Update app.py

app.py

@@ -1,17 +1,139 @@
 import streamlit as st
-from my_pages import landing, txt_pages, conclusion, bibliography
-from my_pages import information_loss, rashomon_effect, rashomon_developer, developer_decisions, ica, multiverse
-
-# --- Configure the app ---
-# st.set_page_config(page_title="Multiplicity Interactive Demo", layout="centered")
-st.set_page_config(page_title="Multiplicity Interactive Demo")
-
-# --- Initialize session state ---
-if "page" not in st.session_state:
-    st.session_state.page = "landing"
-
-if "txt_" in st.session_state.page:
-    txt_pages.render()
-else:
-    page_obj = globals().get(st.session_state.page)
-    page_obj.render()
+
+st.set_page_config(layout="wide")
+
+st.title("AI Model Auditing Strategy Simulator")
+
+st.markdown(
+"""
+Select actions taken by the **Model Owner** and the **Auditor**.  
+The dashboard will update to estimate:
+
+- Probability the model owner can cheat the audit
+- Cost of auditing
+- Level of openness required
+"""
+)
+
+# -------------------------
+# Strategy Definitions
+# -------------------------
+
+model_owner_actions = {
+    "Fine-tuning / overfitting on auditing test": {
+        "cheating": 25,
+        "cost": 0,
+        "openness": -5
+    },
+    "Fine-tuning on auditing-like data distribution": {
+        "cheating": 20,
+        "cost": 0,
+        "openness": -3
+    },
+    "Training a special audit-behavior mode": {
+        "cheating": 30,
+        "cost": 5,
+        "openness": -10
+    },
+    "Withholding training details": {
+        "cheating": 15,
+        "cost": 0,
+        "openness": -15
+    }
+}
+
+auditor_actions = {
+    "Audit on one dataset": {
+        "cheating": 20,
+        "cost": 10,
+        "openness": 5
+    },
+    "Audit on multiple datasets": {
+        "cheating": -15,
+        "cost": 25,
+        "openness": 10
+    },
+    "Request internal model access": {
+        "cheating": -25,
+        "cost": 30,
+        "openness": 30
+    },
+    "Request training data documentation": {
+        "cheating": -10,
+        "cost": 5,
+        "openness": 20
+    }
+}
+
+# -------------------------
+# Layout
+# -------------------------
+
+left, right = st.columns(2)
+
+with left:
+    st.header("Model Owner Actions")
+
+    selected_owner = st.multiselect(
+        "Choose actions:",
+        list(model_owner_actions.keys())
+    )
+
+with right:
+    st.header("Auditor Actions")
+
+    selected_auditor = st.multiselect(
+        "Choose actions:",
+        list(auditor_actions.keys())
+    )
+
+# -------------------------
+# Score Calculation
+# -------------------------
+
+cheating_score = 50
+audit_cost = 0
+openness_required = 0
+
+for action in selected_owner:
+    cheating_score += model_owner_actions[action]["cheating"]
+    audit_cost += model_owner_actions[action]["cost"]
+    openness_required += model_owner_actions[action]["openness"]
+
+for action in selected_auditor:
+    cheating_score += auditor_actions[action]["cheating"]
+    audit_cost += auditor_actions[action]["cost"]
+    openness_required += auditor_actions[action]["openness"]
+
+cheating_score = max(0, min(100, cheating_score))
+openness_required = max(0, openness_required)
+
+# -------------------------
+# Dashboard
+# -------------------------
+
+st.divider()
+st.subheader("Audit Outcome Dashboard")
+
+col1, col2, col3 = st.columns(3)
+
+col1.metric(
+    "Chance of Cheating",
+    f"{cheating_score}%"
+)
+
+col2.metric(
+    "Audit Cost",
+    audit_cost
+)
+
+col3.metric(
+    "Openness Required",
+    openness_required
+)
+
+# -------------------------
+# Optional Visualization
+# -------------------------
+
+st.progress(cheating_score / 100)