Update app.py

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 from cear_model import CEARModel
 import pandas as pd
+import numpy as np # Used for consistency, though cear_model uses it
 
 # Initialize Model
 cear_analyzer = CEARModel()
@@ -18,7 +19,7 @@ DEFAULT_DATA = [
 
 def interpret_scores(c_score, a_risk, d_index):
     """
-    Converts raw numbers into human-readable context.
+    Converts raw numbers into human-readable context with thresholds.
     """
     # --- 1. Interpret Algorithmic Risk (A-Risk) ---
     # Thresholds: <150 (Low), 150-500 (Medium), >500 (High)
@@ -91,14 +92,15 @@ def analyze_user_data(input_table, variety_rating, fomo_rating):
     
     ---
     ### 🗣️ Self-Reported Context
-    * **Variety:** {variety_rating}/5
-    * **FOMO:** {fomo_rating}/5
+    * **Perceived Variety:** {variety_rating}/5
+    * **FOMO Intensity:** {fomo_rating}/5
     """
 
     return summary, pd.DataFrame(raw_scores['Per_Platform_Efficiency'])
 
-# --- Interface Definition ---
-with gr.Blocks(title="CEAR Model", theme=gr.themes.Soft()) as demo:
+# --- Interface Definition (FIXED: theme removed) ---
+# Removed: theme=gr.themes.Soft()
+with gr.Blocks(title="CEAR Model") as demo: 
     gr.Markdown("# 🧭 Cultural Exposure & Algorithmic Risk (CEAR) Model")
     gr.Markdown("""
     **Instructions:**
@@ -136,4 +138,4 @@ with gr.Blocks(title="CEAR Model", theme=gr.themes.Soft()) as demo:
     )
 
     if __name__ == "__main__":
-        iface.launch(server_name="0.0.0.0", server_port=7860)
+        demo.launch(server_name="0.0.0.0", server_port=7860)