Data

import json
import re
import tempfile
from datetime import datetime, timezone
from pathlib import Path
from typing import Any
from urllib.parse import parse_qsl, urlencode, urlparse, urlunparse

import aiofiles
import aiofiles.os as aiofiles_os
import httpx
import validators

from langflow.base.curl.parse import parse_context
from langflow.custom.custom_component.component import Component
from langflow.inputs.inputs import TabInput
from langflow.io import (
    BoolInput,
    DataInput,
    DropdownInput,
    IntInput,
    MessageTextInput,
    MultilineInput,
    Output,
    TableInput,
)
from langflow.schema.data import Data
from langflow.schema.dotdict import dotdict
from langflow.services.deps import get_settings_service
from langflow.utils.component_utils import set_current_fields, set_field_advanced, set_field_display

# Define fields for each mode
MODE_FIELDS = {
    "URL": [
        "url_input",
        "method",
    ],
    "cURL": ["curl_input"],
}

# Fields that should always be visible
DEFAULT_FIELDS = ["mode"]


class APIRequestComponent(Component):
    display_name = "API Request"
    description = "Make HTTP requests using URL or cURL commands."
    documentation: str = "https://docs.langflow.org/components-data#api-request"
    icon = "Globe"
    name = "APIRequest"

    inputs = [
        MessageTextInput(
            name="url_input",
            display_name="URL",
            info="Enter the URL for the request.",
            advanced=False,
            tool_mode=True,
        ),
        MultilineInput(
            name="curl_input",
            display_name="cURL",
            info=(
                "Paste a curl command to populate the fields. "
                "This will fill in the dictionary fields for headers and body."
            ),
            real_time_refresh=True,
            tool_mode=True,
            advanced=True,
            show=False,
        ),
        DropdownInput(
            name="method",
            display_name="Method",
            options=["GET", "POST", "PATCH", "PUT", "DELETE"],
            value="GET",
            info="The HTTP method to use.",
            real_time_refresh=True,
        ),
        TabInput(
            name="mode",
            display_name="Mode",
            options=["URL", "cURL"],
            value="URL",
            info="Enable cURL mode to populate fields from a cURL command.",
            real_time_refresh=True,
        ),
        DataInput(
            name="query_params",
            display_name="Query Parameters",
            info="The query parameters to append to the URL.",
            advanced=True,
        ),
        TableInput(
            name="body",
            display_name="Body",
            info="The body to send with the request as a dictionary (for POST, PATCH, PUT).",
            table_schema=[
                {
                    "name": "key",
                    "display_name": "Key",
                    "type": "str",
                    "description": "Parameter name",
                },
                {
                    "name": "value",
                    "display_name": "Value",
                    "description": "Parameter value",
                },
            ],
            value=[],
            input_types=["Data"],
            advanced=True,
            real_time_refresh=True,
        ),
        TableInput(
            name="headers",
            display_name="Headers",
            info="The headers to send with the request",
            table_schema=[
                {
                    "name": "key",
                    "display_name": "Header",
                    "type": "str",
                    "description": "Header name",
                },
                {
                    "name": "value",
                    "display_name": "Value",
                    "type": "str",
                    "description": "Header value",
                },
            ],
            value=[{"key": "User-Agent", "value": get_settings_service().settings.user_agent}],
            advanced=True,
            input_types=["Data"],
            real_time_refresh=True,
        ),
        IntInput(
            name="timeout",
            display_name="Timeout",
            value=30,
            info="The timeout to use for the request.",
            advanced=True,
        ),
        BoolInput(
            name="follow_redirects",
            display_name="Follow Redirects",
            value=True,
            info="Whether to follow http redirects.",
            advanced=True,
        ),
        BoolInput(
            name="save_to_file",
            display_name="Save to File",
            value=False,
            info="Save the API response to a temporary file",
            advanced=True,
        ),
        BoolInput(
            name="include_httpx_metadata",
            display_name="Include HTTPx Metadata",
            value=False,
            info=(
                "Include properties such as headers, status_code, response_headers, "
                "and redirection_history in the output."
            ),
            advanced=True,
        ),
    ]

    outputs = [
        Output(display_name="API Response", name="data", method="make_api_request"),
    ]

    def _parse_json_value(self, value: Any) -> Any:
        """Parse a value that might be a JSON string."""
        if not isinstance(value, str):
            return value

        try:
            parsed = json.loads(value)
        except json.JSONDecodeError:
            return value
        else:
            return parsed

    def _process_body(self, body: Any) -> dict:
        """Process the body input into a valid dictionary."""
        if body is None:
            return {}
        if isinstance(body, dict):
            return self._process_dict_body(body)
        if isinstance(body, str):
            return self._process_string_body(body)
        if isinstance(body, list):
            return self._process_list_body(body)
        return {}

    def _process_dict_body(self, body: dict) -> dict:
        """Process dictionary body by parsing JSON values."""
        return {k: self._parse_json_value(v) for k, v in body.items()}

    def _process_string_body(self, body: str) -> dict:
        """Process string body by attempting JSON parse."""
        try:
            return self._process_body(json.loads(body))
        except json.JSONDecodeError:
            return {"data": body}

    def _process_list_body(self, body: list) -> dict:
        """Process list body by converting to key-value dictionary."""
        processed_dict = {}
        try:
            for item in body:
                if not self._is_valid_key_value_item(item):
                    continue
                key = item["key"]
                value = self._parse_json_value(item["value"])
                processed_dict[key] = value
        except (KeyError, TypeError, ValueError) as e:
            self.log(f"Failed to process body list: {e}")
            return {}
        return processed_dict

    def _is_valid_key_value_item(self, item: Any) -> bool:
        """Check if an item is a valid key-value dictionary."""
        return isinstance(item, dict) and "key" in item and "value" in item

    def parse_curl(self, curl: str, build_config: dotdict) -> dotdict:
        """Parse a cURL command and update build configuration."""
        try:
            parsed = parse_context(curl)

            # Update basic configuration
            url = parsed.url
            # Normalize URL before setting it
            url = self._normalize_url(url)

            build_config["url_input"]["value"] = url
            build_config["method"]["value"] = parsed.method.upper()

            # Process headers
            headers_list = [{"key": k, "value": v} for k, v in parsed.headers.items()]
            build_config["headers"]["value"] = headers_list

