fix: adds better boolean check for DataFrame and fixes output display (#4933)

* feat: Add DataFrameInput to inputs module

* feat: add DataFrame support and refactor array processing

* feat: add truth value testing for DataFrame class

* refactor: remove Python 2 compatibility method from DataFrame class

src/backend/base/langflow/inputs/__init__.py

@@ -1,6 +1,7 @@
 from .inputs import (
     BoolInput,
     CodeInput,
+    DataFrameInput,
     DataInput,
     DefaultPromptField,
     DictInput,
@@ -51,4 +52,5 @@ __all__ = [
     "SliderInput",
     "StrInput",
     "TableInput",
+    "DataFrameInput",
 ]

src/backend/base/langflow/schema/artifact.py

@@ -6,6 +6,7 @@ from loguru import logger
 from pydantic import BaseModel
 
 from langflow.schema.data import Data
+from langflow.schema.dataframe import DataFrame
 from langflow.schema.encoders import CUSTOM_ENCODERS
 from langflow.schema.message import Message
 from langflow.schema.serialize import recursive_serialize_or_str
@@ -40,9 +41,8 @@ def get_artifact_type(value, build_result=None) -> str:
         case dict():
             result = ArtifactType.OBJECT
 
-        case list():
+        case list() | DataFrame():
             result = ArtifactType.ARRAY
-
     if result == ArtifactType.UNKNOWN and (
         (build_result and isinstance(build_result, Generator))
         or (isinstance(value, Message) and isinstance(value.text, Generator))
@@ -52,17 +52,21 @@ def get_artifact_type(value, build_result=None) -> str:
     return result.value
 
 
+def _to_list_of_dicts(raw):
+    _raw = []
+    for item in raw:
+        if hasattr(item, "dict") or hasattr(item, "model_dump"):
+            _raw.append(recursive_serialize_or_str(item))
+        else:
+            _raw.append(str(item))
+    return _raw
+
+
 def post_process_raw(raw, artifact_type: str):
     if artifact_type == ArtifactType.STREAM.value:
         raw = ""
     elif artifact_type == ArtifactType.ARRAY.value:
-        _raw = []
-        for item in raw:
-            if hasattr(item, "dict") or hasattr(item, "model_dump"):
-                _raw.append(recursive_serialize_or_str(item))
-            else:
-                _raw.append(str(item))
-        raw = _raw
+        raw = raw.to_dict(orient="records") if isinstance(raw, DataFrame) else _to_list_of_dicts(raw)
     elif artifact_type == ArtifactType.UNKNOWN.value and raw is not None:
         if isinstance(raw, BaseModel | dict):
             try:

src/backend/base/langflow/schema/dataframe.py

@@ -96,3 +96,10 @@ class DataFrame(pandas_DataFrame):
             return DataFrame(*args, **kwargs).__finalize__(self)
 
         return _c
+
+    def __bool__(self):
+        """Truth value testing for the DataFrame.
+
+        Returns True if the DataFrame has at least one row, False otherwise.
+        """
+        return not self.empty