fix: change LoopComponent data input to DataFrame type and update display name (#8297)

* Update loop.py

* [autofix.ci] apply automated fixes

* Update loop-component.spec.ts

* change dataframe input name to inputs

* [autofix.ci] apply automated fixes

* 🐛 (typescript_test.yml): increase the maximum shard count to 40 to improve test distribution and efficiency

* 🔧 (typescript_test.yml): adjust optimal shard count calculation to use a maximum of 10 shards instead of 40 for better test distribution
🐛 (loop-component.spec.ts): fix getByTestId selector to match the updated element ID for testing purposes

---------

Co-authored-by: autofix-ci[bot] <114827586+autofix-ci[bot]@users.noreply.github.com>
Co-authored-by: Yuqi Tang <yuqi.tang@datastax.com>
Co-authored-by: cristhianzl <cristhian.lousa@gmail.com>

src/backend/base/langflow/components/logic/loop.py

@@ -14,9 +14,9 @@ class LoopComponent(Component):
     inputs = [
         HandleInput(
             name="data",
-            display_name="Data or DataFrame",
+            display_name="Inputs",
             info="The initial list of Data objects or DataFrame to iterate over.",
-            input_types=["Data", "DataFrame"],
+            input_types=["DataFrame"],
         ),
     ]
 

src/backend/base/langflow/initial_setup/starter_projects/Research Translation Loop.json

