Update app.py

app.py

@@ -134,7 +134,6 @@ def enrich_dataframe(df: pd.DataFrame) -> pd.DataFrame:
         qasm_df = df[qasm_source].apply(qasm_features).apply(pd.Series)
         df = pd.concat([df, qasm_df], axis=1)
 
-    # Create error targets
     for basis in ["Z", "X", "Y"]:
         ideal_col = f"ideal_expval_{basis}_global"
         noisy_col = f"noisy_expval_{basis}_global"
@@ -263,6 +262,49 @@ def train_regressor(
     return fig_z, metrics_text, fig_x, fig_y, None
 
 
+# ======================= EXPLORER FUNCTIONS =======================
+def build_dataset_profile(df: pd.DataFrame) -> str:
+    return (
+        f"### Dataset profile\n\n"
+        f"**Rows:** {len(df):,} \n"
+        f"**Columns:** {len(df.columns):,} \n"
+        f"**Classes / Noise:** amplitude_damping"
+    )
+
+
+def refresh_explorer(dataset_key: str, split_name: str):
+    df = load_single_dataset()
+    splits = df["split"].dropna().unique().tolist() if "split" in df.columns else ["train"]
+    if not splits:
+        splits = ["train"]
+    if split_name not in splits:
+        split_name = splits[0]
+
+    filtered = df[df["split"] == split_name] if "split" in df.columns else df
+    display_df = filtered.head(12).copy()
+
+    raw_qasm = display_df["qasm_raw"].iloc[0] if not display_df.empty and "qasm_raw" in display_df.columns else "// N/A"
+    transpiled_qasm = display_df["qasm_transpiled"].iloc[0] if not display_df.empty and "qasm_transpiled" in display_df.columns else "// N/A"
+
+    profile_box = build_dataset_profile(df)
+    summary_box = (
+        f"### Split summary\n\n"
+        f"**Dataset:** `{dataset_key}` \n"
+        f"**Label:** `amplitude_damping` \n"
+        f"**Available splits:** {', '.join(splits)} \n"
+        f"**Preview rows:** {len(display_df)}"
+    )
+
+    return (
+        gr.update(choices=splits, value=split_name),
+        display_df,
+        raw_qasm,
+        transpiled_qasm,
+        profile_box,
+        summary_box,
+    )
+
+
 CUSTOM_CSS = """
 .gradio-container {
     max-width: 1400px !important;
@@ -277,6 +319,25 @@ with gr.Blocks(title=APP_TITLE) as demo:
     gr.Markdown(APP_SUBTITLE)
 
     with gr.Tabs():
+        with gr.TabItem("🔎 Explorer"):
+            dataset_dropdown = gr.Dropdown(
+                list(REPO_CONFIG.keys()),
+                value="amplitude_damping",
+                label="Dataset",
+            )
+            split_dropdown = gr.Dropdown(
+                ["train"],
+                value="train",
+                label="Split",
+            )
+            profile_box = gr.Markdown(value="### Loading dataset...")
+            summary_box = gr.Markdown(value="### Loading split summary...")
+            explorer_df = gr.Dataframe(label="Preview", interactive=False)
+
+            with gr.Row():
+                raw_qasm = gr.Code(label="Raw QASM", language=None)
+                transpiled_qasm = gr.Code(label="Transpiled QASM", language=None)
+
         with gr.TabItem("🧠 Regression Training"):
             feature_picker = gr.CheckboxGroup(
                 label="Input features (circuit structure + topology)",
@@ -299,7 +360,6 @@ with gr.Blocks(title=APP_TITLE) as demo:
         with gr.TabItem("📖 Guide"):
             gr.Markdown("Guide will be added in the next step.")
 
-    # Footer с твоими ссылками
     gr.Markdown("---")
     gr.Markdown(
         "### 🔗 Links\n"
@@ -308,20 +368,38 @@ with gr.Blocks(title=APP_TITLE) as demo:
         "[GitHub](https://github.com/QSBench)"
     )
 
-    # Callbacks (те же, что раньше)
+    # ======================= CALLBACKS =======================
     def sync_features():
         df = load_single_dataset()
         features = get_available_feature_columns(df)
         defaults = default_feature_selection(features)
         return gr.update(choices=features, value=defaults)
 
+    dataset_dropdown.change(
+        refresh_explorer,
+        [dataset_dropdown, split_dropdown],
+        [split_dropdown, explorer_df, raw_qasm, transpiled_qasm, profile_box, summary_box],
+    )
+    split_dropdown.change(
+        refresh_explorer,
+        [dataset_dropdown, split_dropdown],
+        [split_dropdown, explorer_df, raw_qasm, transpiled_qasm, profile_box, summary_box],
+    )
+    dataset_dropdown.change(sync_features, [dataset_dropdown], [feature_picker])
+
     run_btn.click(
         train_regressor,
         [feature_picker, test_size, max_iter, max_depth, seed],
         [plot_z, metrics, plot_x, plot_y],
     )
 
-    demo.load(sync_features, outputs=[feature_picker])
+    demo.load(
+        refresh_explorer,
+        [dataset_dropdown, split_dropdown],
+        [split_dropdown, explorer_df, raw_qasm, transpiled_qasm, profile_box, summary_box],
+    )
+    demo.load(sync_features, [dataset_dropdown], [feature_picker])
+
 
 if __name__ == "__main__":
     demo.launch(theme=gr.themes.Soft(), css=CUSTOM_CSS)