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"
    legacy = True

    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 datetime import datetime
from zoneinfo import ZoneInfo

from loguru import logger

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


class CurrentDateComponent(Component):
    display_name = "Current Date"
    description = "Returns the current date and time in the selected timezone."
    icon = "clock"
    beta = True
    name = "CurrentDate"

    inputs = [
        DropdownInput(
            name="timezone",
            display_name="Timezone",
            options=[
                "UTC",
                "US/Eastern",
                "US/Central",
                "US/Mountain",
                "US/Pacific",
                "Europe/London",
                "Europe/Paris",
                "Europe/Berlin",
                "Europe/Moscow",
                "Asia/Tokyo",
                "Asia/Shanghai",
                "Asia/Singapore",
                "Asia/Dubai",
                "Australia/Sydney",
                "Australia/Melbourne",
                "Pacific/Auckland",
                "America/Sao_Paulo",
                "America/Mexico_City",
                "America/Toronto",
                "America/Vancouver",
                "Africa/Cairo",
                "Africa/Johannesburg",
                "Atlantic/Reykjavik",
                "Indian/Maldives",
                "America/Bogota",
                "America/Lima",
                "America/Santiago",
                "America/Buenos_Aires",
                "America/Caracas",
                "America/La_Paz",
                "America/Montevideo",
                "America/Asuncion",
                "America/Cuiaba",
            ],
            value="UTC",
            info="Select the timezone for the current date and time.",
            tool_mode=True,
        ),
    ]
    outputs = [
        Output(display_name="Current Date", name="current_date", method="get_current_date"),
    ]

    def get_current_date(self) -> Message:
        try:
            tz = ZoneInfo(self.timezone)
            current_date = datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S %Z")
            result = f"Current date and time in {self.timezone}: {current_date}"
            self.status = result
            return Message(text=result)
        except Exception as e:  # noqa: BLE001
            logger.opt(exception=True).debug("Error getting current date")
            error_message = f"Error: {e}"
            self.status = error_message
            return Message(text=error_message)

# 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 = "code"
    name = "CustomComponent"

    inputs = [
        MessageTextInput(
            name="input_value",
            display_name="Input Value",
            info="This is a custom component Input",
            value="Hello, World!",
            tool_mode=True,
        ),
    ]

    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

404: Not Found

import uuid
from typing import Any

from typing_extensions import override

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"),
    ]

    @override
    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = 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 langchain.memory import ConversationBufferMemory

from langflow.custom import Component
from langflow.field_typing import BaseChatMemory
from langflow.helpers.data import data_to_text
from langflow.inputs import HandleInput
from langflow.io import DropdownInput, IntInput, MessageTextInput, MultilineInput, Output
from langflow.memory import LCBuiltinChatMemory, get_messages
from langflow.schema import Data
from langflow.schema.message import Message
from langflow.utils.constants import MESSAGE_SENDER_AI, MESSAGE_SENDER_USER


class MemoryComponent(Component):
    display_name = "Message History"
    description = "Retrieves stored chat messages from Langflow tables or an external memory."
    icon = "message-square-more"
    name = "Memory"

    inputs = [
        HandleInput(
            name="memory",
            display_name="External Memory",
            input_types=["BaseChatMessageHistory"],
            info="Retrieve messages from an external memory. If empty, it will use the Langflow tables.",
        ),
        DropdownInput(
            name="sender",
            display_name="Sender Type",
            options=[MESSAGE_SENDER_AI, MESSAGE_SENDER_USER, "Machine and User"],
            value="Machine and User",
            info="Filter by sender type.",
            advanced=True,
        ),
        MessageTextInput(
            name="sender_name",
            display_name="Sender Name",
            info="Filter by sender name.",
            advanced=True,
        ),
        IntInput(
            name="n_messages",
            display_name="Number of Messages",
            value=100,
            info="Number of messages to retrieve.",
            advanced=True,
        ),
        MessageTextInput(
            name="session_id",
            display_name="Session ID",
            info="The session ID of the chat. If empty, the current session ID parameter will be used.",
            advanced=True,
        ),
        DropdownInput(
            name="order",
            display_name="Order",
            options=["Ascending", "Descending"],
            value="Ascending",
            info="Order of the messages.",
            advanced=True,
        ),
        MultilineInput(
            name="template",
            display_name="Template",
            info="The template to use for formatting the data. "
            "It can contain the keys {text}, {sender} or any other key in the message data.",
            value="{sender_name}: {text}",
            advanced=True,
        ),
    ]

    outputs = [
        Output(display_name="Data", name="messages", method="retrieve_messages"),
        Output(display_name="Text", name="messages_text", method="retrieve_messages_as_text"),
    ]

    def retrieve_messages(self) -> Data:
        sender = self.sender
        sender_name = self.sender_name
        session_id = self.session_id
        n_messages = self.n_messages
        order = "DESC" if self.order == "Descending" else "ASC"

        if sender == "Machine and User":
            sender = None

        if self.memory:
            # override session_id
            self.memory.session_id = session_id