@@ -101,7 +101,6 @@
             "fieldName": "data",
             "id": "LoopComponent-9BK8B",
             "inputTypes": [
-              "Data",
               "DataFrame"
             ],
             "type": "other"
@@ -112,7 +111,7 @@
         "source": "ArXivComponent-ylKFb",
         "sourceHandle": "{œdataTypeœ: œArXivComponentœ, œidœ: œArXivComponent-ylKFbœ, œnameœ: œdataframeœ, œoutput_typesœ: [œDataFrameœ]}",
         "target": "LoopComponent-9BK8B",
-        "targetHandle": "{œfieldNameœ: œdataœ, œidœ: œLoopComponent-9BK8Bœ, œinputTypesœ: [œDataœ, œDataFrameœ], œtypeœ: œotherœ}"
+        "targetHandle": "{œfieldNameœ: œdataœ, œidœ: œLoopComponent-9BK8Bœ, œinputTypesœ: [œDataFrameœ], œtypeœ: œotherœ}"
       },
       {
         "animated": false,
@@ -1728,16 +1727,15 @@
                 "show": true,
                 "title_case": false,
                 "type": "code",
-                "value": "from langflow.custom import Component\nfrom langflow.io import HandleInput, Output\nfrom langflow.schema import Data\nfrom langflow.schema.dataframe import DataFrame\n\n\nclass LoopComponent(Component):\n    display_name = \"Loop\"\n    description = (\n        \"Iterates over a list of Data objects, outputting one item at a time and aggregating results from loop inputs.\"\n    )\n    icon = \"infinity\"\n\n    inputs = [\n        HandleInput(\n            name=\"data\",\n            display_name=\"Data or DataFrame\",\n            info=\"The initial list of Data objects or DataFrame to iterate over.\",\n            input_types=[\"Data\", \"DataFrame\"],\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Item\", name=\"item\", method=\"item_output\", allows_loop=True, group_outputs=True),\n        Output(display_name=\"Done\", name=\"done\", method=\"done_output\", group_outputs=True),\n    ]\n\n    def initialize_data(self) -> None:\n        \"\"\"Initialize the data list, context index, and aggregated list.\"\"\"\n        if self.ctx.get(f\"{self._id}_initialized\", False):\n            return\n\n        # Ensure data is a list of Data objects\n        data_list = self._validate_data(self.data)\n\n        # Store the initial data and context variables\n        self.update_ctx(\n            {\n                f\"{self._id}_data\": data_list,\n                f\"{self._id}_index\": 0,\n                f\"{self._id}_aggregated\": [],\n                f\"{self._id}_initialized\": True,\n            }\n        )\n\n    def _validate_data(self, data):\n        \"\"\"Validate and return a list of Data objects.\"\"\"\n        if isinstance(data, DataFrame):\n            return data.to_data_list()\n        if isinstance(data, Data):\n            return [data]\n        if isinstance(data, list) and all(isinstance(item, Data) for item in data):\n            return data\n        msg = \"The 'data' input must be a DataFrame, a list of Data objects, or a single Data object.\"\n        raise TypeError(msg)\n\n    def evaluate_stop_loop(self) -> bool:\n        \"\"\"Evaluate whether to stop item or done output.\"\"\"\n        current_index = self.ctx.get(f\"{self._id}_index\", 0)\n        data_length = len(self.ctx.get(f\"{self._id}_data\", []))\n        return current_index > data_length\n\n    def item_output(self) -> Data:\n        \"\"\"Output the next item in the list or stop if done.\"\"\"\n        self.initialize_data()\n        current_item = Data(text=\"\")\n\n        if self.evaluate_stop_loop():\n            self.stop(\"item\")\n        else:\n            # Get data list and current index\n            data_list, current_index = self.loop_variables()\n            if current_index < len(data_list):\n                # Output current item and increment index\n                try:\n                    current_item = data_list[current_index]\n                except IndexError:\n                    current_item = Data(text=\"\")\n            self.aggregated_output()\n            self.update_ctx({f\"{self._id}_index\": current_index + 1})\n\n        # Now we need to update the dependencies for the next run\n        self.update_dependency()\n        return current_item\n\n    def update_dependency(self):\n        item_dependency_id = self.get_incoming_edge_by_target_param(\"item\")\n\n        self.graph.run_manager.run_predecessors[self._id].append(item_dependency_id)\n\n    def done_output(self) -> DataFrame:\n        \"\"\"Trigger the done output when iteration is complete.\"\"\"\n        self.initialize_data()\n\n        if self.evaluate_stop_loop():\n            self.stop(\"item\")\n            self.start(\"done\")\n\n            aggregated = self.ctx.get(f\"{self._id}_aggregated\", [])\n\n            return DataFrame(aggregated)\n        self.stop(\"done\")\n        return DataFrame([])\n\n    def loop_variables(self):\n        \"\"\"Retrieve loop variables from context.\"\"\"\n        return (\n            self.ctx.get(f\"{self._id}_data\", []),\n            self.ctx.get(f\"{self._id}_index\", 0),\n        )\n\n    def aggregated_output(self) -> list[Data]:\n        \"\"\"Return the aggregated list once all items are processed.\"\"\"\n        self.initialize_data()\n\n        # Get data list and aggregated list\n        data_list = self.ctx.get(f\"{self._id}_data\", [])\n        aggregated = self.ctx.get(f\"{self._id}_aggregated\", [])\n        loop_input = self.item\n        if loop_input is not None and not isinstance(loop_input, str) and len(aggregated) <= len(data_list):\n            aggregated.append(loop_input)\n            self.update_ctx({f\"{self._id}_aggregated\": aggregated})\n        return aggregated\n"
