修复了问题无法显示的bug

app.py

@@ -331,7 +331,11 @@ with gr.Blocks() as demo:
                 ret[question_info_textbox_p2] = gr.update(value=question_info_p2)
                 ret[statistics_textbox] = gr.update(value=create_statistic(df_list, id_list, interval=interval))
                 ret[plot] = gr.update(value=create_plot(df_list, id_list, interval=interval))
-                ret[user_input_box] = gr.update(value=df[COLUMN_QUESTION][0])
+                if df[COLUMN_OPTION][0] is not None:
+                    user_input_box_value = df[COLUMN_QUESTION][0] + '\n\nOptions:\n' + df[COLUMN_OPTION][0]
+                else:
+                    user_input_box_value = df[COLUMN_QUESTION][0]
+                ret[user_input_box] = gr.update(value=user_input_box_value)
                 ret[user_output_box] = gr.update(value=df[COLUMN_ANSWER][0])
                 ret[submit_info_box] = gr.update(value="")
             else:
@@ -419,8 +423,8 @@ with gr.Blocks() as demo:
 if __name__ == "__main__":
 
     app = gr.mount_gradio_app(app, demo, path="/")
-    # host = "127.0.0.1" if os.getenv("DEV") else "0.0.0.0"
-    host = "0.0.0.0"
+    host = "127.0.0.1" 
+    # host = "0.0.0.0"
     # import subprocess
     # subprocess.Popen(["python", "test_server.py"])
     uvicorn.run(app, host=host, port=7860)

utils.py

@@ -57,25 +57,44 @@ def flatten_ndarray_column(df, column_name, rows_to_include, name_mapping_map:di
 
     return df
 
-def create_plot(dfs:list[pd.DataFrame], ids:list[str], interval:list[int, int]=None) -> go.Figure:
+def create_plot(dfs: list[pd.DataFrame], ids: list[str], interval: list[int, int] = None) -> go.Figure:
     """
     创建一个包含所有传入 DataFrame 的线图。
     """
     fig = go.Figure()
+    
     for df, df_id in zip(dfs, ids):
         if interval:
             df = df.iloc[interval[0]:interval[1]]
+        
         df_normalized = df.copy()
-        if len(df.columns) > 2:
-            df_normalized[df.columns[1:]] = (df[df.columns[1:]] - df[df.columns[1:]].mean()) / df[df.columns[1:]].std()
-        for i, column in enumerate(df.columns[1:]):
+        if len(df.columns) > 1:
+            for column in df.columns[1:]:
+                min_val = df[column].min()
+                max_val = df[column].max()
+                df_normalized[column] = (df[column] - min_val) / (max_val - min_val) if max_val != min_val else 0
+        
+        for column in df.columns[1:]:
+            # 归一化数据曲线（默认可见）
             fig.add_trace(go.Scatter(
-                x=list(range(len(df[df.columns[0]]))), 
+                x=df[df.columns[0]], 
                 y=df_normalized[column], 
                 mode='lines', 
-                name=f"item_{df_id} - {column}",
-                # visible=True if i == 0 else 'legendonly'
-                visible=True
+                name=f"Normalized {df_id} - {column}",
+                hovertext=list(range(len(df))),
+                hoverinfo="x+text+y",
+                visible=True  # 归一化数据默认可见
+            ))
+            
+            # 原始数据曲线（默认隐藏）
+            fig.add_trace(go.Scatter(
+                x=df[df.columns[0]], 
+                y=df[column], 
+                mode='lines', 
+                name=f"Raw {df_id} - {column}",
+                hovertext=list(range(len(df))),
+                hoverinfo="x+text+y",
+                visible='legendonly'  # 原始数据默认隐藏
             ))
     
     # 配置图例
@@ -88,9 +107,12 @@ def create_plot(dfs:list[pd.DataFrame], ids:list[str], interval:list[int, int]=N
             xanchor="center",
             x=0.5
         ),
-        xaxis_title='Time',
+        xaxis=dict(
+            title="Timestamp",
+        ),
         yaxis_title='Values'
     )
+    
     return fig
 
 def create_statistic(dfs: list[pd.DataFrame], ids: list[str], interval:list[int, int]=None) -> pd.DataFrame: