Update app.py

app.py

@@ -47,13 +47,11 @@ NON_FEATURE_COLS = {
 
 _ASSET_CACHE = {}
 
-# --- CORE LOGIC ---
 def fetch_remote_json(url: str) -> Optional[dict]:
     try:
         response = requests.get(url, timeout=5)
         return response.json() if response.status_code == 200 else None
-    except Exception as e:
-        logger.error(f"Error: {e}")
+    except:
         return None
 
 def load_all_assets(key: str) -> Dict:
@@ -70,29 +68,39 @@ def generate_guide_markdown(assets: Dict) -> str:
     meta = assets.get("meta", {})
     params = meta.get("parameters", {})
     report = assets.get("report", {})
-    if not meta: return "### ⚠️ Metadata Unreachable"
+    
+    if not meta:
+        return "### ⚠️ Metadata Unreachable"
 
+    # Формируем таблицу БЕЗ лишних отступов слева (это ломает Markdown)
     families = report.get("families", {})
     fam_table = "| Family | Samples | Description |\n| :--- | :--- | :--- |\n"
     for f, count in families.items():
-        fam_table += f"| **{f.upper()}** | {count} | Synthetic {f} circuits |\n"
+        fam_table += f"| {f.upper()} | {count} | Synthetic {f} circuits |\n"
 
-    return f"""
-    ## 📖 Methodology & Release Notes: {meta.get('dataset_version', '1.0.0-demo')}
-    
-    ### 1. Generation Engine
-    - **Generator:** QSBench v{meta.get('generator_version', '5.x')}
-    - **Noise:** `{params.get('noise', 'None')}` (p={params.get('noise_prob', 0)})
-    - **Backend:** {meta.get('backend_device', 'GPU')} | {meta.get('precision_mode', 'double')}
-
-    ### 2. Structural Metrics
-    * **Gate Entropy:** Measures circuit "chaos" and gate distribution complexity.
-    * **Meyer-Wallach:** Quantifies global entanglement levels.
-    * **Adjacency:** Graph density of the qubit interaction map.
-
-    ### 3. Circuit Family Coverage
-    {fam_table}
+    guide = f"""
+## 📖 Methodology & Release Notes: {meta.get('dataset_version', '1.0.0-demo')}
+
+### 1. Generation Engine
+Dataset produced by **QSBench v{meta.get('generator_version', '5.0')}**.
+- **Hardware Profile:** {params.get('n_qubits')} Qubits | Depth: {params.get('depth')}
+- **Noise Model:** `{params.get('noise', 'None')}` (p={params.get('noise_prob', 0)})
+- **Backend:** {meta.get('backend_device', 'GPU')} | {meta.get('precision_mode', 'double')}
+
+### 2. Structural Metrics
+- **Gate Entropy:** Measures circuit complexity and gate distribution diversity.
+- **Meyer-Wallach:** Global entanglement scalar.
+- **Adjacency:** Topological density of the interaction graph.
+
+### 3. Circuit Family Coverage
+{fam_table}
+
+### 4. Split distribution
+- **Train:** {report.get('splits', {}).get('train')} 
+- **Validation:** {report.get('splits', {}).get('val')}
+- **Test:** {report.get('splits', {}).get('test')}
     """
+    return guide
 
 def update_explorer_view(ds_name: str, split_name: str):
     assets = load_all_assets(ds_name)
@@ -116,7 +124,6 @@ def sync_ml_inputs(ds_name: str):
 
 def train_baseline_model(ds_name: str, selected_features: List[str]):
     if not selected_features: return None, "### ❌ Error: Select features."
-    
     assets = load_all_assets(ds_name)
     df = assets["df"]
     target = "ideal_expval_Z_global" if "ideal_expval_Z_global" in df.columns else df.filter(like="expval").columns[0]
@@ -129,35 +136,30 @@ def train_baseline_model(ds_name: str, selected_features: List[str]):
     model.fit(X_train, y_train)
     preds = model.predict(X_test)
 
