Update app.py

app.py

@@ -1,68 +1,4 @@
-# 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 streamlit as st
 import cv2
 from streamlit_drawable_canvas import st_canvas
 from keras.models import load_model
@@ -112,30 +48,17 @@ with col2:
 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
-    _, thresh_img = cv2.threshold(img, 50, 255, cv2.THRESH_BINARY)
-
-    # Find contours of digits
-    contours, _ = cv2.findContours(thresh_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-    contours = sorted(contours, key=lambda ctr: cv2.boundingRect(ctr)[0])  # Sort left-to-right
-
-    predictions = []
-
+    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, caption="Thresholded Image", clamp=True, channels="GRAY")
-
+        st.image(img_resized, caption="28x28 Preprocessed", clamp=True, channels="GRAY")
     with col4:
-        for cnt in contours:
-            x, y, w, h = cv2.boundingRect(cnt)
-            if w > 5 and h > 5:  # Filter out noise/small contours
-                digit_roi = thresh_img[y:y+h, x:x+w]
-                digit_resized = cv2.resize(digit_roi, (28, 28))
-                digit_normalized = digit_resized / 255.0
-                input_img = digit_normalized.reshape(1, 28, 28, 1)
-                pred = np.argmax(model.predict(input_img))
-                predictions.append(str(pred))
-
-        st.markdown(f"### 🧠 Predicted Digits: **{''.join(predictions) if predictions else 'No digits found'}**")
+        prediction = model.predict(final_img)
+        predicted_digit = np.argmax(prediction)
+        st.markdown(f"### 🧠 Predicted Digit: **{predicted_digit}**")