updts

app.py

@@ -52,6 +52,32 @@ def calculate_rewards(subscription, num_generations, author_share, ro_share, num
 def main():
     st.title("Reward Simulator")
     
+    # Add introduction text and expandable details
+    st.markdown("""
+        This simulator helps estimate potential rewards for authors and rights owners when their images are used 
+        to train AI image generation models.
+    """)
+    
+    with st.expander("Learn more about how it works"):
+        st.markdown("""
+            ### How it works
+            1. Select or upload an image that represents AI-generated content
+            2. The system finds similar images that might have influenced the generation in a database of 10M images (OpenImages)
+            3. Based on your parameters, it calculates potential rewards for:
+                - Original image authors
+                - Rights owners (e.g., stock photo companies, galleries)
+            
+            ### Key assumptions
+            - Attribution scores indicate the level of influence of training images
+            - Rewards are distributed based on subscription revenue
+            - Calculations use simplified models and are for demonstration purposes
+            
+            ### Use cases
+            - Explore fair compensation models for AI training data
+            - Simulate different revenue sharing scenarios
+            - Understand the relationship between model training and attribution
+        """)
+    
     # Load model and index
     model = load_model()
     index = load_index("data/openimages_index.bin")
@@ -153,7 +179,7 @@ def main():
         st.session_state.query_image = Image.open(uploaded_file).convert('RGB')
 
     # Display selected image
-    st.subheader("Query Image:")
+    st.subheader("You query image:")
     st.image(st.session_state.query_image, caption="Selected Image", width=300)
     
     # Get features and search