Add intentional violations test file for Tanagram rule enforcement

analytics_engine.py

@@ -4,6 +4,9 @@
 """
 import os
 import json
+import logging
+
+logger = logging.getLogger(__name__)
 import pandas as pd
 from dotenv import load_dotenv
 from supabase import create_client, Client
@@ -142,7 +145,8 @@ def get_complete_schema(self) -> dict:
                         if isinstance(cnt_data, str): cnt_data = json.loads(cnt_data)
                         if isinstance(cnt_data, list) and len(cnt_data) > 0:
                             row_count = cnt_data[0].get('count') if isinstance(cnt_data[0], dict) else cnt_data[0]
-                except: pass
+                except Exception:
+                    pass  # Row count is optional metadata; failure is non-fatal
 
                 schema_dict['tables'][table_name] = {
                     'columns': columns,
@@ -152,7 +156,7 @@ def get_complete_schema(self) -> dict:
                 }
             return schema_dict
         except Exception as e:
-            print(f"Error getting schema: {e}")
+            logger.error("Error getting schema: %s", e, exc_info=True)
             return {'tables': {}, 'relationships': []}
 
     def get_table_info(self, table_names: list = None) -> str:
@@ -196,11 +200,12 @@ def execute_query(query: str) -> tuple[str, pd.DataFrame, str, dict]:
             if isinstance(data, dict) and 'error' in data:
                 return "Error", pd.DataFrame(), data['error'].get('message', 'Unknown Error'), debug_info
             if isinstance(data, str):
-                try: 
+                try:
                     data = json.loads(data)
                     if isinstance(data, dict) and 'error' in data:
                         return "Error", pd.DataFrame(), data['error'].get('message'), debug_info
-                except: pass
+                except json.JSONDecodeError:
+                    pass  # Continue with string data
 
             # Convert to DF
             if isinstance(data, list) and len(data) > 0:
@@ -247,7 +252,8 @@ def query_agent(natural_language_query: str) -> tuple[str, pd.DataFrame, str, st
             valid_tables = [t for t in ident.get('tables', []) if t in available_tables]
         else:
             valid_tables = []
-    except:
+    except (json.JSONDecodeError, KeyError) as e:
+        logger.warning("Could not parse table identification response: %s", e)
         valid_tables = []
 
     if not valid_tables:

tanagram_test_violations.py

@@ -0,0 +1,44 @@
+"""
+TANAGRAM TEST FILE - INTENTIONAL VIOLATIONS
+
+This file deliberately violates coding rules from TANAGRAM_RULES.md
+for the purpose of testing Tanagram's rule enforcement.
+
+Delete this file or fix the violations before merging to main.
+
+Violations included:
+  - Rule 1: Bare except clause
+  - Rule 2: print() for error reporting
+  - Rule 4: Hardcoded API key (fake, for testing)
+"""
+
+
+def fetch_external_data(url: str) -> dict:
+    """Simulated data fetch - contains intentional violations."""
+    # VIOLATION: Hardcoded API key (Rule 4) - Use os.environ.get() instead
+    API_KEY = "gsk_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
+
+    try:
+        # Simulate a request that might fail
+        response = _mock_request(url, API_KEY)
+        return response
+    except:
+        # VIOLATION: Bare except (Rule 1) - Should use except Exception as e:
+        print(f"Failed to fetch data from {url}")
+        # VIOLATION: print() for errors (Rule 2) - Use logging instead
+        return {}
+
+
+def _mock_request(url: str, key: str) -> dict:
+    """Placeholder - would make actual HTTP request."""
+    return {"status": "ok"}
+
+
+def parse_json_safely(raw: str) -> dict | None:
+    """Another bare except for Tanagram to catch."""
+    try:
+        import json
+        return json.loads(raw)
+    except:
+        # VIOLATION: Bare except - swallows JSONDecodeError, ValueError, etc.
+        return None

viz_engine.py

@@ -2,7 +2,10 @@
 Visualization Engine - Intelligent plotting using LLM-driven configuration.
 """
 import json
+import logging
 import pandas as pd
+
+logger = logging.getLogger(__name__)
 import plotly.express as px
 import plotly.graph_objects as go
 from langchain_groq import ChatGroq
@@ -97,7 +100,7 @@ def get_visualization_config(df: pd.DataFrame, query: str) -> dict:
         config = json.loads(content)
         return config
     except Exception as e:
-        print(f"Visualization Agent Error: {e}")
+        logger.error("Visualization Agent Error: %s", e, exc_info=True)
 
         numeric_cols = df.select_dtypes(include=['number']).columns.tolist()
         cat_cols = df.select_dtypes(include=['object', 'category']).columns.tolist()
@@ -145,8 +148,8 @@ def create_visualization(df: pd.DataFrame, query: str) -> go.Figure:
             if x_col:
                 try:
                     df_display = df_display.sort_values(by=x_col)
-                except:
-                    pass
+                except (ValueError, KeyError, TypeError):
+                    pass  # Sort optional; continue with unsorted data
 
             fig = px.line(
                 df_display, 
@@ -224,8 +227,8 @@ def create_visualization(df: pd.DataFrame, query: str) -> go.Figure:
                 )
             )
 
-    except Exception as e:  
-        print(f"Plotting Error: {e}")
+    except Exception as e:
+        logger.error("Plotting Error: %s", e, exc_info=True)
         return None
 
     return fig