            # Process body data
            if not parsed.data:
                build_config["body"]["value"] = []
            elif parsed.data:
                try:
                    json_data = json.loads(parsed.data)
                    if isinstance(json_data, dict):
                        body_list = [
                            {"key": k, "value": json.dumps(v) if isinstance(v, dict | list) else str(v)}
                            for k, v in json_data.items()
                        ]
                        build_config["body"]["value"] = body_list
                    else:
                        build_config["body"]["value"] = [{"key": "data", "value": json.dumps(json_data)}]
                except json.JSONDecodeError:
                    build_config["body"]["value"] = [{"key": "data", "value": parsed.data}]

        except Exception as exc:
            msg = f"Error parsing curl: {exc}"
            self.log(msg)
            raise ValueError(msg) from exc

        return build_config

    def _normalize_url(self, url: str) -> str:
        """Normalize URL by adding https:// if no protocol is specified."""
        if not url or not isinstance(url, str):
            msg = "URL cannot be empty"
            raise ValueError(msg)

        url = url.strip()
        if url.startswith(("http://", "https://")):
            return url
        return f"https://{url}"

    async def make_request(
        self,
        client: httpx.AsyncClient,
        method: str,
        url: str,
        headers: dict | None = None,
        body: Any = None,
        timeout: int = 5,
        *,
        follow_redirects: bool = True,
        save_to_file: bool = False,
        include_httpx_metadata: bool = False,
    ) -> Data:
        method = method.upper()
        if method not in {"GET", "POST", "PATCH", "PUT", "DELETE"}:
            msg = f"Unsupported method: {method}"
            raise ValueError(msg)

        processed_body = self._process_body(body)
        redirection_history = []

        try:
            # Prepare request parameters
            request_params = {
                "method": method,
                "url": url,
                "headers": headers,
                "json": processed_body,
                "timeout": timeout,
                "follow_redirects": follow_redirects,
            }
            response = await client.request(**request_params)

            redirection_history = [
                {
                    "url": redirect.headers.get("Location", str(redirect.url)),
                    "status_code": redirect.status_code,
                }
                for redirect in response.history
            ]

            is_binary, file_path = await self._response_info(response, with_file_path=save_to_file)
            response_headers = self._headers_to_dict(response.headers)

            # Base metadata
            metadata = {
                "source": url,
                "status_code": response.status_code,
                "response_headers": response_headers,
            }

            if redirection_history:
                metadata["redirection_history"] = redirection_history

            if save_to_file:
                mode = "wb" if is_binary else "w"
                encoding = response.encoding if mode == "w" else None
                if file_path:
                    await aiofiles_os.makedirs(file_path.parent, exist_ok=True)
                    if is_binary:
                        async with aiofiles.open(file_path, "wb") as f:
                            await f.write(response.content)
                            await f.flush()
                    else:
                        async with aiofiles.open(file_path, "w", encoding=encoding) as f:
                            await f.write(response.text)
                            await f.flush()
                    metadata["file_path"] = str(file_path)

                if include_httpx_metadata:
                    metadata.update({"headers": headers})
                return Data(data=metadata)

            # Handle response content
            if is_binary:
                result = response.content
            else:
                try:
                    result = response.json()
                except json.JSONDecodeError:
                    self.log("Failed to decode JSON response")
                    result = response.text.encode("utf-8")

            metadata["result"] = result

            if include_httpx_metadata:
                metadata.update({"headers": headers})

            return Data(data=metadata)
        except (httpx.HTTPError, httpx.RequestError, httpx.TimeoutException) as exc:
            self.log(f"Error making request to {url}")
            return Data(
                data={
                    "source": url,
                    "headers": headers,
                    "status_code": 500,
                    "error": str(exc),
                    **({"redirection_history": redirection_history} if redirection_history else {}),
                },
            )

    def add_query_params(self, url: str, params: dict) -> str:
        """Add query parameters to URL efficiently."""
        if not params:
            return url
        url_parts = list(urlparse(url))
        query = dict(parse_qsl(url_parts[4]))
        query.update(params)
        url_parts[4] = urlencode(query)
        return urlunparse(url_parts)

    def _headers_to_dict(self, headers: httpx.Headers) -> dict[str, str]:
        """Convert HTTP headers to a dictionary with lowercased keys."""
        return {k.lower(): v for k, v in headers.items()}

    def _process_headers(self, headers: Any) -> dict:
        """Process the headers input into a valid dictionary."""
        if headers is None:
            return {}
        if isinstance(headers, dict):
            return headers
        if isinstance(headers, list):
            return {item["key"]: item["value"] for item in headers if self._is_valid_key_value_item(item)}
        return {}

    async def make_api_request(self) -> Data:
        """Make HTTP request with optimized parameter handling."""
        method = self.method
        url = self.url_input.strip() if isinstance(self.url_input, str) else ""
        headers = self.headers or {}
        body = self.body or {}
        timeout = self.timeout
        follow_redirects = self.follow_redirects
        save_to_file = self.save_to_file
        include_httpx_metadata = self.include_httpx_metadata

        # if self.mode == "cURL" and self.curl_input:
        #     self._build_config = self.parse_curl(self.curl_input, dotdict())
        #     # After parsing curl, get the normalized URL
        #     url = self._build_config["url_input"]["value"]

        # Normalize URL before validation
        url = self._normalize_url(url)

        # Validate URL
        if not validators.url(url):
            msg = f"Invalid URL provided: {url}"
            raise ValueError(msg)

        # Process query parameters
        if isinstance(self.query_params, str):
            query_params = dict(parse_qsl(self.query_params))
        else:
            query_params = self.query_params.data if self.query_params else {}

        # Process headers and body
        headers = self._process_headers(headers)
        body = self._process_body(body)
        url = self.add_query_params(url, query_params)

        async with httpx.AsyncClient() as client:
            result = await self.make_request(
                client,
                method,
                url,
                headers,
                body,
                timeout,
                follow_redirects=follow_redirects,
                save_to_file=save_to_file,
                include_httpx_metadata=include_httpx_metadata,
            )
        self.status = result
        return result

    def update_build_config(self, build_config: dotdict, field_value: Any, field_name: str | None = None) -> dotdict:
        """Update the build config based on the selected mode."""
        if field_name != "mode":
            if field_name == "curl_input" and self.mode == "cURL" and self.curl_input:
                return self.parse_curl(self.curl_input, build_config)
            return build_config

        # print(f"Current mode: {field_value}")
        if field_value == "cURL":
            set_field_display(build_config, "curl_input", value=True)
            if build_config["curl_input"]["value"]:
                build_config = self.parse_curl(build_config["curl_input"]["value"], build_config)
        else:
            set_field_display(build_config, "curl_input", value=False)

        return set_current_fields(
            build_config=build_config,
            action_fields=MODE_FIELDS,
            selected_action=field_value,
            default_fields=DEFAULT_FIELDS,
            func=set_field_advanced,
            default_value=True,
        )

    async def _response_info(
        self, response: httpx.Response, *, with_file_path: bool = False
    ) -> tuple[bool, Path | None]:
        """Determine the file path and whether the response content is binary.