+                "value": "from langflow.custom import Component\nfrom langflow.io import HandleInput, Output\nfrom langflow.schema import Data\nfrom langflow.schema.dataframe import DataFrame\n\n\nclass LoopComponent(Component):\n    display_name = \"Loop\"\n    description = (\n        \"Iterates over a list of Data objects, outputting one item at a time and aggregating results from loop inputs.\"\n    )\n    icon = \"infinity\"\n\n    inputs = [\n        HandleInput(\n            name=\"data\",\n            display_name=\"Inputs\",\n            info=\"The initial list of Data objects or DataFrame to iterate over.\",\n            input_types=[\"DataFrame\"],\n        ),\n    ]\n\n    outputs = [\n        Output(display_name=\"Item\", name=\"item\", method=\"item_output\", allows_loop=True, group_outputs=True),\n        Output(display_name=\"Done\", name=\"done\", method=\"done_output\", group_outputs=True),\n    ]\n\n    def initialize_data(self) -> None:\n        \"\"\"Initialize the data list, context index, and aggregated list.\"\"\"\n        if self.ctx.get(f\"{self._id}_initialized\", False):\n            return\n\n        # Ensure data is a list of Data objects\n        data_list = self._validate_data(self.data)\n\n        # Store the initial data and context variables\n        self.update_ctx(\n            {\n                f\"{self._id}_data\": data_list,\n                f\"{self._id}_index\": 0,\n                f\"{self._id}_aggregated\": [],\n                f\"{self._id}_initialized\": True,\n            }\n        )\n\n    def _validate_data(self, data):\n        \"\"\"Validate and return a list of Data objects.\"\"\"\n        if isinstance(data, DataFrame):\n            return data.to_data_list()\n        if isinstance(data, Data):\n            return [data]\n        if isinstance(data, list) and all(isinstance(item, Data) for item in data):\n            return data\n        msg = \"The 'data' input must be a DataFrame, a list of Data objects, or a single Data object.\"\n        raise TypeError(msg)\n\n    def evaluate_stop_loop(self) -> bool:\n        \"\"\"Evaluate whether to stop item or done output.\"\"\"\n        current_index = self.ctx.get(f\"{self._id}_index\", 0)\n        data_length = len(self.ctx.get(f\"{self._id}_data\", []))\n        return current_index > data_length\n\n    def item_output(self) -> Data:\n        \"\"\"Output the next item in the list or stop if done.\"\"\"\n        self.initialize_data()\n        current_item = Data(text=\"\")\n\n        if self.evaluate_stop_loop():\n            self.stop(\"item\")\n        else:\n            # Get data list and current index\n            data_list, current_index = self.loop_variables()\n            if current_index < len(data_list):\n                # Output current item and increment index\n                try:\n                    current_item = data_list[current_index]\n                except IndexError:\n                    current_item = Data(text=\"\")\n            self.aggregated_output()\n            self.update_ctx({f\"{self._id}_index\": current_index + 1})\n\n        # Now we need to update the dependencies for the next run\n        self.update_dependency()\n        return current_item\n\n    def update_dependency(self):\n        item_dependency_id = self.get_incoming_edge_by_target_param(\"item\")\n\n        self.graph.run_manager.run_predecessors[self._id].append(item_dependency_id)\n\n    def done_output(self) -> DataFrame:\n        \"\"\"Trigger the done output when iteration is complete.\"\"\"\n        self.initialize_data()\n\n        if self.evaluate_stop_loop():\n            self.stop(\"item\")\n            self.start(\"done\")\n\n            aggregated = self.ctx.get(f\"{self._id}_aggregated\", [])\n\n            return DataFrame(aggregated)\n        self.stop(\"done\")\n        return DataFrame([])\n\n    def loop_variables(self):\n        \"\"\"Retrieve loop variables from context.\"\"\"\n        return (\n            self.ctx.get(f\"{self._id}_data\", []),\n            self.ctx.get(f\"{self._id}_index\", 0),\n        )\n\n    def aggregated_output(self) -> list[Data]:\n        \"\"\"Return the aggregated list once all items are processed.\"\"\"\n        self.initialize_data()\n\n        # Get data list and aggregated list\n        data_list = self.ctx.get(f\"{self._id}_data\", [])\n        aggregated = self.ctx.get(f\"{self._id}_aggregated\", [])\n        loop_input = self.item\n        if loop_input is not None and not isinstance(loop_input, str) and len(aggregated) <= len(data_list):\n            aggregated.append(loop_input)\n            self.update_ctx({f\"{self._id}_aggregated\": aggregated})\n        return aggregated\n"
               },
               "data": {
                 "_input_type": "HandleInput",
                 "advanced": false,
-                "display_name": "Data or DataFrame",
+                "display_name": "Inputs",
                 "dynamic": false,
                 "info": "The initial list of Data objects or DataFrame to iterate over.",
                 "input_types": [
-                  "Data",
                   "DataFrame"
                 ],
                 "list": false,

src/frontend/tests/extended/features/loop-component.spec.ts

@@ -132,7 +132,7 @@ test(
       .first()
       .click();
     await page
-      .getByTestId("handle-loopcomponent-shownode-data or dataframe-left")
+      .getByTestId("handle-loopcomponent-shownode-inputs-left")
       .first()
       .click();