Update app.py

app.py

@@ -57,8 +57,8 @@ if not os.path.exists('my_authorship_model'):
     except Exception as e:
         st.write(f"Failed to download or extract the model: {e}")
         exit(1)
-else:
-    st.write("System Ready !!")
+# else:
+#     st.write("System Ready !!")
 
 
 # Download the required files
@@ -120,10 +120,7 @@ if press_me_button:
 
 import streamlit as st
 
-st.title("Smart Authorship Detection System of AI-Generated Text Models")
 
-# Slogan under the title
-st.subheader("Uncover the Invisible Ink: Who's the Author?")
 
 # Using expander to make FAQ sections
 st.subheader("Frequently Asked Questions (FAQ)")
@@ -136,14 +133,20 @@ with st.expander("What is this project about?"):
     For inquiries, contact [[email protected]](mailto:[email protected]).
     Supervised by Dr. Mohamed Bader.
     """)
-
-# System Details
-with st.expander("How does the system work?"):
+    
+# Aim and Objectives
+with st.expander("Aim and Objectives"):
     st.write("""
-    The system is trained using a CNN model on a dataset of 140,546 paragraphs, varying in length from 10 to 500 words.
-    It achieves an accuracy of 0.9964 with a validation loss of 0.094.
+    The project aims to help staff at the University of Portsmouth distinguish between student-written artifacts and those generated by LLMs. It focuses on text feature extraction, model testing, and implementing a user-friendly dashboard among other objectives.
     """)
 
+# # System Details
+# with st.expander("How does the system work?"):
+#     st.write("""
+#     The system is trained using a CNN model on a dataset of 140,546 paragraphs, varying in length from 10 to 500 words.
+#     It achieves an accuracy of 0.9964 with a validation loss of 0.094.
+#     """)
+
 # Data Storage Information
 with st.expander("Does the system store my data?"):
     st.write("No, the system does not collect or store any user input data.")
@@ -154,21 +157,6 @@ with st.expander("Can I use this as evidence?"):
     No, this system is a Proof of Concept (POC) and should not be used as evidence against students or similar entities.
     """)
 
-# Background and Context
-with st.expander("Background and Context"):
-    st.write("""
-    The proliferation of AI and Large Language Models (LLMs) like ChatGPT and Google Bard has raised questions about authorship. This project aims to analyze and predict the unique features of these models compared to human-written text.
-    """)
 
-# Problem Statement
-with st.expander("Problem Statement"):
-    st.write("""
-    Most AI authorship detection systems fail to identify the specific LLM behind a text. This research aims to fill this gap, offering detailed analysis on various LLMs and human writing.
-    """)
 
-# Aim and Objectives
-with st.expander("Aim and Objectives"):
-    st.write("""
-    The project aims to help staff at the University of Portsmouth distinguish between student-written artifacts and those generated by LLMs. It focuses on text feature extraction, model testing, and implementing a user-friendly dashboard among other objectives.
-    """)