        Args:
            response (Response): The HTTP response object.
            with_file_path (bool): Whether to save the response content to a file.

        Returns:
            Tuple[bool, Path | None]:
                A tuple containing a boolean indicating if the content is binary and the full file path (if applicable).
        """
        content_type = response.headers.get("Content-Type", "")
        is_binary = "application/octet-stream" in content_type or "application/binary" in content_type

        if not with_file_path:
            return is_binary, None

        component_temp_dir = Path(tempfile.gettempdir()) / self.__class__.__name__

        # Create directory asynchronously
        await aiofiles_os.makedirs(component_temp_dir, exist_ok=True)

        filename = None
        if "Content-Disposition" in response.headers:
            content_disposition = response.headers["Content-Disposition"]
            filename_match = re.search(r'filename="(.+?)"', content_disposition)
            if filename_match:
                extracted_filename = filename_match.group(1)
                filename = extracted_filename

        # Step 3: Infer file extension or use part of the request URL if no filename
        if not filename:
            # Extract the last segment of the URL path
            url_path = urlparse(str(response.request.url) if response.request else "").path
            base_name = Path(url_path).name  # Get the last segment of the path
            if not base_name:  # If the path ends with a slash or is empty
                base_name = "response"

            # Infer file extension
            content_type_to_extension = {
                "text/plain": ".txt",
                "application/json": ".json",
                "image/jpeg": ".jpg",
                "image/png": ".png",
                "application/octet-stream": ".bin",
            }
            extension = content_type_to_extension.get(content_type, ".bin" if is_binary else ".txt")
            filename = f"{base_name}{extension}"

        # Step 4: Define the full file path
        file_path = component_temp_dir / filename

        # Step 5: Check if file exists asynchronously and handle accordingly
        try:
            # Try to create the file exclusively (x mode) to check existence
            async with aiofiles.open(file_path, "x") as _:
                pass  # File created successfully, we can use this path
        except FileExistsError:
            # If file exists, append a timestamp to the filename
            timestamp = datetime.now(timezone.utc).strftime("%Y%m%d%H%M%S%f")
            file_path = component_temp_dir / f"{timestamp}-{filename}"

        return is_binary, file_path

from langflow.base.data.utils import TEXT_FILE_TYPES, parallel_load_data, parse_text_file_to_data, retrieve_file_paths
from langflow.custom.custom_component.component import Component
from langflow.io import BoolInput, IntInput, MessageTextInput, MultiselectInput
from langflow.schema.data import Data
from langflow.schema.dataframe import DataFrame
from langflow.template.field.base import Output


class DirectoryComponent(Component):
    display_name = "Directory"
    description = "Recursively load files from a directory."
    documentation: str = "https://docs.langflow.org/components-data#directory"
    icon = "folder"
    name = "Directory"

    inputs = [
        MessageTextInput(
            name="path",
            display_name="Path",
            info="Path to the directory to load files from. Defaults to current directory ('.')",
            value=".",
            tool_mode=True,
        ),
        MultiselectInput(
            name="types",
            display_name="File Types",
            info="File types to load. Select one or more types or leave empty to load all supported types.",
            options=TEXT_FILE_TYPES,
            value=[],
        ),
        IntInput(
            name="depth",
            display_name="Depth",
            info="Depth to search for files.",
            value=0,
        ),
        IntInput(
            name="max_concurrency",
            display_name="Max Concurrency",
            advanced=True,
            info="Maximum concurrency for loading files.",
            value=2,
        ),
        BoolInput(
            name="load_hidden",
            display_name="Load Hidden",
            advanced=True,
            info="If true, hidden files will be loaded.",
        ),
        BoolInput(
            name="recursive",
            display_name="Recursive",
            advanced=True,
            info="If true, the search will be recursive.",
        ),
        BoolInput(
            name="silent_errors",
            display_name="Silent Errors",
            advanced=True,
            info="If true, errors will not raise an exception.",
        ),
        BoolInput(
            name="use_multithreading",
            display_name="Use Multithreading",
            advanced=True,
            info="If true, multithreading will be used.",
        ),
    ]

    outputs = [
        Output(display_name="Loaded Files", name="dataframe", method="as_dataframe"),
    ]

    def load_directory(self) -> list[Data]:
        path = self.path
        types = self.types
        depth = self.depth
        max_concurrency = self.max_concurrency
        load_hidden = self.load_hidden
        recursive = self.recursive
        silent_errors = self.silent_errors
        use_multithreading = self.use_multithreading

        resolved_path = self.resolve_path(path)

        # If no types are specified, use all supported types
        if not types:
            types = TEXT_FILE_TYPES

        # Check if all specified types are valid
        invalid_types = [t for t in types if t not in TEXT_FILE_TYPES]
        if invalid_types:
            msg = f"Invalid file types specified: {invalid_types}. Valid types are: {TEXT_FILE_TYPES}"
            raise ValueError(msg)

        valid_types = types

        file_paths = retrieve_file_paths(
            resolved_path, load_hidden=load_hidden, recursive=recursive, depth=depth, types=valid_types
        )

        loaded_data = []
        if use_multithreading:
            loaded_data = parallel_load_data(file_paths, silent_errors=silent_errors, max_concurrency=max_concurrency)
        else:
            loaded_data = [parse_text_file_to_data(file_path, silent_errors=silent_errors) for file_path in file_paths]

        valid_data = [x for x in loaded_data if x is not None and isinstance(x, Data)]
        self.status = valid_data
        return valid_data

    def as_dataframe(self) -> DataFrame:
        return DataFrame(self.load_directory())

from copy import deepcopy
from typing import Any

from langflow.base.data.base_file import BaseFileComponent
from langflow.base.data.utils import TEXT_FILE_TYPES, parallel_load_data, parse_text_file_to_data
from langflow.io import BoolInput, FileInput, IntInput, Output
from langflow.schema.data import Data


class FileComponent(BaseFileComponent):
    """Handles loading and processing of individual or zipped text files.

