Update app.py

app.py

@@ -229,52 +229,62 @@ if st.button("🚀 Run Topic Modeling Pipeline", type="primary", use_container_w
             st.info(f"**Theme {t_id + 1}**\n\n" + "\n".join([f"🔹 {w}" for w in theme_w]))
         col_idx += 1
 
-    # --- METRICS CALCULATIONS ---
+    # --- METRICS CALCULATIONS (Math First, UI Second) ---
     div_val, npmi_val, sil_val = 0.0, 0.0, 0.0
+    div_status, npmi_status, sil_status = "Skipped", "Skipped", "Skipped"
     
     if len(eval_metrics) > 0:
-        st.markdown("### 📊 Key Performance Indicators (KPI)")
-        
+        # 1. DO ALL THE MATH INVISIBLY FIRST
         with st.spinner("Calculating mathematical metrics... (NPMI requires building a dictionary and takes a moment)"):
             
-            for metric in eval_metrics:
-                if "Diversity" in metric:
-                    if len(all_words) > 0:
-                        u_words = set([w for t in all_words for w in t])
-                        t_words = sum([len(t) for t in all_words])
-                        div_val = len(u_words) / t_words if t_words > 0 else 0
-                        st.metric("Topic Diversity (Target: 1.0)", f"{div_val:.2f}")
-                    else:
-                        st.metric("Topic Diversity", "Skipped")
-                        
-                elif "NPMI" in metric:
-                    try:
-                        tokenized = [vectorizer_model.build_analyzer()(s) for s in sentences]
-                        dictionary = corpora.Dictionary(tokenized)
-                        cm = CoherenceModel(topics=all_words, texts=tokenized, dictionary=dictionary, coherence='c_npmi')
-                        temp_npmi = cm.get_coherence()
-                        if np.isnan(temp_npmi):
-                            st.metric("NPMI Coherence", "N/A (Too few words)")
-                        else:
-                            npmi_val = float(temp_npmi)
-                            st.metric("NPMI Coherence (Target: >0.1)", f"{npmi_val:.2f}")
-                    except Exception:
-                        st.metric("NPMI Coherence", "Skipped (Data too small)")
-                        
-                elif "Silhouette" in metric:
-                    valid_idx = [i for i, t in enumerate(topics) if t != -1]
-                    unique_topics = set([topics[i] for i in valid_idx])
-                    
-                    if 1 < len(unique_topics) < len(valid_idx):
-                        sil_val = float(silhouette_score(
-                            np.array([embeddings[i] for i in valid_idx]), 
-                            [topics[i] for i in valid_idx], 
-                            metric='cosine'
-                        ))
-                        st.metric("Silhouette Score (Target: >0.0)", f"{sil_val:.2f}")
+            if "Topic Diversity" in eval_metrics:
+                if len(all_words) > 0:
+                    u_words = set([w for t in all_words for w in t])
+                    t_words = sum([len(t) for t in all_words])
+                    div_val = float(len(u_words) / t_words) if t_words > 0 else 0.0
+                    div_status = f"{div_val:.2f}"
+
+            if "NPMI Coherence" in eval_metrics:
+                try:
+                    tokenized = [vectorizer_model.build_analyzer()(s) for s in sentences]
+                    dictionary = corpora.Dictionary(tokenized)
+                    cm = CoherenceModel(topics=all_words, texts=tokenized, dictionary=dictionary, coherence='c_npmi')
+                    temp_npmi = cm.get_coherence()
+                    if np.isnan(temp_npmi):
+                        npmi_status = "N/A (Too few words)"
                     else:
-                        st.metric("Silhouette Score", "Skipped (Themes need ≥2 sentences each)")
+                        npmi_val = float(temp_npmi)
+                        npmi_status = f"{npmi_val:.2f}"
+                except Exception:
+                    npmi_status = "Skipped (Data too small)"
+
+            if "Silhouette Score" in eval_metrics:
+                valid_idx = [i for i, t in enumerate(topics) if t != -1]
+                unique_topics = set([topics[i] for i in valid_idx])
+                if 1 < len(unique_topics) < len(valid_idx):
+                    sil_val = float(silhouette_score(
+                        np.array([embeddings[i] for i in valid_idx]), 
+                        [topics[i] for i in valid_idx], 
+                        metric='cosine'
+                    ))
+                    sil_status = f"{sil_val:.2f}"
+                else:
+                    sil_status = "Skipped (Themes need ≥2 sentences)"
+
+        # 2. DRAW THE UI INSTANTLY (No more jumping!)
+        st.markdown("### 📊 Key Performance Indicators (KPI)")
+        kpi_cols = st.columns(len(eval_metrics))
+        
+        for idx, metric in enumerate(eval_metrics):
+            with kpi_cols[idx]:
+                if metric == "Topic Diversity":
+                    st.metric("Topic Diversity (Target: 1.0)", div_status)
+                elif metric == "NPMI Coherence":
+                    st.metric("NPMI Coherence (Target: >0.1)", npmi_status)
+                elif metric == "Silhouette Score":
+                    st.metric("Silhouette Score (Target: >0.0)", sil_status)
 
+                    
     # ==========================================
     # 5. XAI VISUALIZATION GRAPH
     # ==========================================