Update app.py

app.py

@@ -17,7 +17,7 @@ hf_logging.set_verbosity_error()
 
 MODEL_NAME = "bert-base-uncased"
 DEVICE     = "cpu"
-SAVE_DIR   = "저장저장1"
+SAVE_DIR   = "저장저장1" # This folder name is from your setup
 LAYER_ID   = 4
 SEED       = 0
 CLF_NAME   = "linear"
@@ -133,7 +133,6 @@ def analyze_sentence_for_gradio(sentence_text, top_k_value):
         error_html = f"<p style='color:red;'>Initialization Error: {html.escape(MODEL_LOADING_ERROR_MESSAGE)}</p>"
         empty_df = pd.DataFrame(columns=['token', 'score'])
         empty_fig = create_empty_plotly_figure("Model Loading Failed")
-        # gr.Label에 대한 오류 반환값 수정 (단순 딕셔너리 또는 문자열)
         error_label_output = {"Status": "Error", "Message": "Model Loading Failed. Check logs."}
         return error_html, [], "Model Loading Failed", error_label_output, [], empty_df, empty_fig
 
@@ -215,8 +214,7 @@ def analyze_sentence_for_gradio(sentence_text, top_k_value):
         predicted_class_label_str = CLASS_LABEL_MAP.get(pred_idx, f"Unknown Index ({pred_idx})")
         
         prediction_summary_text = f"Predicted Class: {predicted_class_label_str}\nProbability: {pred_prob_val:.3f}"
-        # 수정된 부분: gr.Label에 적합한 딕셔너리 형태 (클래스명: 확률값)
-        prediction_details_for_label = {predicted_class_label_str: float(f"{pred_prob_val:.3f}")} # 확률값을 float으로 전달
+        prediction_details_for_label = {predicted_class_label_str: float(f"{pred_prob_val:.3f}")}
 
         pca_fig = create_empty_plotly_figure("PCA Plot N/A\n(Not enough non-special tokens for 3D)")
         non_special_token_indices = [idx for idx, token_id in enumerate(input_ids[0,:len(actual_tokens)].tolist())
@@ -244,11 +242,10 @@ def analyze_sentence_for_gradio(sentence_text, top_k_value):
         print(f"analyze_sentence_for_gradio error: {e}\n{tb_str}")
         empty_df = pd.DataFrame(columns=['token', 'score'])
         empty_fig = create_empty_plotly_figure("Analysis Error")
-        # gr.Label에 대한 오류 반환값 수정
         error_label_output = {"Status": "Error", "Message": f"Analysis failed: {str(e)}"}
         return error_html, [], "Analysis Failed", error_label_output, [], empty_df, empty_fig
 
-# --- Gradio UI Definition (Translated and Enhanced) ---
+# --- Gradio UI Definition (Tabs removed, visualizations shown sequentially or in rows) ---
 theme = gr.themes.Monochrome(
     primary_hue=gr.themes.colors.blue, 
     secondary_hue=gr.themes.colors.sky, 
@@ -265,6 +262,7 @@ with gr.Blocks(title="AI Sentence Analyzer XAI 🚀", theme=theme, css=".gradio-
     gr.Markdown("Analyze English sentences to understand BERT model predictions through various XAI visualization techniques. "
                 "Explore token importance and their distribution in the embedding space.")
 
+    # Inputs and Summary Outputs Row
     with gr.Row(equal_height=False):
         with gr.Column(scale=1, min_width=350):
             with gr.Group():
@@ -276,32 +274,48 @@ with gr.Blocks(title="AI Sentence Analyzer XAI 🚀", theme=theme, css=".gradio-
         with gr.Column(scale=2):
             with gr.Accordion("🎯 Prediction Outcome", open=True):
                 output_prediction_summary = gr.Textbox(label="Prediction Summary", lines=2, interactive=False)
-                output_prediction_details = gr.Label(label="Prediction Details & Confidence") # 레이블 이름 변경
+                output_prediction_details = gr.Label(label="Prediction Details & Confidence")
             with gr.Accordion("⭐ Top-K Important Tokens (Table)", open=True):
                 output_top_tokens_df = gr.DataFrame(headers=["Token", "Score"], label="Most Important Tokens", 
                                                     row_count=(1,"dynamic"), col_count=(2,"fixed"), interactive=False, wrap=True)
+    gr.Markdown("---") # Separator
 
-    with gr.Tabs() as tabs:
-        with gr.TabItem("🎨 HTML Highlight (Custom)", id=0):
-            output_html_visualization = gr.HTML(label="Token Importance (Gradient x Input based)")
-        with gr.TabItem("🖍️ Highlighted Text (Gradio)", id=1):
-            output_highlighted_text = gr.HighlightedText(
-                label="Token Importance (Score: 0-1)",
-                show_legend=True,
-                combine_adjacent=False 
-            )
-        with gr.TabItem("📊 Top-K Bar Plot", id=2):
-            output_top_tokens_barplot = gr.BarPlot(
-                label="Top-K Token Importance Scores", 
-                x="token", 
-                y="score", 
-                tooltip=['token', 'score'],
-                min_width=300
-            )
-        with gr.TabItem("🌐 Token Embeddings 3D PCA (Interactive)", id=3):
-            output_pca_plot = gr.Plot(label="3D PCA of Token Embeddings (Colored by Importance Score)")
+    # Visualization Section Title
+    gr.Markdown("## 📊 Detailed Visualizations")
+
+    # HTML Highlight (Custom) - Full Width
+    with gr.Group():
+        gr.Markdown("### 🎨 HTML Highlight (Custom)")
+        output_html_visualization = gr.HTML(label="Token Importance (Gradient x Input based)")
+
+    # Highlighted Text (Gradio) - Full Width
+    with gr.Group():
+        gr.Markdown("### 🖍️ Highlighted Text (Gradio)")
+        output_highlighted_text = gr.HighlightedText(
+            label="Token Importance (Score: 0-1)",
+            show_legend=True,
+            combine_adjacent=False 
+        )
+    
+    # BarPlot and PCA Plot Side-by-Side
+    with gr.Row():
+        with gr.Column(scale=1, min_width=400): # Adjusted min_width for BarPlot
+            with gr.Group():
+                gr.Markdown("### 📊 Top-K Bar Plot")
+                output_top_tokens_barplot = gr.BarPlot(
+                    label="Top-K Token Importance Scores", 
+                    x="token", 
+                    y="score", 
+                    tooltip=['token', 'score'],
+                    min_width=300 # BarPlot itself can define min_width
+                )
+        with gr.Column(scale=1, min_width=400): # Adjusted min_width for PCA
+            with gr.Group():
+                gr.Markdown("### 🌐 Token Embeddings 3D PCA (Interactive)")
+                output_pca_plot = gr.Plot(label="3D PCA of Token Embeddings (Colored by Importance Score)")
             
-    gr.Markdown("---")
+    gr.Markdown("---") # Separator
+    
     gr.Examples(
         examples=[
             ["This movie is an absolute masterpiece, captivating from start to finish.", 5],