Update app.py

app.py

@@ -1,5 +1,4 @@
 import streamlit as st
-#from button_click import find_order_id
 import tensorflow as tf
 from button_click_alt import find_order_id
 
@@ -8,22 +7,29 @@ def main():
     st.title('OCR + Font type demo')
     st.write('intro')
 
-    with st.sidebar:
-        st.write('## Input')
-        uploaded_file = st.file_uploader('Upload the image file (PNG or JPG)', type=['png', 'jpg'], help='help')
-        input_file = st.file_uploader('Upload the input file (TXT)', type=['txt'], help='help')
-        ocre = st.selectbox('OCR Engine', ['Hive', 'Tesseract'])
-        img_processing = st.selectbox('Image preprocessing', ['Gray Scaling', 'Thresholding, Denoising, Binarization, Skew Correction', 'Adaptive Thresholding, Morphological Operations, CCA'])
-        cnn_model = st.selectbox('Neural Network Model', ['CNN-MaxPool-Dense-Dropout', 'BatchNorm-CNN-MaxPool-Dense-Dropout'])
-    
-    if st.button('Find Order ID') and uploaded_file and input_file:
-        st.write('## Output')
-        model = tf.keras.models.load_model('model.h5')
-        result = find_order_id(uploaded_file, input_file, model, ocre)
-        if result['status'] == 'success':
-            st.success(result['message'])
-        elif result['status'] == 'warning':
-            st.warning(result['message'])
+    tabs = ['Find Order ID', 'About']
+    selected_tab = st.radio('Select Tab', tabs)
+
+    if selected_tab == 'Find Order ID':
+        with st.expander('Input'):
+            uploaded_file = st.file_uploader('Upload the image file (PNG or JPG)', type=['png', 'jpg'], help='help')
+            input_file = st.file_uploader('Upload the input file (TXT)', type=['txt'], help='help')
+            ocre = st.selectbox('OCR Engine', ['Hive', 'Tesseract'])
+            img_processing = st.selectbox('Image preprocessing', ['Gray Scaling', 'Thresholding, Denoising, Binarization, Skew Correction', 'Adaptive Thresholding, Morphological Operations, CCA'])
+            cnn_model = st.selectbox('Neural Network Model', ['CNN-MaxPool-Dense-Dropout', 'BatchNorm-CNN-MaxPool-Dense-Dropout'])
+
+        if st.button('Find Order ID') and uploaded_file and input_file:
+            with st.expander('Output'):
+                model = tf.keras.models.load_model('model.h5')
+                result = find_order_id(uploaded_file, input_file, model, ocre)
+                if result['status'] == 'success':
+                    st.success(result['message'])
+                elif result['status'] == 'warning':
+                    st.warning(result['message'])
+
+    elif selected_tab == 'About':
+        st.subheader('About this app')
+        st.write('This app is designed to demonstrate OCR and font type identification using deep learning models. It allows you to upload an image file and a text file containing the font type, and it will attempt to extract the order ID from the image using OCR and deep learning models.')
 
 if __name__ == '__main__':
     main()