            stored = self.memory.messages
            # langchain memories are supposed to return messages in ascending order
            if order == "DESC":
                stored = stored[::-1]
            if n_messages:
                stored = stored[:n_messages]
            stored = [Message.from_lc_message(m) for m in stored]
            if sender:
                expected_type = MESSAGE_SENDER_AI if sender == MESSAGE_SENDER_AI else MESSAGE_SENDER_USER
                stored = [m for m in stored if m.type == expected_type]
        else:
            stored = get_messages(
                sender=sender,
                sender_name=sender_name,
                session_id=session_id,
                limit=n_messages,
                order=order,
            )
        self.status = stored
        return stored

    def retrieve_messages_as_text(self) -> Message:
        stored_text = data_to_text(self.template, self.retrieve_messages())
        self.status = stored_text
        return Message(text=stored_text)

    def build_lc_memory(self) -> BaseChatMemory:
        chat_memory = self.memory or LCBuiltinChatMemory(flow_id=self.flow_id, session_id=self.session_id)
        return ConversationBufferMemory(chat_memory=chat_memory)

404: Not Found

from langflow.custom import Component
from langflow.inputs import HandleInput, MessageInput
from langflow.inputs.inputs import MessageTextInput
from langflow.memory import get_messages, store_message
from langflow.schema.message import Message
from langflow.template import Output
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.",
        ),
        MessageTextInput(
            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,
        ),
        MessageTextInput(
            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,
        ),
        MessageTextInput(
            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="",
            advanced=True,
        ),
    ]

    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

from typing import cast

from pydantic import BaseModel, Field, create_model

from langflow.base.models.chat_result import get_chat_result
from langflow.custom import Component
from langflow.field_typing.constants import LanguageModel
from langflow.helpers.base_model import build_model_from_schema
from langflow.io import BoolInput, HandleInput, MessageTextInput, Output, StrInput, TableInput
from langflow.schema.data import Data


class StructuredOutputComponent(Component):
    display_name = "Structured Output"
    description = (
        "Transforms LLM responses into **structured data formats**. Ideal for extracting specific information "
        "or creating consistent outputs."
    )
    icon = "braces"

    inputs = [
        HandleInput(
            name="llm",
            display_name="Language Model",
            info="The language model to use to generate the structured output.",
            input_types=["LanguageModel"],
        ),
        MessageTextInput(name="input_value", display_name="Input message"),
        StrInput(
            name="schema_name",
            display_name="Schema Name",
            info="Provide a name for the output data schema.",
        ),
        TableInput(
            name="output_schema",
            display_name="Output Schema",
            info="Define the structure and data types for the model's output.",
            table_schema=[
                {
                    "name": "name",
                    "display_name": "Name",
                    "type": "str",
                    "description": "Specify the name of the output field.",
                },
                {
                    "name": "description",
                    "display_name": "Description",
                    "type": "str",
                    "description": "Describe the purpose of the output field.",
                },
                {
                    "name": "type",
                    "display_name": "Type",
                    "type": "str",
                    "description": (
                        "Indicate the data type of the output field " "(e.g., str, int, float, bool, list, dict)."
                    ),
                    "default": "text",
                },
                {
                    "name": "multiple",
                    "display_name": "Multiple",
                    "type": "boolean",
                    "description": "Set to True if this output field should be a list of the specified type.",
                    "default": "False",
                },
            ],
        ),
        BoolInput(
            name="multiple",
            display_name="Generate Multiple",
            info="Set to True if the model should generate a list of outputs instead of a single output.",
        ),
    ]

    outputs = [
        Output(name="structured_output", display_name="Structured Output", method="build_structured_output"),
    ]

    def build_structured_output(self) -> Data:
        if not hasattr(self.llm, "with_structured_output"):
            msg = "Language model does not support structured output."
            raise TypeError(msg)
        if not self.output_schema:
            msg = "Output schema cannot be empty"
            raise ValueError(msg)

        _output_model = build_model_from_schema(self.output_schema)
        if self.multiple:
            output_model = create_model(
                self.schema_name,
                objects=(list[_output_model], Field(description=f"A list of {self.schema_name}.")),  # type: ignore[valid-type]
            )
        else:
            output_model = _output_model
        try:
            llm_with_structured_output = cast(LanguageModel, self.llm).with_structured_output(schema=output_model)  # type: ignore[valid-type, attr-defined]

        except NotImplementedError as exc:
            msg = f"{self.llm.__class__.__name__} does not support structured output."
            raise TypeError(msg) from exc
        config_dict = {
            "run_name": self.display_name,
            "project_name": self.get_project_name(),
            "callbacks": self.get_langchain_callbacks(),
        }
        output = get_chat_result(runnable=llm_with_structured_output, input_value=self.input_value, config=config_dict)
        if isinstance(output, BaseModel):
            output_dict = output.model_dump()
        else:
            msg = f"Output should be a Pydantic BaseModel, got {type(output)} ({output})"
            raise TypeError(msg)
        return Data(data=output_dict)