Update app.py

app.py

@@ -8,6 +8,7 @@ from keras.preprocessing.sequence import pad_sequences
 from sklearn.preprocessing import LabelEncoder
 import pickle
 import numpy as np
+from PIL import Image
 
 
 # Custom headers for the HTTP request
@@ -140,12 +141,21 @@ with st.expander("Aim and Objectives"):
     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.
-#     """)
+# 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.
+    """)
+    
+    # Fetch the image from the URL
+    accuracy_image_request = requests.get("https://jaifar.net/best_accuracy.png")
+    
+    # Open the image
+    accuracy_image = Image.open(accuracy_image_request.content)
+    
+    # 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?"):