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from langflow.custom import Component
from langflow.io import MessageTextInput, Output
from langflow.schema.message import Message


class CombineTextComponent(Component):
    display_name = "Combine Text"
    description = "Concatenate two text sources into a single text chunk using a specified delimiter."
    icon = "merge"
    name = "CombineText"

    inputs = [
        MessageTextInput(
            name="text1",
            display_name="First Text",
            info="The first text input to concatenate.",
        ),
        MessageTextInput(
            name="text2",
            display_name="Second Text",
            info="The second text input to concatenate.",
        ),
        MessageTextInput(
            name="delimiter",
            display_name="Delimiter",
            info="A string used to separate the two text inputs. Defaults to a whitespace.",
            value=" ",
        ),
    ]

    outputs = [
        Output(display_name="Combined Text", name="combined_text", method="combine_texts"),
    ]

    def combine_texts(self) -> Message:
        combined = self.delimiter.join([self.text1, self.text2])
        self.status = combined
        return Message(text=combined)

from langflow.custom import Component
from langflow.inputs import StrInput
from langflow.schema import Data
from langflow.template import Output


class CreateListComponent(Component):
    display_name = "Create List"
    description = "Creates a list of texts."
    icon = "list"
    name = "CreateList"

    inputs = [
        StrInput(
            name="texts",
            display_name="Texts",
            info="Enter one or more texts.",
            is_list=True,
        ),
    ]

    outputs = [
        Output(display_name="Data List", name="list", method="create_list"),
    ]

    def create_list(self) -> list[Data]:
        data = [Data(text=text) for text in self.texts]
        self.status = data
        return data

# from langflow.field_typing import Data
from langflow.custom import Component
from langflow.io import MessageTextInput, Output
from langflow.schema import Data


class CustomComponent(Component):
    display_name = "Custom Component"
    description = "Use as a template to create your own component."
    documentation: str = "http://docs.langflow.org/components/custom"
    icon = "custom_components"
    name = "CustomComponent"

    inputs = [
        MessageTextInput(name="input_value", display_name="Input Value", value="Hello, World!"),
    ]

    outputs = [
        Output(display_name="Output", name="output", method="build_output"),
    ]

    def build_output(self) -> Data:
        data = Data(value=self.input_value)
        self.status = data
        return data

from typing import List

from langflow.custom import Component
from langflow.io import DataInput, MessageTextInput, Output
from langflow.schema import Data


class FilterDataComponent(Component):
    display_name = "Filter Data"
    description = "Filters a Data object based on a list of keys."
    icon = "filter"
    beta = True
    name = "FilterData"

    inputs = [
        DataInput(
            name="data",
            display_name="Data",
            info="Data object to filter.",
        ),
        MessageTextInput(
            name="filter_criteria",
            display_name="Filter Criteria",
            info="List of keys to filter by.",
            is_list=True,
        ),
    ]

    outputs = [
        Output(display_name="Filtered Data", name="filtered_data", method="filter_data"),
    ]

    def filter_data(self) -> Data:
        filter_criteria: List[str] = self.filter_criteria
        data = self.data.data if isinstance(self.data, Data) else {}

        # Filter the data
        filtered = {key: value for key, value in data.items() if key in filter_criteria}

        # Create a new Data object with the filtered data
        filtered_data = Data(data=filtered)
        self.status = filtered_data
        return filtered_data

from langflow.base.agents.crewai.tasks import HierarchicalTask
from langflow.custom import Component
from langflow.io import HandleInput, MultilineInput, Output


class HierarchicalTaskComponent(Component):
    display_name: str = "Hierarchical Task"
    description: str = "Each task must have a description, an expected output and an agent responsible for execution."
    icon = "CrewAI"
    inputs = [
        MultilineInput(
            name="task_description",
            display_name="Description",
            info="Descriptive text detailing task's purpose and execution.",
        ),
        MultilineInput(
            name="expected_output",
            display_name="Expected Output",
            info="Clear definition of expected task outcome.",
        ),
        HandleInput(
            name="tools",
            display_name="Tools",
            input_types=["Tool"],
            is_list=True,
            info="List of tools/resources limited for task execution. Uses the Agent tools by default.",
            required=False,
            advanced=True,
        ),
    ]

    outputs = [
        Output(display_name="Task", name="task_output", method="build_task"),
    ]

