Update app.py

app.py

@@ -56,6 +56,17 @@ SOFT_EXCLUDE_PATTERNS = ["ideal_", "noisy_", "sign_ideal_", "sign_noisy_"]
 
 _ASSET_CACHE: Dict[str, pd.DataFrame] = {}
 
+# ========================= HELPERS =========================
+
+def load_guide_content() -> str:
+    """
+    Read the GUIDE.md file from the root directory.
+    """
+    try:
+        with open("GUIDE.md", "r", encoding="utf-8") as f:
+            return f.read()
+    except FileNotFoundError:
+        return "### ⚠️ GUIDE.md not found in the root directory."
 
 def safe_parse(value):
     """Safely parse stringified Python literals."""
@@ -66,7 +77,6 @@ def safe_parse(value):
             return value
     return value
 
-
 def adjacency_features(adj_value) -> Dict[str, float]:
     """Derive graph statistics from an adjacency matrix."""
     parsed = safe_parse(adj_value)
@@ -98,7 +108,6 @@ def adjacency_features(adj_value) -> Dict[str, float]:
             "adj_degree_std": np.nan,
         }
 
-
 def qasm_features(qasm_value) -> Dict[str, float]:
     """Extract lightweight text statistics from QASM."""
     if not isinstance(qasm_value, str) or not qasm_value.strip():
@@ -123,7 +132,6 @@ def qasm_features(qasm_value) -> Dict[str, float]:
         "qasm_measure_count": float(measure_count),
     }
 
-
 def enrich_dataframe(df: pd.DataFrame) -> pd.DataFrame:
     """Add derived numeric features and compute error targets."""
     df = df.copy()
@@ -143,9 +151,8 @@ def enrich_dataframe(df: pd.DataFrame) -> pd.DataFrame:
             df[error_col] = df[noisy_col] - df[ideal_col]
     return df
 
-
 def load_single_dataset() -> pd.DataFrame:
-    """Fetch dataset from repository and apply feature enrichment."""
+    """Fetch and cache the dataset."""
     key = "amplitude_damping"
     if key not in _ASSET_CACHE:
         logger.info("Loading dataset: %s", key)
@@ -155,7 +162,6 @@ def load_single_dataset() -> pd.DataFrame:
         _ASSET_CACHE[key] = df
     return _ASSET_CACHE[key]
 
-
 def get_available_feature_columns(df: pd.DataFrame) -> List[str]:
     """Retrieve filtered list of numerical feature columns."""
     numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()
@@ -168,7 +174,6 @@ def get_available_feature_columns(df: pd.DataFrame) -> List[str]:
         features.append(col)
     return sorted(features)
 
-
 def default_feature_selection(features: List[str]) -> List[str]:
     """Provide a curated list of default structural features."""
     preferred = [
@@ -179,7 +184,6 @@ def default_feature_selection(features: List[str]) -> List[str]:
     selected = [f for f in preferred if f in features]
     return selected[:10] if selected else features[:10]
 
-
 def make_regression_figure(y_true: np.ndarray, y_pred: np.ndarray, basis: str) -> plt.Figure:
     """Generate diagnostic regression plots."""
     fig, axs = plt.subplots(1, 2, figsize=(14, 6))
@@ -202,7 +206,6 @@ def make_regression_figure(y_true: np.ndarray, y_pred: np.ndarray, basis: str) -
     fig.tight_layout()
     return fig
 
-
 def train_regressor(
     feature_columns: List[str],
     test_size: float,
@@ -267,8 +270,8 @@ def train_regressor(
 
     return fig_z, metrics_text, fig_x, fig_y
 
-
 # ======================= EXPLORER FUNCTIONS =======================
+
 def build_dataset_profile(df: pd.DataFrame) -> str:
     """Generate Markdown summary of the loaded dataset."""
     return (
@@ -278,7 +281,6 @@ def build_dataset_profile(df: pd.DataFrame) -> str:
         f"**Classes / Noise:** amplitude_damping"
     )
 
-
 def refresh_explorer(dataset_key: str, split_name: str):
     """Update Explorer tab components."""
     df = load_single_dataset()
@@ -312,6 +314,7 @@ def refresh_explorer(dataset_key: str, split_name: str):
         summary_box,
     )
 
+# ========================= INTERFACE =========================
 
 CUSTOM_CSS = """
 .gradio-container {
@@ -366,7 +369,7 @@ with gr.Blocks(title=APP_TITLE) as demo:
             metrics = gr.Markdown()
 
         with gr.TabItem("📖 Guide"):
-            gr.Markdown("Guide will be added in the next step.")
+            gr.Markdown(load_guide_content())
 
     gr.Markdown("---")
     gr.Markdown(
@@ -409,6 +412,5 @@ with gr.Blocks(title=APP_TITLE) as demo:
     )
     demo.load(sync_features, [dataset_dropdown], [feature_picker])
 
-
 if __name__ == "__main__":
     demo.launch(theme=gr.themes.Soft(), css=CUSTOM_CSS)