    This component supports processing multiple valid files within a zip archive,
    resolving paths, validating file types, and optionally using multithreading for processing.
    """

    display_name = "File"
    description = "Loads content from one or more files."
    documentation: str = "https://docs.langflow.org/components-data#file"
    icon = "file-text"
    name = "File"

    VALID_EXTENSIONS = TEXT_FILE_TYPES

    _base_inputs = deepcopy(BaseFileComponent._base_inputs)

    for input_item in _base_inputs:
        if isinstance(input_item, FileInput) and input_item.name == "path":
            input_item.real_time_refresh = True
            break

    inputs = [
        *_base_inputs,
        BoolInput(
            name="use_multithreading",
            display_name="[Deprecated] Use Multithreading",
            advanced=True,
            value=True,
            info="Set 'Processing Concurrency' greater than 1 to enable multithreading.",
        ),
        IntInput(
            name="concurrency_multithreading",
            display_name="Processing Concurrency",
            advanced=True,
            info="When multiple files are being processed, the number of files to process concurrently.",
            value=1,
        ),
    ]

    outputs = [
        Output(display_name="Raw Content", name="message", method="load_files_message"),
    ]

    def update_outputs(self, frontend_node: dict, field_name: str, field_value: Any) -> dict:
        """Dynamically show only the relevant output based on the number of files processed."""
        if field_name == "path":
            # Add outputs based on the number of files in the path
            if len(field_value) == 0:
                return frontend_node

            frontend_node["outputs"] = []

            if len(field_value) == 1:
                # We need to check if the file is structured content
                file_path = frontend_node["template"]["path"]["file_path"][0]
                if file_path.endswith((".csv", ".xlsx", ".parquet")):
                    frontend_node["outputs"].append(
                        Output(display_name="Structured Content", name="dataframe", method="load_files_structured"),
                    )
                elif file_path.endswith(".json"):
                    frontend_node["outputs"].append(
                        Output(display_name="Structured Content", name="json", method="load_files_json"),
                    )

                # All files get the raw content and path outputs
                frontend_node["outputs"].append(
                    Output(display_name="Raw Content", name="message", method="load_files_message"),
                )
                frontend_node["outputs"].append(
                    Output(display_name="File Path", name="path", method="load_files_path"),
                )
            else:
                # For multiple files, we only show the files output
                frontend_node["outputs"].append(
                    Output(display_name="Files", name="dataframe", method="load_files"),
                )

        return frontend_node

    def process_files(self, file_list: list[BaseFileComponent.BaseFile]) -> list[BaseFileComponent.BaseFile]:
        """Processes files either sequentially or in parallel, depending on concurrency settings.

        Args:
            file_list (list[BaseFileComponent.BaseFile]): List of files to process.

        Returns:
            list[BaseFileComponent.BaseFile]: Updated list of files with merged data.
        """

        def process_file(file_path: str, *, silent_errors: bool = False) -> Data | None:
            """Processes a single file and returns its Data object."""
            try:
                return parse_text_file_to_data(file_path, silent_errors=silent_errors)
            except FileNotFoundError as e:
                msg = f"File not found: {file_path}. Error: {e}"
                self.log(msg)
                if not silent_errors:
                    raise
                return None
            except Exception as e:
                msg = f"Unexpected error processing {file_path}: {e}"
                self.log(msg)
                if not silent_errors:
                    raise
                return None

        if not file_list:
            msg = "No files to process."
            raise ValueError(msg)

        concurrency = 1 if not self.use_multithreading else max(1, self.concurrency_multithreading)
        file_count = len(file_list)

        parallel_processing_threshold = 2
        if concurrency < parallel_processing_threshold or file_count < parallel_processing_threshold:
            if file_count > 1:
                self.log(f"Processing {file_count} files sequentially.")
            processed_data = [process_file(str(file.path), silent_errors=self.silent_errors) for file in file_list]
        else:
            self.log(f"Starting parallel processing of {file_count} files with concurrency: {concurrency}.")
            file_paths = [str(file.path) for file in file_list]
            processed_data = parallel_load_data(
                file_paths,
                silent_errors=self.silent_errors,
                load_function=process_file,
                max_concurrency=concurrency,
            )

        # Use rollup_basefile_data to merge processed data with BaseFile objects
        return self.rollup_data(file_list, processed_data)

import base64
import json
import re
from collections.abc import Iterator
from json.decoder import JSONDecodeError
from typing import Any

from google.auth.exceptions import RefreshError
from google.oauth2.credentials import Credentials
from googleapiclient.discovery import build
from langchain_core.chat_sessions import ChatSession
from langchain_core.messages import HumanMessage
from langchain_google_community.gmail.loader import GMailLoader
from loguru import logger

from langflow.custom.custom_component.component import Component
from langflow.inputs.inputs import MessageTextInput
from langflow.io import SecretStrInput
from langflow.schema.data import Data
from langflow.template.field.base import Output


class GmailLoaderComponent(Component):
    display_name = "Gmail Loader"
    description = "Loads emails from Gmail using provided credentials."
    icon = "Google"
    legacy: bool = True

    inputs = [
        SecretStrInput(
            name="json_string",
            display_name="JSON String of the Service Account Token",
            info="JSON string containing OAuth 2.0 access token information for service account access",
            required=True,
            value="""{
                "account": "",
                "client_id": "",
                "client_secret": "",
                "expiry": "",
                "refresh_token": "",
                "scopes": [
                    "https://www.googleapis.com/auth/gmail.readonly",
                ],
                "token": "",
                "token_uri": "https://oauth2.googleapis.com/token",
                "universe_domain": "googleapis.com"
            }""",
        ),
        MessageTextInput(
            name="label_ids",
            display_name="Label IDs",
            info="Comma-separated list of label IDs to filter emails.",
            required=True,
            value="INBOX,SENT,UNREAD,IMPORTANT",
        ),
        MessageTextInput(
            name="max_results",
            display_name="Max Results",
            info="Maximum number of emails to load.",
            required=True,
            value="10",
        ),
    ]

    outputs = [
        Output(display_name="Data", name="data", method="load_emails"),
    ]

    def load_emails(self) -> Data:
        class CustomGMailLoader(GMailLoader):
            def __init__(
                self, creds: Any, *, n: int = 100, label_ids: list[str] | None = None, raise_error: bool = False
            ) -> None:
                super().__init__(creds, n, raise_error)
                self.label_ids = label_ids if label_ids is not None else ["SENT"]

            def clean_message_content(self, message):
                # Remove URLs
                message = re.sub(r"http\S+|www\S+|https\S+", "", message, flags=re.MULTILINE)