-    # Улучшенная визуализация (исправляет обрезку)
     sns.set_theme(style="whitegrid", context="talk")
-    fig, axes = plt.subplots(1, 3, figsize=(22, 7))
+    fig, axes = plt.subplots(1, 3, figsize=(24, 8))
     
-    # 1. Parity
-    axes[0].scatter(y_test, preds, alpha=0.4, color='#34495e')
+    axes[0].scatter(y_test, preds, alpha=0.4, color='#2c3e50')
     axes[0].plot([y.min(), y.max()], [y.min(), y.max()], 'r--', lw=2)
     axes[0].set_title(f"Accuracy (R²: {r2_score(y_test, preds):.3f})")
 
-    # 2. Importance (с поправкой на длинные названия)
     imp = model.feature_importances_
     idx = np.argsort(imp)[-10:]
-    axes[1].barh([selected_features[i] for i in idx], imp[idx], color='#1abc9c')
-    axes[1].set_title("Top Metrics Importance")
+    axes[1].barh([selected_features[i] for i in idx], imp[idx], color='#27ae60')
+    axes[1].set_title("Feature Importance")
 
-    # 3. Residuals
-    sns.histplot(y_test - preds, kde=True, ax=axes[2], color='#e67e22')
-    axes[2].set_title("Prediction Error")
+    sns.histplot(y_test - preds, kde=True, ax=axes[2], color='#d35400')
+    axes[2].set_title("Residuals")
 
-    plt.tight_layout() # Автоматически подгоняет отступы
-    return fig, f"**MAE:** {mean_absolute_error(y_test, preds):.4f}"
+    plt.tight_layout(pad=4.0)
+    return fig, f"**Model Performance** | MAE: {mean_absolute_error(y_test, preds):.4f}"
 
-# --- UI ---
 with gr.Blocks(theme=gr.themes.Soft(), title="QSBench Hub") as demo:
     gr.Markdown("# 🌌 QSBench: Quantum Analytics Hub")
     
     with gr.Tabs():
         with gr.TabItem("🔎 Explorer"):
-            metadata_box = gr.Markdown("### Syncing...")
+            metadata_box = gr.Markdown("### Synchronizing data...")
             with gr.Row():
                 ds_select = gr.Dropdown(choices=list(REPO_CONFIG.keys()), value="Core (Clean)", label="Dataset Pack")
                 split_select = gr.Dropdown(choices=["train"], value="train", label="Subset")
@@ -176,18 +178,16 @@ with gr.Blocks(theme=gr.themes.Soft(), title="QSBench Hub") as demo:
                     plot_out = gr.Plot(); txt_out = gr.Markdown()
 
         with gr.TabItem("📖 Methodology & Guide"):
-            guide_md = gr.Markdown("Loading guide...")
+            guide_md = gr.Markdown("Loading research guide...")
 
-    # FOOTER
     gr.Markdown(f"""
-    ---
-    ### 🔗 Project Resources & Links
-    * **🤗 Hugging Face:** [QSBench Org](https://huggingface.co/QSBench) — Dataset shards and demos.
-    * **💻 GitHub:** [QSBench Repository](https://github.com/QSBench) — Generator source code.
-    * **🌐 Project Site:** [qsbench.github.io](https://qsbench.github.io) — Documentation & Papers.
+---
+### 🔗 Official Links & Resources
+* **🤗 Hugging Face:** [**QSBench Organization**](https://huggingface.co/QSBench)
+* **💻 GitHub:** [**QSBench Repository**](https://github.com/QSBench)
+* **🌐 Website:** [**qsbench.github.io**](https://qsbench.github.io)
     """)
 
-    # Handlers
     ds_select.change(update_explorer_view, [ds_select, split_select], [split_select, data_table, code_raw, code_tr, metadata_box, guide_md])
     ml_ds.change(sync_ml_inputs, [ml_ds], [ml_feat])
     btn.click(train_baseline_model, [ml_ds, ml_feat], [plot_out, txt_out])