Update app.py

app.py

@@ -232,19 +232,21 @@ with gr.Blocks(title="Cheminformatics Platform") as demo:
         cl_method = gr.Radio(['KMeans','DBSCAN'], label="分群方法", value="KMeans")
         nclus = gr.Slider(2, 8, 3, 1, label="KMeans分群數")
         dr_img = gr.Image(label="降維/分群視覺化")
+        dr_desc = gr.Markdown(label="圖像說明")      # <======= 新增這一行
         rep_imgs = gr.Image(label="群代表分子圖(每群1個)")
-        
+    
         def dimred_and_cluster(file, fp, dr, cl, nclu):
             df = load_table(file)
             df = calc_features(df, [fp], [], None)
             pc = apply_dim_red(df, fp, dr)
             labels = KMeans(n_clusters=int(nclu), random_state=42).fit_predict(pc) if cl == 'KMeans' else DBSCAN(eps=3, min_samples=2).fit_predict(pc)
             plotimg = plot_scatter(pc, labels, f"{dr}-{cl}")
-            exp = explain_dimred(pc, labels, dr, cl)
+            # 圖像解釋
+            desc = explain_dimred(pc, labels, dr, cl)
             reps = cluster_reps(df, labels, fp)
             rep_img = batch_mol_imgs(reps)
-            return plotimg, exp, rep_img
-
+            return plotimg, desc, rep_img
+    
         gr.Button("降維+分群分析", variant="primary").click(
             dimred_and_cluster, [file4, use_fp, dr_method, cl_method, nclus], [dr_img, dr_desc, rep_imgs]
         )