literal instead of eval

app.py

@@ -57,14 +57,17 @@ def Sonar_Web_Search_Tool(arg1: str, arg2: str) -> str:
 
 def parse_json(text: str):
     """
-    A minimal JSON parser workaround using eval.
-    Replaces 'true', 'false', and 'null' with their Python equivalents.
-    WARNING: Use only with trusted input.
+    A safer JSON parser using ast.literal_eval.
+    Converts JSON-like strings to Python objects without executing code.
+    Handles common JSON literals (true, false, null) by converting them to Python equivalents.
     """
-    safe_text = text.replace("true", "True").replace("false", "False").replace("null", "None")
+    # Replace JSON literals with Python equivalents
+    prepared_text = text.replace("true", "True").replace("false", "False").replace("null", "None")
+    
     try:
-        return eval(safe_text, {"__builtins__": None}, {})
-    except Exception as e:
+        import ast
+        return ast.literal_eval(prepared_text)
+    except (SyntaxError, ValueError) as e:
         raise ValueError(f"Failed to parse JSON: {str(e)}")
 
 def Dataset_Creator_Function(dataset_name: str, conversation_data: str) -> str:
@@ -82,7 +85,7 @@ def Dataset_Creator_Function(dataset_name: str, conversation_data: str) -> str:
     """
     log_text = ""
     try:
-        # Required imports (json replaced by our parse_json)
+        # Required imports
         import pandas as pd
         from datasets import Dataset, DatasetDict
         from huggingface_hub import HfApi
@@ -113,22 +116,27 @@ def Dataset_Creator_Function(dataset_name: str, conversation_data: str) -> str:
         # Process input data
         created_ds = None
         try:
-            # Use parse_json instead of json.loads
-            json_data = parse_json(conversation_data)
-            if isinstance(json_data, list) and all(isinstance(item, dict) for item in json_data):
-                log_text += f"Processing JSON array with {len(json_data)} items\n"
-                df = pd.DataFrame(json_data)
-                ds = Dataset.from_pandas(df)
-                created_ds = DatasetDict({"train": ds})
-            elif isinstance(json_data, dict):
-                log_text += "Processing single JSON object\n"
-                df = pd.DataFrame([json_data])
-                ds = Dataset.from_pandas(df)
-                created_ds = DatasetDict({"train": ds})
-            else:
-                raise ValueError("JSON not recognized as array or single object")
-        except Exception as e:
-            log_text += f"Not processing as JSON: {str(e)}\n"
+            # Try parsing as JSON using the safer parse_json function
+            try:
+                json_data = parse_json(conversation_data)
+                
+                # Process based on data structure
+                if isinstance(json_data, list) and all(isinstance(item, dict) for item in json_data):
+                    log_text += f"Processing JSON array with {len(json_data)} items\n"
+                    df = pd.DataFrame(json_data)
+                    ds = Dataset.from_pandas(df)
+                    created_ds = DatasetDict({"train": ds})
+                elif isinstance(json_data, dict):
+                    log_text += "Processing single JSON object\n"
+                    df = pd.DataFrame([json_data])
+                    ds = Dataset.from_pandas(df)
+                    created_ds = DatasetDict({"train": ds})
+                else:
+                    raise ValueError("JSON not recognized as array or single object")
+            except Exception as json_error:
+                log_text += f"Not processing as JSON: {str(json_error)}\n"
+                raise json_error  # Propagate to next handler
+        except Exception:
             # Try pipe-separated format
             lines = conversation_data.strip().split('\n')
             if '|' in conversation_data and len(lines) > 1: