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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+mnist_cnn_model_32x32_balanced_dataset.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,96 @@
+import pandas as pd
+import numpy as np
+import plotly.express as px
+
+import streamlit as st
+from streamlit_drawable_canvas import st_canvas
+
+import cv2
+
+from keras.models import load_model
+
+# --- App Configuration ---
+st.set_page_config(page_title="Handwritten Digit Recognizer", layout="centered")
+st.markdown("""
+    <style>
+    .stButton>button {background-color: #4b7bec; color: white; border-radius: 8px; padding: 10px;}
+    .stButton>button:hover {background-color: #3867d6;}
+    </style>
+""", unsafe_allow_html=True)
+
+# --- Load Model ---
+@st.cache_resource
+def load_digit_model():
+    return load_model("mnist_cnn_model_32x32_balanced_dataset.keras")
+
+# --- App Title and Instructions ---
+st.title("✍️ Handwritten Digit Recognizer")
+st.markdown("Draw a digit (0–9) in the box below and click **Predict** to see the result!")
+
+with st.expander("ℹ️ How to Use", expanded=False):
+    st.markdown("""
+    - Draw a digit clearly in the center of the canvas.
+    - Use a thick stroke.
+    - Click **Predict** to see the result.
+    - Use **Clear Canvas** to start over.
+    """)
+
+# --- Drawing Canvas ---
+st.subheader("Draw Your Digit")
+canvas_result = st_canvas(
+    fill_color="rgba(0, 0, 0, 0)",
+    stroke_width=20,
+    stroke_color="#000000",
+    background_color="#FFFFFF",
+    update_streamlit=True,
+    height=280,
+    width=280,
+    drawing_mode="freedraw",
+    key="canvas",
+    display_toolbar=True,
+)
+
+# --- Predict Button ---
+predict_clicked = st.button("🔍 Predict", use_container_width=True, key="predict_button")
+
+if predict_clicked and canvas_result.image_data is not None:
+    img = cv2.cvtColor(canvas_result.image_data.astype(np.uint8), cv2.COLOR_RGBA2GRAY)
+    
+    if np.all(img == 255):
+        st.warning("⚠️ Please draw something before predicting!")
+    else:
+        img = 255 - img  # Inverting the colors to mimic the dataset
+        img_resized = cv2.resize(img, (32, 32), interpolation=cv2.INTER_AREA)
+        img_normalized = img_resized.astype("float32") / 255.0
+        input_img = img_normalized.reshape(1, 32, 32, 1)
+
+        model = load_digit_model()
+        pred_probs = model.predict(input_img)
+        pred_class = np.argmax(pred_probs)
+        confidence = np.max(pred_probs)
+
+        st.subheader("Prediction Results")
+        col_img, col_result = st.columns([1, 2])
+
+        with col_img:
+            st.image(img_resized, caption="Processed Drawing", width=100, clamp=True)
+
+        with col_result:
+            st.success(f"🧠 Predicted Digit: **{pred_class}**")
+            st.info(f"🔍 Confidence: **{confidence * 100:.2f}%**")
+
+        # --- Plot probabilities ---
+        probs_df = pd.DataFrame({
+            "Digit": list(range(10)),
+            "Probability": pred_probs[0] * 100
+        })
+        fig = px.bar(probs_df, x="Digit", y="Probability", 
+                     title="Prediction Probabilities",
+                     color="Probability",
+                     color_continuous_scale="Blues",
+                     height=300)
+        fig.update_layout(xaxis_title="Digit", yaxis_title="Probability (%)", xaxis=dict(tickmode="linear"))
+        st.plotly_chart(fig, use_container_width=True)
+
+elif predict_clicked:
+    st.warning("⚠️ Please draw something before predicting!")

mnist_cnn_model_32x32_balanced_dataset.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c9127a145f3cb5a18fe28d2eac6711e0fa9e21ebea84ec032eb9b655d748b505
+size 3686272