                # Remove email addresses
                message = re.sub(r"\S+@\S+", "", message)

                # Remove special characters and excessive whitespace
                message = re.sub(r"[^A-Za-z0-9\s]+", " ", message)
                message = re.sub(r"\s{2,}", " ", message)

                # Trim leading and trailing whitespace
                return message.strip()

            def _extract_email_content(self, msg: Any) -> HumanMessage:
                from_email = None
                for values in msg["payload"]["headers"]:
                    name = values["name"]
                    if name == "From":
                        from_email = values["value"]
                if from_email is None:
                    msg = "From email not found."
                    raise ValueError(msg)

                parts = msg["payload"]["parts"] if "parts" in msg["payload"] else [msg["payload"]]

                for part in parts:
                    if part["mimeType"] == "text/plain":
                        data = part["body"]["data"]
                        data = base64.urlsafe_b64decode(data).decode("utf-8")
                        pattern = re.compile(r"\r\nOn .+(\r\n)*wrote:\r\n")
                        newest_response = re.split(pattern, data)[0]
                        return HumanMessage(
                            content=self.clean_message_content(newest_response),
                            additional_kwargs={"sender": from_email},
                        )
                msg = "No plain text part found in the email."
                raise ValueError(msg)

            def _get_message_data(self, service: Any, message: Any) -> ChatSession:
                msg = service.users().messages().get(userId="me", id=message["id"]).execute()
                message_content = self._extract_email_content(msg)

                in_reply_to = None
                email_data = msg["payload"]["headers"]
                for values in email_data:
                    name = values["name"]
                    if name == "In-Reply-To":
                        in_reply_to = values["value"]

                thread_id = msg["threadId"]

                if in_reply_to:
                    thread = service.users().threads().get(userId="me", id=thread_id).execute()
                    messages = thread["messages"]

                    response_email = None
                    for _message in messages:
                        email_data = _message["payload"]["headers"]
                        for values in email_data:
                            if values["name"] == "Message-ID":
                                message_id = values["value"]
                                if message_id == in_reply_to:
                                    response_email = _message
                    if response_email is None:
                        msg = "Response email not found in the thread."
                        raise ValueError(msg)
                    starter_content = self._extract_email_content(response_email)
                    return ChatSession(messages=[starter_content, message_content])
                return ChatSession(messages=[message_content])

            def lazy_load(self) -> Iterator[ChatSession]:
                service = build("gmail", "v1", credentials=self.creds)
                results = (
                    service.users().messages().list(userId="me", labelIds=self.label_ids, maxResults=self.n).execute()
                )
                messages = results.get("messages", [])
                if not messages:
                    logger.warning("No messages found with the specified labels.")
                for message in messages:
                    try:
                        yield self._get_message_data(service, message)
                    except Exception:
                        if self.raise_error:
                            raise
                        else:
                            logger.exception(f"Error processing message {message['id']}")

        json_string = self.json_string
        label_ids = self.label_ids.split(",") if self.label_ids else ["INBOX"]
        max_results = int(self.max_results) if self.max_results else 100

        # Load the token information from the JSON string
        try:
            token_info = json.loads(json_string)
        except JSONDecodeError as e:
            msg = "Invalid JSON string"
            raise ValueError(msg) from e

        creds = Credentials.from_authorized_user_info(token_info)

        # Initialize the custom loader with the provided credentials
        loader = CustomGMailLoader(creds=creds, n=max_results, label_ids=label_ids)

        try:
            docs = loader.load()
        except RefreshError as e:
            msg = "Authentication error: Unable to refresh authentication token. Please try to reauthenticate."
            raise ValueError(msg) from e
        except Exception as e:
            msg = f"Error loading documents: {e}"
            raise ValueError(msg) from e

        # Return the loaded documents
        self.status = docs
        return Data(data={"text": docs})

import json
from json.decoder import JSONDecodeError

from google.auth.exceptions import RefreshError
from google.oauth2.credentials import Credentials
from langchain_google_community import GoogleDriveLoader

from langflow.custom.custom_component.component import Component
from langflow.helpers.data import docs_to_data
from langflow.inputs.inputs import MessageTextInput
from langflow.io import SecretStrInput
from langflow.schema.data import Data
from langflow.template.field.base import Output


class GoogleDriveComponent(Component):
    display_name = "Google Drive Loader"
    description = "Loads documents from Google Drive using provided credentials."
    icon = "Google"
    legacy: bool = True

    inputs = [
        SecretStrInput(
            name="json_string",
            display_name="JSON String of the Service Account Token",
            info="JSON string containing OAuth 2.0 access token information for service account access",
            required=True,
        ),
        MessageTextInput(
            name="document_id", display_name="Document ID", info="Single Google Drive document ID", required=True
        ),
    ]

    outputs = [
        Output(display_name="Loaded Documents", name="docs", method="load_documents"),
    ]

    def load_documents(self) -> Data:
        class CustomGoogleDriveLoader(GoogleDriveLoader):
            creds: Credentials | None = None
            """Credentials object to be passed directly."""

            def _load_credentials(self):
                """Load credentials from the provided creds attribute or fallback to the original method."""
                if self.creds:
                    return self.creds
                msg = "No credentials provided."
                raise ValueError(msg)

            class Config:
                arbitrary_types_allowed = True

        json_string = self.json_string

        document_ids = [self.document_id]
        if len(document_ids) != 1:
            msg = "Expected a single document ID"
            raise ValueError(msg)

        # TODO: Add validation to check if the document ID is valid

        # Load the token information from the JSON string
        try:
            token_info = json.loads(json_string)
        except JSONDecodeError as e:
            msg = "Invalid JSON string"
            raise ValueError(msg) from e

        # Initialize the custom loader with the provided credentials and document IDs
        loader = CustomGoogleDriveLoader(
            creds=Credentials.from_authorized_user_info(token_info), document_ids=document_ids
        )

        # Load the documents
        try:
            docs = loader.load()
        # catch google.auth.exceptions.RefreshError
        except RefreshError as e:
            msg = "Authentication error: Unable to refresh authentication token. Please try to reauthenticate."
            raise ValueError(msg) from e
        except Exception as e:
            msg = f"Error loading documents: {e}"
            raise ValueError(msg) from e

        if len(docs) != 1:
            msg = "Expected a single document to be loaded."
            raise ValueError(msg)

        data = docs_to_data(docs)
        # Return the loaded documents
        self.status = data
        return Data(data={"text": data})

import json

from google.oauth2.credentials import Credentials
from googleapiclient.discovery import build

from langflow.custom.custom_component.component import Component
from langflow.inputs.inputs import DropdownInput, MessageTextInput
from langflow.io import SecretStrInput
from langflow.schema.data import Data
from langflow.template.field.base import Output


class GoogleDriveSearchComponent(Component):
    display_name = "Google Drive Search"
    description = "Searches Google Drive files using provided credentials and query parameters."
    icon = "Google"
    legacy: bool = True

    inputs = [
        SecretStrInput(
            name="token_string",
            display_name="Token String",
            info="JSON string containing OAuth 2.0 access token information for service account access",
            required=True,
        ),
        DropdownInput(
            name="query_item",
            display_name="Query Item",
            options=[
                "name",
                "fullText",
                "mimeType",
                "modifiedTime",
                "viewedByMeTime",
                "trashed",
                "starred",
                "parents",
                "owners",
                "writers",
                "readers",
                "sharedWithMe",
                "createdTime",
                "properties",
                "appProperties",
                "visibility",
                "shortcutDetails.targetId",
            ],
            info="The field to query.",
            required=True,
        ),
        DropdownInput(
            name="valid_operator",
            display_name="Valid Operator",
            options=["contains", "=", "!=", "<=", "<", ">", ">=", "in", "has"],
            info="Operator to use in the query.",
            required=True,
        ),
        MessageTextInput(
            name="search_term",
            display_name="Search Term",
            info="The value to search for in the specified query item.",
            required=True,
        ),
        MessageTextInput(
            name="query_string",
            display_name="Query String",
            info="The query string used for searching. You can edit this manually.",
            value="",  # This will be updated with the generated query string
        ),
    ]

    outputs = [
        Output(display_name="Document URLs", name="doc_urls", method="search_doc_urls"),
        Output(display_name="Document IDs", name="doc_ids", method="search_doc_ids"),
        Output(display_name="Document Titles", name="doc_titles", method="search_doc_titles"),
        Output(display_name="Data", name="Data", method="search_data"),
    ]

    def generate_query_string(self) -> str:
        query_item = self.query_item
        valid_operator = self.valid_operator
        search_term = self.search_term

        # Construct the query string
        query = f"{query_item} {valid_operator} '{search_term}'"

        # Update the editable query string input with the generated query
        self.query_string = query

        return query

    def on_inputs_changed(self) -> None:
        # Automatically regenerate the query string when inputs change
        self.generate_query_string()

    def generate_file_url(self, file_id: str, mime_type: str) -> str:
        """Generates the appropriate Google Drive URL for a file based on its MIME type."""
        return {
            "application/vnd.google-apps.document": f"https://docs.google.com/document/d/{file_id}/edit",
            "application/vnd.google-apps.spreadsheet": f"https://docs.google.com/spreadsheets/d/{file_id}/edit",
            "application/vnd.google-apps.presentation": f"https://docs.google.com/presentation/d/{file_id}/edit",
            "application/vnd.google-apps.drawing": f"https://docs.google.com/drawings/d/{file_id}/edit",
            "application/pdf": f"https://drive.google.com/file/d/{file_id}/view?usp=drivesdk",
        }.get(mime_type, f"https://drive.google.com/file/d/{file_id}/view?usp=drivesdk")

    def search_files(self) -> dict:
        # Load the token information from the JSON string
        token_info = json.loads(self.token_string)
        creds = Credentials.from_authorized_user_info(token_info)

        # Use the query string from the input (which might have been edited by the user)
        query = self.query_string or self.generate_query_string()

        # Initialize the Google Drive API service
        service = build("drive", "v3", credentials=creds)

        # Perform the search
        results = service.files().list(q=query, pageSize=5, fields="nextPageToken, files(id, name, mimeType)").execute()
        items = results.get("files", [])

        doc_urls = []
        doc_ids = []
        doc_titles_urls = []
        doc_titles = []

        if items:
            for item in items:
                # Directly use the file ID, title, and MIME type to generate the URL
                file_id = item["id"]
                file_title = item["name"]
                mime_type = item["mimeType"]
                file_url = self.generate_file_url(file_id, mime_type)

                # Store the URL, ID, and title+URL in their respective lists
                doc_urls.append(file_url)
                doc_ids.append(file_id)
                doc_titles.append(file_title)
                doc_titles_urls.append({"title": file_title, "url": file_url})

        return {"doc_urls": doc_urls, "doc_ids": doc_ids, "doc_titles_urls": doc_titles_urls, "doc_titles": doc_titles}

    def search_doc_ids(self) -> list[str]:
        return self.search_files()["doc_ids"]

    def search_doc_urls(self) -> list[str]:
        return self.search_files()["doc_urls"]

    def search_doc_titles(self) -> list[str]:
        return self.search_files()["doc_titles"]

    def search_data(self) -> Data:
        return Data(data={"text": self.search_files()["doc_titles_urls"]})

from typing import TYPE_CHECKING, Any

from langchain_community.utilities import SQLDatabase
from sqlalchemy.exc import SQLAlchemyError

from langflow.custom.custom_component.component_with_cache import ComponentWithCache
from langflow.io import BoolInput, MessageTextInput, MultilineInput, Output
from langflow.schema.dataframe import DataFrame
from langflow.schema.message import Message
from langflow.services.cache.utils import CacheMiss

if TYPE_CHECKING:
    from sqlalchemy.engine import Result


class SQLComponent(ComponentWithCache):
    """A sql component."""