    def build_task(self) -> HierarchicalTask:
        task = HierarchicalTask(
            description=self.task_description,
            expected_output=self.expected_output,
            tools=self.tools or [],
        )
        self.status = task
        return task

import uuid
from typing import Any, Optional

from langflow.custom import Component
from langflow.io import MessageTextInput, Output
from langflow.schema import dotdict
from langflow.schema.message import Message


class IDGeneratorComponent(Component):
    display_name = "ID Generator"
    description = "Generates a unique ID."
    icon = "fingerprint"
    name = "IDGenerator"

    inputs = [
        MessageTextInput(
            name="unique_id",
            display_name="Value",
            info="The generated unique ID.",
            refresh_button=True,
        ),
    ]

    outputs = [
        Output(display_name="ID", name="id", method="generate_id"),
    ]

    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: Optional[str] = None):
        if field_name == "unique_id":
            build_config[field_name]["value"] = str(uuid.uuid4())
        return build_config

    def generate_id(self) -> Message:
        unique_id = self.unique_id or str(uuid.uuid4())
        self.status = f"Generated ID: {unique_id}"
        return Message(text=unique_id)

from langflow.custom import CustomComponent
from langflow.schema import Data


class MergeDataComponent(CustomComponent):
    display_name = "Merge Data"
    description = "Combines multiple data sources into a single unified Data object."
    beta: bool = True
    name = "MergeData"

    field_config = {
        "data": {"display_name": "Data"},
    }

    def build(self, data: list[Data]) -> Data:
        if not data:
            return Data()
        if len(data) == 1:
            return data[0]
        merged_data = Data()
        for value in data:
            if merged_data is None:
                merged_data = value
            else:
                merged_data += value
        self.status = merged_data
        return merged_data

from langflow.custom import Component
from langflow.helpers.data import data_to_text
from langflow.io import DataInput, MultilineInput, Output, StrInput
from langflow.schema.message import Message


class ParseDataComponent(Component):
    display_name = "Parse Data"
    description = "Convert Data into plain text following a specified template."
    icon = "braces"
    name = "ParseData"

    inputs = [
        DataInput(name="data", display_name="Data", info="The data to convert to text."),
        MultilineInput(
            name="template",
            display_name="Template",
            info="The template to use for formatting the data. It can contain the keys {text}, {data} or any other key in the Data.",
            value="{text}",
        ),
        StrInput(name="sep", display_name="Separator", advanced=True, value="\n"),
    ]

    outputs = [
        Output(display_name="Text", name="text", method="parse_data"),
    ]

    def parse_data(self) -> Message:
        data = self.data if isinstance(self.data, list) else [self.data]
        template = self.template

        result_string = data_to_text(template, data, sep=self.sep)
        self.status = result_string
        return Message(text=result_string)

from langflow.base.agents.crewai.tasks import SequentialTask
from langflow.custom import Component
from langflow.io import BoolInput, HandleInput, MultilineInput, Output


class SequentialTaskComponent(Component):
    display_name: str = "Sequential Task"
    description: str = "Each task must have a description, an expected output and an agent responsible for execution."
    icon = "CrewAI"
    inputs = [
        MultilineInput(
            name="task_description",
            display_name="Description",
            info="Descriptive text detailing task's purpose and execution.",
        ),
        MultilineInput(
            name="expected_output",
            display_name="Expected Output",
            info="Clear definition of expected task outcome.",
        ),
        HandleInput(
            name="tools",
            display_name="Tools",
            input_types=["Tool"],
            is_list=True,
            info="List of tools/resources limited for task execution. Uses the Agent tools by default.",
            required=False,
            advanced=True,
        ),
        HandleInput(
            name="agent",
            display_name="Agent",
            input_types=["Agent"],
            info="CrewAI Agent that will perform the task",
            required=True,
        ),
        HandleInput(
            name="task",
            display_name="Task",
            input_types=["SequentialTask"],
            info="CrewAI Task that will perform the task",
        ),
        BoolInput(
            name="async_execution",
            display_name="Async Execution",
            value=True,
            advanced=True,
            info="Boolean flag indicating asynchronous task execution.",
        ),
    ]

    outputs = [
        Output(display_name="Task", name="task_output", method="build_task"),
    ]

