fix

app.py

@@ -1,23 +1,27 @@
 import gradio as gr
 import pandas as pd
-import numpy as np
 from datasets import load_dataset
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.metrics import r2_score
 import matplotlib.pyplot as plt
 
-# Загрузка датасета
+# Загружаем датасет один раз при старте
 ds = load_dataset("QSBench/QSBench-Core-v1.0.0-demo")
 
-# Функция для отображения данных выбранного сплита
+# Функция для отображения таблицы данных выбранного сплита
 def show_data(split):
-    df = pd.DataFrame(ds[split])
-    return df.head(10)
+    try:
+        df = pd.DataFrame(ds[split])
+        # Показываем первые 10 строк, все колонки
+        return df.head(10)
+    except Exception as e:
+        return f"Error loading data: {e}"
 
-# Функция для обучения модели
-def train_and_plot():
+# Функция для обучения модели и создания графика
+def train_model():
     feature_cols = ["total_gates", "gate_entropy", "meyer_wallach"]
     target_col = "ideal_expval_Z_global"
+
     X_train = pd.DataFrame(ds["train"])[feature_cols]
     y_train = pd.DataFrame(ds["train"])[target_col]
     X_test = pd.DataFrame(ds["test"])[feature_cols]
@@ -36,25 +40,50 @@ def train_and_plot():
     ax.set_title(f"Predictions vs. Truth (R² = {r2:.4f})")
     return fig
 
+# Интерфейс
 with gr.Blocks(title="QSBench Demo Explorer") as demo:
     gr.Markdown("""
     # QSBench Core Demo Explorer
-    Interactive demo of the **QSBench Core Demo** dataset – 200 synthetic quantum circuits (6 qubits, depth 4).
+    Interactive demo of the **QSBench Core Demo** dataset – 200 synthetic quantum circuits (6 qubits, depth 4).  
     This space shows how to load the data, inspect it, and train a simple model on the ideal expectation values.
 
     👉 **Full datasets (up to 200k samples, noisy versions, 10‑qubit transpilation packs) are available for purchase.**  
     [Visit the QSBench website](https://qsbench.github.io/)
     """)
+
     with gr.Tabs():
         with gr.TabItem("Data Explorer"):
-            split_selector = gr.Dropdown(choices=["train", "validation", "test"], label="Choose a split", value="train")
-            data_table = gr.Dataframe()
-            split_selector.change(fn=show_data, inputs=split_selector, outputs=data_table)
+            split_selector = gr.Dropdown(
+                choices=["train", "validation", "test"],
+                label="Choose a split",
+                value="train"
+            )
+            data_table = gr.Dataframe(
+                label="First 10 rows",
+                interactive=False
+            )
+            split_selector.change(
+                fn=show_data,
+                inputs=split_selector,
+                outputs=data_table
+            )
+            # Загружаем данные по умолчанию при старте
+            demo.load(fn=lambda: show_data("train"), outputs=data_table)
+
         with gr.TabItem("Model Demo"):
             train_button = gr.Button("Train Random Forest")
             plot_output = gr.Plot()
-            train_button.click(fn=train_and_plot, outputs=plot_output)
+            train_button.click(fn=train_model, outputs=plot_output)
+
     gr.Markdown("---")
-    gr.Markdown("### Get the full datasets\n- **QSBench Core** – 75k clean circuits (8 qubits)\n- **Depolarizing Noise Pack** – 150k circuits with depolarizing noise\n- **Amplitude Damping Pack** – 150k circuits with T1‑like relaxation\n- **Transpilation Hardware Pack** – 200k circuits (10 qubits) with hardware‑aware transpilation\n\n🔗 [Browse all datasets and purchase licenses](https://qsbench.github.io/)")
+    gr.Markdown("""
+    ### Get the full datasets
+    - **QSBench Core** – 75k clean circuits (8 qubits)  
+    - **Depolarizing Noise Pack** – 150k circuits with depolarizing noise  
+    - **Amplitude Damping Pack** – 150k circuits with T1‑like relaxation  
+    - **Transpilation Hardware Pack** – 200k circuits (10 qubits) with hardware‑aware transpilation  
+
+    🔗 [Browse all datasets and purchase licenses](https://qsbench.github.io/)
+    """)
 
 demo.launch()