    display_name = "SQL Database"
    description = "Executes SQL queries on SQLAlchemy-compatible databases."
    documentation: str = "https://docs.langflow.org/components-data#sql-database"
    icon = "database"
    name = "SQLComponent"
    metadata = {"keywords": ["sql", "database", "query", "db", "fetch"]}

    def __init__(self, **kwargs) -> None:
        super().__init__(**kwargs)
        self.db: SQLDatabase = None

    def maybe_create_db(self):
        if self.database_url != "":
            cached_db = self._shared_component_cache.get(self.database_url)
            if not isinstance(cached_db, CacheMiss):
                self.db = cached_db
                return
            self.log("Connecting to database")
            try:
                self.db = SQLDatabase.from_uri(self.database_url)
            except Exception as e:
                msg = f"An error occurred while connecting to the database: {e}"
                raise ValueError(msg) from e
            self._shared_component_cache.set(self.database_url, self.db)

    inputs = [
        MessageTextInput(name="database_url", display_name="Database URL", required=True),
        MultilineInput(name="query", display_name="SQL Query", tool_mode=True, required=True),
        BoolInput(name="include_columns", display_name="Include Columns", value=True, tool_mode=True, advanced=True),
        BoolInput(
            name="add_error",
            display_name="Add Error",
            value=False,
            tool_mode=True,
            info="If True, the error will be added to the result",
            advanced=True,
        ),
    ]

    outputs = [
        Output(display_name="Result Table", name="run_sql_query", method="run_sql_query"),
    ]

    def build_component(
        self,
    ) -> Message:
        error = None
        self.maybe_create_db()
        try:
            result = self.db.run(self.query, include_columns=self.include_columns)
            self.status = result
        except SQLAlchemyError as e:
            msg = f"An error occurred while running the SQL Query: {e}"
            self.log(msg)
            result = str(e)
            self.status = result
            error = repr(e)

        if self.add_error and error is not None:
            result = f"{result}\n\nError: {error}\n\nQuery: {self.query}"
        elif error is not None:
            # Then we won't add the error to the result
            result = self.query

        return Message(text=result)

    def __execute_query(self) -> list[dict[str, Any]]:
        self.maybe_create_db()
        try:
            cursor: Result[Any] = self.db.run(self.query, fetch="cursor")
            return [x._asdict() for x in cursor.fetchall()]
        except SQLAlchemyError as e:
            msg = f"An error occurred while running the SQL Query: {e}"
            self.log(msg)
            raise ValueError(msg) from e

    def run_sql_query(self) -> DataFrame:
        result = self.__execute_query()
        df_result = DataFrame(result)
        self.status = df_result
        return df_result

import re

import requests
from bs4 import BeautifulSoup
from langchain_community.document_loaders import RecursiveUrlLoader
from loguru import logger

from langflow.custom.custom_component.component import Component
from langflow.field_typing.range_spec import RangeSpec
from langflow.helpers.data import safe_convert
from langflow.io import BoolInput, DropdownInput, IntInput, MessageTextInput, Output, SliderInput, TableInput
from langflow.schema.dataframe import DataFrame
from langflow.schema.message import Message
from langflow.services.deps import get_settings_service

# Constants
DEFAULT_TIMEOUT = 30
DEFAULT_MAX_DEPTH = 1
DEFAULT_FORMAT = "Text"
URL_REGEX = re.compile(
    r"^(https?:\/\/)?" r"(www\.)?" r"([a-zA-Z0-9.-]+)" r"(\.[a-zA-Z]{2,})?" r"(:\d+)?" r"(\/[^\s]*)?$",
    re.IGNORECASE,
)


class URLComponent(Component):
    """A component that loads and parses content from web pages recursively.

    This component allows fetching content from one or more URLs, with options to:
    - Control crawl depth
    - Prevent crawling outside the root domain
    - Use async loading for better performance
    - Extract either raw HTML or clean text
    - Configure request headers and timeouts
    """

    display_name = "URL"
    description = "Fetch content from one or more web pages, following links recursively."
    documentation: str = "https://docs.langflow.org/components-data#url"
    icon = "layout-template"
    name = "URLComponent"

    inputs = [
        MessageTextInput(
            name="urls",
            display_name="URLs",
            info="Enter one or more URLs to crawl recursively, by clicking the '+' button.",
            is_list=True,
            tool_mode=True,
            placeholder="Enter a URL...",
            list_add_label="Add URL",
            input_types=[],
        ),
        SliderInput(
            name="max_depth",
            display_name="Depth",
            info=(
                "Controls how many 'clicks' away from the initial page the crawler will go:\n"
                "- depth 1: only the initial page\n"
                "- depth 2: initial page + all pages linked directly from it\n"
                "- depth 3: initial page + direct links + links found on those direct link pages\n"
                "Note: This is about link traversal, not URL path depth."
            ),
            value=DEFAULT_MAX_DEPTH,
            range_spec=RangeSpec(min=1, max=5, step=1),
            required=False,
            min_label=" ",
            max_label=" ",
            min_label_icon="None",
            max_label_icon="None",
            # slider_input=True
        ),
        BoolInput(
            name="prevent_outside",
            display_name="Prevent Outside",
            info=(
                "If enabled, only crawls URLs within the same domain as the root URL. "
                "This helps prevent the crawler from going to external websites."
            ),
            value=True,
            required=False,
            advanced=True,
        ),
        BoolInput(
            name="use_async",
            display_name="Use Async",
            info=(
                "If enabled, uses asynchronous loading which can be significantly faster "
                "but might use more system resources."
            ),
            value=True,
            required=False,
            advanced=True,
        ),
        DropdownInput(
            name="format",
            display_name="Output Format",
            info="Output Format. Use 'Text' to extract the text from the HTML or 'HTML' for the raw HTML content.",
            options=["Text", "HTML"],
            value=DEFAULT_FORMAT,
            advanced=True,
        ),
        IntInput(
            name="timeout",
            display_name="Timeout",
            info="Timeout for the request in seconds.",
            value=DEFAULT_TIMEOUT,
            required=False,
            advanced=True,
        ),
        TableInput(
            name="headers",
            display_name="Headers",
            info="The headers to send with the request",
            table_schema=[
                {
                    "name": "key",
                    "display_name": "Header",
                    "type": "str",
                    "description": "Header name",
                },
                {
                    "name": "value",
                    "display_name": "Value",
                    "type": "str",
                    "description": "Header value",
                },
            ],
            value=[{"key": "User-Agent", "value": get_settings_service().settings.user_agent}],
            advanced=True,
            input_types=["DataFrame"],
        ),
        BoolInput(
            name="filter_text_html",
            display_name="Filter Text/HTML",
            info="If enabled, filters out text/css content type from the results.",
            value=True,
            required=False,
            advanced=True,
        ),
        BoolInput(
            name="continue_on_failure",
            display_name="Continue on Failure",
            info="If enabled, continues crawling even if some requests fail.",
            value=True,
            required=False,
            advanced=True,
        ),
        BoolInput(
            name="check_response_status",
            display_name="Check Response Status",
            info="If enabled, checks the response status of the request.",
            value=False,
            required=False,
            advanced=True,
        ),
        BoolInput(
            name="autoset_encoding",
            display_name="Autoset Encoding",
            info="If enabled, automatically sets the encoding of the request.",
            value=True,
            required=False,
            advanced=True,
        ),
    ]