    def build_task(self) -> list[SequentialTask]:
        tasks: list[SequentialTask] = []
        task = SequentialTask(
            description=self.task_description,
            expected_output=self.expected_output,
            tools=self.agent.tools,
            async_execution=False,
            agent=self.agent,
        )
        tasks.append(task)
        self.status = task
        if self.task:
            if isinstance(self.task, list) and all(isinstance(task, SequentialTask) for task in self.task):
                tasks = self.task + tasks
            elif isinstance(self.task, SequentialTask):
                tasks = [self.task] + tasks
        return tasks

from typing import List

from langchain_text_splitters import CharacterTextSplitter

from langflow.custom import Component
from langflow.io import HandleInput, IntInput, MessageTextInput, Output
from langflow.schema import Data
from langflow.utils.util import unescape_string


class SplitTextComponent(Component):
    display_name: str = "Split Text"
    description: str = "Split text into chunks based on specified criteria."
    icon = "scissors-line-dashed"
    name = "SplitText"

    inputs = [
        HandleInput(
            name="data_inputs",
            display_name="Data Inputs",
            info="The data to split.",
            input_types=["Data"],
            is_list=True,
        ),
        IntInput(
            name="chunk_overlap",
            display_name="Chunk Overlap",
            info="Number of characters to overlap between chunks.",
            value=200,
        ),
        IntInput(
            name="chunk_size",
            display_name="Chunk Size",
            info="The maximum number of characters in each chunk.",
            value=1000,
        ),
        MessageTextInput(
            name="separator",
            display_name="Separator",
            info="The character to split on. Defaults to newline.",
            value="\n",
        ),
    ]

    outputs = [
        Output(display_name="Chunks", name="chunks", method="split_text"),
    ]

    def _docs_to_data(self, docs):
        data = []
        for doc in docs:
            data.append(Data(text=doc.page_content, data=doc.metadata))
        return data

    def split_text(self) -> List[Data]:
        separator = unescape_string(self.separator)

        documents = []
        for _input in self.data_inputs:
            if isinstance(_input, Data):
                documents.append(_input.to_lc_document())

        splitter = CharacterTextSplitter(
            chunk_overlap=self.chunk_overlap,
            chunk_size=self.chunk_size,
            separator=separator,
        )
        docs = splitter.split_documents(documents)
        data = self._docs_to_data(docs)
        self.status = data
        return data

from langflow.custom import Component
from langflow.inputs import MessageInput, StrInput, HandleInput
from langflow.schema.message import Message
from langflow.template import Output
from langflow.memory import get_messages, store_message
from langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_NAME_AI


class StoreMessageComponent(Component):
    display_name = "Store Message"
    description = "Stores a chat message or text into Langflow tables or an external memory."
    icon = "save"
    name = "StoreMessage"

    inputs = [
        MessageInput(name="message", display_name="Message", info="The chat message to be stored.", required=True),
        HandleInput(
            name="memory",
            display_name="External Memory",
            input_types=["BaseChatMessageHistory"],
            info="The external memory to store the message. If empty, it will use the Langflow tables.",
        ),
        StrInput(
            name="sender",
            display_name="Sender",
            info="The sender of the message. Might be Machine or User. If empty, the current sender parameter will be used.",
            advanced=True,
        ),
        StrInput(
            name="sender_name",
            display_name="Sender Name",
            info="The name of the sender. Might be AI or User. If empty, the current sender parameter will be used.",
            advanced=True,
        ),
        StrInput(
            name="session_id",
            display_name="Session ID",
            info="The session ID of the chat. If empty, the current session ID parameter will be used.",
            value="",
        ),
    ]

    outputs = [
        Output(display_name="Stored Messages", name="stored_messages", method="store_message"),
    ]

    def store_message(self) -> Message:
        message = self.message

        message.session_id = self.session_id or message.session_id
        message.sender = self.sender or message.sender or MESSAGE_SENDER_AI
        message.sender_name = self.sender_name or message.sender_name or MESSAGE_SENDER_NAME_AI

        if self.memory:
            # override session_id
            self.memory.session_id = message.session_id
            lc_message = message.to_lc_message()
            self.memory.add_messages([lc_message])
            stored = self.memory.messages
            stored = [Message.from_lc_message(m) for m in stored]
            if message.sender:
                stored = [m for m in stored if m.sender == message.sender]
        else:
            store_message(message, flow_id=self.graph.flow_id)
            stored = get_messages(session_id=message.session_id, sender_name=message.sender_name, sender=message.sender)
        self.status = stored
        return stored