Update app.py

app.py

@@ -583,7 +583,7 @@ sequence_generators = {
     "Flexibilidad": generate_flexibility_sequence,
 }
 
-# --- Data Handling (Unchanged) ---
+# --- Data Handling (Unchanged structure, FIX SyntaxError) ---
 def guardar_resultado(alias, level, scores, test_order):
     ts = datetime.now().strftime("%Y%m%d_%H%M%S")
     avg = sum(scores.values()) / len(scores) if scores else 0.0
@@ -601,37 +601,102 @@ def guardar_resultado(alias, level, scores, test_order):
         except Exception as e: print(f"ERROR inesperado al guardar resultado: {e}"); traceback.print_exc(); return False
 
 def leer_historial_df():
-    results = []; expected_base_cols = ['Alias', 'Timestamp', 'Level', 'AvgPrec']; expected_test_cols = AVAILABLE_TESTS; processed_rows = []
-    if not os.path.isfile(ARCHIVO_RESULTADOS): return None
+    results = []
+    if not os.path.isfile(ARCHIVO_RESULTADOS):
+        return None
+
+    expected_base_cols = ['Alias', 'Timestamp', 'Level', 'AvgPrec']
+    expected_test_cols = AVAILABLE_TESTS
+    processed_rows = []
+
     with csv_lock:
         try:
             with open(ARCHIVO_RESULTADOS, 'r', newline='', encoding='utf-8') as f:
-                if not f.read(1): return None; f.seek(0); reader = csv.DictReader(f)
-                if not reader.fieldnames or not all(col in reader.fieldnames for col in expected_base_cols): pass
+                first_char = f.read(1)
+                if not first_char: return None # Empty file
+                f.seek(0)
+                reader = csv.DictReader(f)
+                # Tolerate missing/bad header or missing columns slightly more robustly
+                if not reader.fieldnames:
+                    print("WARN: No header found in results CSV.")
+                    # Attempt to read assuming standard order if needed, or just return None
+                    return None # Simplest fallback
+
                 for i, row in enumerate(reader):
                     try:
-                        proc_row = {'Alias': str(row.get('Alias', f'Fila_{i+1}_NA'))[:25],'Timestamp': str(row.get('Timestamp', 'NA'))[:15],'Level': int(float(row.get('Level', 0))),'AvgPrec': float(row.get('AvgPrec', 0.0))}
-                        for test in expected_test_cols: raw_val = row.get(test); \
-                            try: proc_row[test] = float(raw_val) if raw_val is not None else 0.0; \
-                            except (ValueError, TypeError): proc_row[test] = 0.0
+                        # Basic data extraction with defaults
+                        proc_row = {
+                            'Alias': str(row.get('Alias', f'Fila_{i+1}_NA'))[:25],
+                            'Timestamp': str(row.get('Timestamp', 'NA'))[:15],
+                            'Level': 0, # Default
+                            'AvgPrec': 0.0 # Default
+                        }
+                        # Safely parse Level and AvgPrec
+                        try: proc_row['Level'] = int(float(row.get('Level', 0)))
+                        except (ValueError, TypeError): pass # Keep default 0
+                        try: proc_row['AvgPrec'] = float(row.get('AvgPrec', 0.0))
+                        except (ValueError, TypeError): pass # Keep default 0.0
+
+                        # Process test scores safely
+                        for test in expected_test_cols:
+                             raw_val = row.get(test) # Use .get() for safety if column missing
+                             # --- START FIX for SyntaxError ---
+                             try:
+                                 # Attempt conversion only if raw_val is not None
+                                 proc_row[test] = float(raw_val) if raw_val is not None else 0.0
+                             except (ValueError, TypeError):
+                                 # If conversion fails or raw_val was None/invalid, assign 0.0
+                                 proc_row[test] = 0.0
+                             # --- END FIX for SyntaxError ---
                         processed_rows.append(proc_row)
-                    except (ValueError, TypeError, KeyError): continue
+                    except KeyError as e:
+                        # Log if a critical expected column is missing, but try to continue
+                        print(f"WARN: Missing expected key {e} in CSV row {i+1}. Skipping row or using defaults.")
+                        continue
+                    except Exception as inner_e:
+                        # Catch other unexpected errors processing a row
+                        print(f"ERROR processing CSV row {i+1}: {inner_e}. Skipping row.")
+                        traceback.print_exc()
+                        continue # Skip malformed row
+
                 results = processed_rows
-        except FileNotFoundError: return None
-        except Exception as e: print(f"ERROR Crítico al leer historial CSV: {e}"); traceback.print_exc(); return None
+        except FileNotFoundError:
+            return None
+        except Exception as e:
+            print(f"ERROR Crítico al leer historial CSV: {e}")
+            traceback.print_exc()
+            return None # Critical error reading the file
+
     if not results: return None
-    try: results.sort(key=lambda x: (x.get('Level', 0), x.get('AvgPrec', 0.0)), reverse=True)
-    except Exception: pass
+
+    try: # Sort best level/score first
+        results.sort(key=lambda x: (x.get('Level', 0), x.get('AvgPrec', 0.0)), reverse=True)
+    except Exception: pass # Ignore sorting errors if data is inconsistent
+
     if PANDAS_AVAILABLE:
         try:
-            df = pd.DataFrame(results); cols_to_display = ['Alias', 'Level', 'AvgPrec', 'Timestamp']; existing_cols = [col for col in cols_to_display if col in df.columns]; df_display = df[existing_cols]
-            rename_map = {'Level': 'Lvl', 'AvgPrec': 'Prec%', 'Timestamp': 'Fecha'}; cols_to_rename = {k: v for k, v in rename_map.items() if k in df_display.columns}
+            df = pd.DataFrame(results)
+            # Ensure essential columns exist before proceeding
+            cols_to_display = ['Alias', 'Level', 'AvgPrec', 'Timestamp']
+            existing_cols = [col for col in cols_to_display if col in df.columns]
+            if not existing_cols: return results[:25] # Fallback if essential columns missing
+
+            df_display = df[existing_cols]
+            rename_map = {'Level': 'Lvl', 'AvgPrec': 'Prec%', 'Timestamp': 'Fecha'}
+            cols_to_rename = {k: v for k, v in rename_map.items() if k in df_display.columns}
             if cols_to_rename: df_display = df_display.rename(columns=cols_to_rename)
+
+            # Safely convert types after renaming
             if 'Lvl' in df_display.columns: df_display['Lvl'] = pd.to_numeric(df_display['Lvl'], errors='coerce').fillna(0).astype(int)
             if 'Prec%' in df_display.columns: df_display['Prec%'] = pd.to_numeric(df_display['Prec%'], errors='coerce').fillna(0.0).round(1)
+
             return df_display.head(25)
-        except Exception as e: print(f"ERROR creando DataFrame con Pandas (usando lista básica): {e}"); traceback.print_exc(); return results[:25]
-    else: return results[:25]
+        except Exception as e:
+            print(f"ERROR creando DataFrame con Pandas (usando lista básica): {e}")
+            traceback.print_exc()
+            return results[:25] # Fallback to list
+    else:
+        return results[:25] # Return raw list if pandas not available
 
 # --- Core Trial Logic ---
 # --- Modified process_response (v8.2 - Inhibition logic) ---