Update app.py

app.py

@@ -20,23 +20,7 @@ headers = {
     'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3',
 }
 
-#################### Load the banner image ##########
-# Fetch the image from the URL
-banner_image_request = requests.get("https://jaifar.net/ADS/banner.jpg", headers=headers)
 
-# Save the downloaded content
-banner_image_path = "banner.jpg"
-with open(banner_image_path, "wb") as f:
-    f.write(banner_image_request.content)
-
-
-# Open the image
-banner_image = Image.open(banner_image_path)
-
-# Display the image using streamlit
-st.image(banner_image, caption='', use_column_width=True)
-
-################ end loading banner image ##################
 
 def get_author_display_name(predicted_author, ridge_prediction, extra_trees_prediction):
     author_map = {
@@ -94,7 +78,7 @@ if not os.path.exists('my_authorship_model'):
         st.write(f"Failed to download or extract the model: {e}")
         exit(1)
 else:
-     st.write("Version: 1.0")
+     st.write("AI Text Detection")
 
 
 # Download the required files
@@ -380,28 +364,19 @@ if press_me_button:
  
 
 # Using expander to make FAQ sections
-st.subheader("Frequently Asked Questions (FAQ)")
+st.subheader("More about AI Text Detector Project :")
 
 # Small Description
 with st.expander("What is this project about?"):
     st.write("""
-    This project is part of an MSc in Data Analytics at the University of Portsmouth.
-    Developed by Jaifar Al Shizawi, it aims to identify whether a text is written by 
-    a human or a specific Large Language Model (LLM) like ChatGPT-3, ChatGPT-4, Google Bard, or HuggingChat.
-    For inquiries, contact [up2152209@myport.ac.uk](mailto:up2152209@myport.ac.uk).
-    Supervised by Dr. Mohamed Bader.
-    """)
+    This AI Text Detector tells whether a text is written by a Human or a specific Large Language Model (LLM) like ChatGPT-3, ChatGPT-4, Google Bard, or HuggingChat.
+    Ridge, Extra trees and CNN are the machine learning algorithms have been used to create this AI Text Detector.
     
-# 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.
     """)
+    
 
 # System Details
-with st.expander("How does the system work?"):
+with st.expander("How does the AI Text Detector work?"):
     st.write("""
     The system is trained using deep learning model on a dataset of 140,546 paragraphs, varying in length from 10 to 1090 words.
     It achieves an accuracy of 0.9964 with a validation loss of 0.094.
@@ -422,15 +397,5 @@ with st.expander("How does the system work?"):
     # Display the image using streamlit
     st.image(accuracy_image, caption='Best Accuracy', use_column_width=True)
 
-# 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.")
-
-# Use-case Limitation
-with st.expander("Can I use this as evidence?"):
-    st.write("""
-    No, this system is a Proof of Concept (POC) and should not be used as evidence against students or similar entities.
-    """)
-