Update app.py

app.py

@@ -1,86 +1,10 @@
-# import streamlit as st
-# import cv2
-# from streamlit_drawable_canvas import st_canvas
-# from keras.models import load_model
-# import numpy as np
-
-# # Sidebar controls
-# st.sidebar.title("Canvas Settings")
-# drawing_mode = st.sidebar.selectbox("Drawing tool:", ("freedraw", "line", "rect", "circle", "transform"))
-# stroke_width = st.sidebar.slider("Stroke width: ", 1, 25, 10)
-# stroke_color = st.sidebar.color_picker("Stroke color hex: ", "#000000")  # black
-# bg_color = st.sidebar.color_picker("Background color hex: ", "#FFFFFF")  # white
-# bg_image = st.sidebar.file_uploader("Background image:", type=["png", "jpg"])
-# realtime_update = st.sidebar.checkbox("Update in realtime", True)
-
-# # Load model with caching
-# @st.cache_resource
-# def load_mnist_model():
-#     return load_model("mnist_model.keras")
-
-# model = load_mnist_model()
-
-# st.title("🖌️ Mindist: Draw a Number, Predict Instantly")
-
-# # Create a two-column layout
-# col1, col2 = st.columns([1, 1])
-
-# with col1:
-#     st.subheader("Draw Here 👇")
-#     canvas_result = st_canvas(
-#         fill_color="rgba(255, 165, 0, 0.3)",
-#         stroke_width=stroke_width,
-#         stroke_color=stroke_color,
-#         background_color=bg_color,
-#         update_streamlit=realtime_update,
-#         height=280,
-#         width=280,
-#         drawing_mode=drawing_mode,
-#         key="canvas",
-#     )
-
-# with col2:
-#     if canvas_result.image_data is not None:
-#         st.subheader("Original Drawing")
-#         st.image(canvas_result.image_data, use_column_width=True)
-
-# # Below the two columns: Show preprocessing and prediction
-# if canvas_result.image_data is not None:
-#     st.markdown("---")
-#     st.subheader("Preprocessed Image & Prediction")
-    
-#     img = cv2.cvtColor(canvas_result.image_data.astype("uint8"), cv2.COLOR_RGBA2GRAY)
-#     img = 255 - img  # Invert colors
-#     img_resized = cv2.resize(img, (28, 28))
-#     img_normalized = img_resized / 255.0
-#     final_img = img_normalized.reshape(1, 28, 28, 1)
-    
-#     col3, col4 = st.columns([1, 1])
-#     with col3:
-#         st.image(img_resized, caption="28x28 Preprocessed", clamp=True, channels="GRAY")
-#     with col4:
-#         prediction = model.predict(final_img)
-#         predicted_digit = np.argmax(prediction)
-#         st.markdown(f"### 🧠 Predicted Digit: **{predicted_digit}**")
 import streamlit as st
 import cv2
-import numpy as np
-from keras.models import load_model
 from streamlit_drawable_canvas import st_canvas
+from keras.models import load_model
+import numpy as np
 
-# === Load models ===
-@st.cache_resource
-def load_single_digit_model():
-    return load_model("mnist_model.keras")
-
-@st.cache_resource
-def load_multi_digit_model():
-    return load_model("best_model.keras")  # multi-digit model
-
-single_digit_model = load_single_digit_model()
-multi_digit_model = load_multi_digit_model()
-
-# === Sidebar controls ===
+# Sidebar controls
 st.sidebar.title("Canvas Settings")
 drawing_mode = st.sidebar.selectbox("Drawing tool:", ("freedraw", "line", "rect", "circle", "transform"))
 stroke_width = st.sidebar.slider("Stroke width: ", 1, 25, 10)
@@ -89,13 +13,18 @@ bg_color = st.sidebar.color_picker("Background color hex: ", "#FFFFFF")  # white
 bg_image = st.sidebar.file_uploader("Background image:", type=["png", "jpg"])
 realtime_update = st.sidebar.checkbox("Update in realtime", True)
 
-# === Title ===
-st.title("🖌️ Multi-Digit and Single-Digit Drawing: Predict Instantly")
+# Load model with caching
+@st.cache_resource
+def load_mnist_model():
+    return load_model("mnist_model.keras")
+
+model = load_mnist_model()
 
-# === Create a two-column layout ===
+st.title("🖌️ Mindist: Draw a Number, Predict Instantly")
+
+# Create a two-column layout
 col1, col2 = st.columns([1, 1])
 
-# === Canvas for drawing ===
 with col1:
     st.subheader("Draw Here 👇")
     canvas_result = st_canvas(
@@ -110,57 +39,26 @@ with col1:
         key="canvas",
     )
 
-# === Display original drawing ===
 with col2:
     if canvas_result.image_data is not None:
         st.subheader("Original Drawing")
         st.image(canvas_result.image_data, use_column_width=True)
 
-# === Image preprocessing and prediction ===
-# === Sidebar reset ===
-if st.sidebar.button("🔄 Reset"):
-    st.session_state.draw_count = 0
-    st.session_state.last_image = None
-    st.experimental_rerun()
-
-# === Initialize draw counter ===
-if "draw_count" not in st.session_state:
-    st.session_state.draw_count = 0
-    st.session_state.last_image = None
-
-# === Preprocess and predict ===
+# Below the two columns: Show preprocessing and prediction
 if canvas_result.image_data is not None:
-    current_image = canvas_result.image_data
-
-    # Check if drawing has changed
-    if st.session_state.last_image is None or not np.array_equal(current_image, st.session_state.last_image):
-        st.session_state.draw_count += 1
-        st.session_state.last_image = current_image
-
-    st.markdown(f"### ✏️ Draw Count: {st.session_state.draw_count}")
+    st.markdown("---")
     st.subheader("Preprocessed Image & Prediction")
-
-    # Convert and preprocess
-    img = cv2.cvtColor(current_image.astype("uint8"), cv2.COLOR_RGBA2GRAY)
+    
+    img = cv2.cvtColor(canvas_result.image_data.astype("uint8"), cv2.COLOR_RGBA2GRAY)
     img = 255 - img  # Invert colors
-
-    if st.session_state.draw_count == 1:
-        # Single digit
-        img_resized = cv2.resize(img, (28, 28))
-        img_normalized = img_resized / 255.0
-        final_img = img_normalized.reshape(1, 28, 28, 1)
-        model_to_use = single_digit_model
-        preds = model_to_use.predict(final_img)
-        predicted_str = str(np.argmax(preds))
-    else:
-        # Multi digit
-        img_resized = cv2.resize(img, (100, 28))
-        img_normalized = img_resized / 255.0
-        final_img = img_normalized.reshape(1, 28, 100, 1)
-        model_to_use = multi_digit_model
-        preds = model_to_use.predict(final_img)
-        predicted_digits = [np.argmax(p[0]) for p in preds]
-        predicted_str = ''.join([str(d) for d in predicted_digits])
-
-    # Output
-    st.markdown(f"### 🧠 Predicted Number: **{predicted_str}**")
+    img_resized = cv2.resize(img, (28, 28))
+    img_normalized = img_resized / 255.0
+    final_img = img_normalized.reshape(1, 28, 28, 1)
+    
+    col3, col4 = st.columns([1, 1])
+    with col3:
+        st.image(img_resized, caption="28x28 Preprocessed", clamp=True, channels="GRAY")
+    with col4:
+        prediction = model.predict(final_img)
+        predicted_digit = np.argmax(prediction)
+        st.markdown(f"### 🧠 Predicted Digit: **{predicted_digit}**")