"])
+
+input_img = None
+
+# Tab 1: Draw
+with tab1:
+    canvas_result = st_canvas(
+        stroke_width=stroke_width,
+        stroke_color=stroke_color,
+        background_color=bg_color,
+        height=200,
+        width=200,
+        drawing_mode=mode,
+        key="canvas",
+    )
+    if canvas_result.image_data is not None:
+        input_img = Image.fromarray(canvas_result.image_data.astype("uint8"))
+
+# Tab 2: Upload
+with tab2:
+    uploaded_file = st.file_uploader("Upload a digit image...", type=["jpg", "png"])
+    if uploaded_file:
+        input_img = Image.open(uploaded_file).convert("RGB")
+
+# Prediction
+if input_img:
+    st.image(input_img, caption="🔍 Input Image", width=150)
+
+    if st.button("🎯 Predict"):
+        processed = preprocess(input_img)
+        prediction = model.predict(processed, verbose=0)
+        digit = int(np.argmax(prediction))
+        confidence = float(np.max(prediction)) * 100
+
+        st.image(processed.reshape(28, 28), width=150, caption="🧪 Preprocessed Image")
+        st.markdown(f"""
+            <div class="result-box">
+            🧠 Predicted Digit: <strong>{digit}</strong><br/>
+            🔎 Confidence: <strong>{confidence:.2f}%</strong>
+            </div>
+        """, unsafe_allow_html=True)

requirements.txt

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+opencv-python
+keras
+streamlit
+streamlit-drawable-canvas
+tensorflow
+numpy
+Pillow