new app

app.py

@@ -266,8 +266,25 @@ def perform_inference(model_object, model_arrow, image, score_threshold):
 def get_image(uploaded_file):
     return Image.open(uploaded_file).convert('RGB')
 
+
 def main():
     st.set_page_config(layout="wide")
+
+    # Add your company logo banner
+    st.image("./images/banner.png", use_column_width=True)
+
+    # Sidebar content
+    st.sidebar.header("This BPMN model recognition by AI is proposed by ELCA in collaboration with EPFL.")
+    st.sidebar.subheader("Instructions:")
+    st.sidebar.text("1. Upload you image")
+    st.sidebar.text("2. Crop the image \n  (try to put the BPMN diagram \n   in the center of the image)")
+    st.sidebar.text("3. Set the score threshold \n   for prediction (default is 0.5)")
+    st.sidebar.text("4. Set the scale for the XML file \n  (default is 1.0)")
+    st.sidebar.text("5. Click on 'Launch Prediction'")
+
+    st.sidebar.subheader("You can close the sidebar")
+
+    # Set the title of the app
     st.title("BPMN model recognition demo")
     
      # Display current memory usage

requirements.txt

@@ -8,3 +8,4 @@ streamlit-cropper==0.2.2
 streamlit-drawable-canvas==0.9.3
 opencv-python
 gdown
+psutil