    outputs = [
        Output(display_name="Extracted Pages", name="page_results", method="fetch_content"),
        Output(display_name="Raw Content", name="raw_results", method="fetch_content_as_message", tool_mode=False),
    ]

    @staticmethod
    def validate_url(url: str) -> bool:
        """Validates if the given string matches URL pattern.

        Args:
            url: The URL string to validate

        Returns:
            bool: True if the URL is valid, False otherwise
        """
        return bool(URL_REGEX.match(url))

    def ensure_url(self, url: str) -> str:
        """Ensures the given string is a valid URL.

        Args:
            url: The URL string to validate and normalize

        Returns:
            str: The normalized URL

        Raises:
            ValueError: If the URL is invalid
        """
        url = url.strip()
        if not url.startswith(("http://", "https://")):
            url = "https://" + url

        if not self.validate_url(url):
            msg = f"Invalid URL: {url}"
            raise ValueError(msg)

        return url

    def _create_loader(self, url: str) -> RecursiveUrlLoader:
        """Creates a RecursiveUrlLoader instance with the configured settings.

        Args:
            url: The URL to load

        Returns:
            RecursiveUrlLoader: Configured loader instance
        """
        headers_dict = {header["key"]: header["value"] for header in self.headers}
        extractor = (lambda x: x) if self.format == "HTML" else (lambda x: BeautifulSoup(x, "lxml").get_text())

        return RecursiveUrlLoader(
            url=url,
            max_depth=self.max_depth,
            prevent_outside=self.prevent_outside,
            use_async=self.use_async,
            extractor=extractor,
            timeout=self.timeout,
            headers=headers_dict,
            check_response_status=self.check_response_status,
            continue_on_failure=self.continue_on_failure,
            base_url=url,  # Add base_url to ensure consistent domain crawling
            autoset_encoding=self.autoset_encoding,  # Enable automatic encoding detection
            exclude_dirs=[],  # Allow customization of excluded directories
            link_regex=None,  # Allow customization of link filtering
        )

    def fetch_url_contents(self) -> list[dict]:
        """Load documents from the configured URLs.

        Returns:
            List[Data]: List of Data objects containing the fetched content

        Raises:
            ValueError: If no valid URLs are provided or if there's an error loading documents
        """
        try:
            urls = list({self.ensure_url(url) for url in self.urls if url.strip()})
            logger.debug(f"URLs: {urls}")
            if not urls:
                msg = "No valid URLs provided."
                raise ValueError(msg)

            all_docs = []
            for url in urls:
                logger.debug(f"Loading documents from {url}")

                try:
                    loader = self._create_loader(url)
                    docs = loader.load()

                    if not docs:
                        logger.warning(f"No documents found for {url}")
                        continue

                    logger.debug(f"Found {len(docs)} documents from {url}")
                    all_docs.extend(docs)

                except requests.exceptions.RequestException as e:
                    logger.exception(f"Error loading documents from {url}: {e}")
                    continue

            if not all_docs:
                msg = "No documents were successfully loaded from any URL"
                raise ValueError(msg)

            # data = [Data(text=doc.page_content, **doc.metadata) for doc in all_docs]
            data = [
                {
                    "text": safe_convert(doc.page_content, clean_data=True),
                    "url": doc.metadata.get("source", ""),
                    "title": doc.metadata.get("title", ""),
                    "description": doc.metadata.get("description", ""),
                    "content_type": doc.metadata.get("content_type", ""),
                    "language": doc.metadata.get("language", ""),
                }
                for doc in all_docs
            ]
        except Exception as e:
            error_msg = e.message if hasattr(e, "message") else e
            msg = f"Error loading documents: {error_msg!s}"
            logger.exception(msg)
            raise ValueError(msg) from e
        return data

    def fetch_content(self) -> DataFrame:
        """Convert the documents to a DataFrame."""
        return DataFrame(data=self.fetch_url_contents())

    def fetch_content_as_message(self) -> Message:
        """Convert the documents to a Message."""
        url_contents = self.fetch_url_contents()
        return Message(text="\n\n".join([x["text"] for x in url_contents]), data={"data": url_contents})

import json

from langflow.custom.custom_component.component import Component
from langflow.io import MultilineInput, Output
from langflow.schema.data import Data


class WebhookComponent(Component):
    display_name = "Webhook"
    documentation: str = "https://docs.langflow.org/components-data#webhook"
    name = "Webhook"
    icon = "webhook"

    inputs = [
        MultilineInput(
            name="data",
            display_name="Payload",
            info="Receives a payload from external systems via HTTP POST.",
            advanced=True,
        ),
        MultilineInput(
            name="curl",
            display_name="cURL",
            value="CURL_WEBHOOK",
            advanced=True,
            input_types=[],
        ),
        MultilineInput(
            name="endpoint",
            display_name="Endpoint",
            value="BACKEND_URL",
            advanced=False,
            copy_field=True,
            input_types=[],
        ),
    ]
    outputs = [
        Output(display_name="Data", name="output_data", method="build_data"),
    ]

    def build_data(self) -> Data:
        message: str | Data = ""
        if not self.data:
            self.status = "No data provided."
            return Data(data={})
        try:
            my_data = self.data.replace('"\n"', '"\\n"')
            body = json.loads(my_data or "{}")
        except json.JSONDecodeError:
            body = {"payload": self.data}
            message = f"Invalid JSON payload. Please check the format.\n\n{self.data}"
        data = Data(data=body)
        if not message:
            message = data
        self.status = message
        return data