updated display on interface

app.py

@@ -26,7 +26,26 @@ def make_json_safe(obj):
     else:
         return obj
 
-# insert format_deviations_as_columns()
+def format_deviations_as_columns(drivers):
+    headers = []
+    cells = []
+
+    for cid, data in drivers.items():
+        headers.append(f"Cluster {cid + 1}")
+
+        dev_lines = [
+            f"{feature}: {value:.3f}"
+            for feature, value in data["deviations"].items()
+        ]
+
+        cells.append("<br>".join(dev_lines))
+
+    table = "| " + " | ".join(headers) + " |\n"
+    table += "|" + "|".join(["---"] * len(headers)) + "|\n"
+    table += "| " + " | ".join(cells) + " |"
+
+    return table
+
 
 def final_clustering(file, top_features):
     try:
@@ -66,13 +85,14 @@ def final_clustering(file, top_features):
             feature_names
         )
         top_drivers = identify_top_drivers(original_centroids, top_features)
-        top_drivers_safe = make_json_safe(top_drivers)
+        deviations_markdown = format_deviations_as_columns(top_drivers)
+        # top_drivers_safe = make_json_safe(top_drivers)
         
         # debug
         print("✅ Best K:", best_k, type(best_k))
         print("📊 Drivers sample:", top_drivers)
         
-        return best_k, top_drivers_safe
+        return best_k, deviations_markdown
 
     except Exception as e:
         print("🔥 ERROR IN final_clustering 🔥")
@@ -89,7 +109,7 @@ with gr.Blocks(title="PERCEUL: Perception-Based Worker Profiler") as app:
         file_input_final = gr.File(label="Upload CSV")
         top_features = gr.Number(
             value=5, 
-            label="Show Top `n` Features", 
+            label="Number of Features to Display", 
             minimum=3, 
             maximum=10,
             step=1,
@@ -98,12 +118,13 @@ with gr.Blocks(title="PERCEUL: Perception-Based Worker Profiler") as app:
 
         run_btn = gr.Button("Run Final Clustering")
         best_k_out = gr.Number(label="Selected K", interactive=False, precision=0)
-        drivers_out = gr.JSON(label="Top Feature Drivers per Cluster")
+        gr.Markdown("### Cluster Characteristics")
+        deviations_out = gr.Markdown()
 
         run_btn.click(
             final_clustering,
             inputs=[file_input_final, top_features],
-            outputs=[best_k_out, drivers_out]
+            outputs=[best_k_out, deviations_out]
         )
 
 app.launch()