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import asyncio
import codecs
import io
import random
import re
import time
from asyncio import Task
from pathlib import Path
from typing import Any, AsyncGenerator, Optional

import orjson as json
from curl_cffi.requests import AsyncSession, Cookies, Response
from curl_cffi.requests.exceptions import ReadTimeout

from .components import GemMixin
from .constants import (
    Endpoint,
    ErrorCode,
    GRPC,
    Model,
    TEMPORARY_CHAT_FLAG_INDEX,
    STREAMING_FLAG_INDEX,
    GEM_FLAG_INDEX,
)
from .exceptions import (
    APIError,
    AuthError,
    GeminiError,
    ModelInvalid,
    TemporarilyBlocked,
    TimeoutError,
    UsageLimitExceeded,
)
from .types import (
    Candidate,
    Gem,
    GeneratedImage,
    ModelOutput,
    RPCData,
    WebImage,
    AvailableModel,
    ChatInfo,
    ChatTurn,
    ChatHistory,
    GeneratedVideo,
)
from .utils import (
    extract_json_from_response,
    get_access_token,
    get_delta_by_fp_len,
    get_nested_value,
    logger,
    parse_file_name,
    parse_response_by_frame,
    rotate_1psidts,
    running,
    upload_file,
)

_CARD_CONTENT_RE = re.compile(r"^http://googleusercontent\.com/card_content/\d+")
_ARTIFACTS_RE = re.compile(r"http://googleusercontent\.com/\w+/\d+\n*")
_DEFAULT_METADATA: list[Any] = ["", "", "", None, None, None, None, None, None, ""]


class GeminiClient(GemMixin):
    """

    Async requests client interface for gemini.google.com.



    `secure_1psid` must be provided unless the optional dependency `browser-cookie3` is installed, and

    you have logged in to google.com in your local browser.



    Parameters

    ----------

    secure_1psid: `str`, optional

        __Secure-1PSID cookie value.

    secure_1psidts: `str`, optional

        __Secure-1PSIDTS cookie value, some Google accounts don't require this value, provide only if it's in the cookie list.

    proxy: `str`, optional

        Proxy URL.

    kwargs: `dict`, optional

        Additional arguments which will be passed to the http client.

        Refer to `curl_cffi.requests.AsyncSession` for more information.



    Raises

    ------

    `ValueError`

        If `browser-cookie3` is installed but cookies for google.com are not found in your local browser storage.

    """

    __slots__ = [
        "_cookies",
        "proxy",
        "_running",
        "client",
        "access_token",
        "build_label",
        "session_id",
        "timeout",
        "auto_close",
        "close_delay",
        "close_task",
        "auto_refresh",
        "refresh_interval",
        "refresh_task",
        "verbose",
        "watchdog_timeout",
        "_lock",
        "_reqid",
        "_gems",  # From GemMixin
        "_available_models",
        "_recent_chats",
        "kwargs",
    ]

    def __init__(

        self,

        secure_1psid: str | None = None,

        secure_1psidts: str | None = None,

        proxy: str | None = None,

        **kwargs,

    ):
        super().__init__()
        self._cookies = Cookies()
        self.proxy = proxy
        self._running: bool = False
        self.client: AsyncSession | None = None
        self.access_token: str | None = None
        self.build_label: str | None = None
        self.session_id: str | None = None
        self.timeout: float = 600
        self.auto_close: bool = False
        self.close_delay: float = 600
        self.close_task: Task | None = None
        self.auto_refresh: bool = True
        self.refresh_interval: float = 600
        self.refresh_task: Task | None = None
        self.verbose: bool = True
        self.watchdog_timeout: float = 90
        self._lock = asyncio.Lock()
        self._reqid: int = random.randint(10000, 99999)

        self._available_models: list[AvailableModel] | None = None
        self._recent_chats: list[ChatInfo] | None = None
        self.kwargs = kwargs

        if secure_1psid:
            self._cookies.set("__Secure-1PSID", secure_1psid, domain=".google.com")
            if secure_1psidts:
                self._cookies.set(
                    "__Secure-1PSIDTS", secure_1psidts, domain=".google.com"
                )

    @property
    def cookies(self) -> Cookies:
        """

        Returns the cookies used for the current session.

        """
        return self.client.cookies if self.client else self._cookies

    @cookies.setter
    def cookies(self, value: Cookies | dict):
        if isinstance(value, Cookies):
            self._cookies.update(value)
        elif isinstance(value, dict):
            for k, v in value.items():
                self._cookies.set(k, v, domain=".google.com")

        if self.client:
            self.client.cookies.update(self._cookies)

    async def init(

        self,

        timeout: float = 600,

        auto_close: bool = False,

        close_delay: float = 600,

        auto_refresh: bool = True,

        refresh_interval: float = 600,

        verbose: bool = True,

        watchdog_timeout: float = 90,

    ) -> None:
        """

        Get SNlM0e value as access token. Without this token posting will fail with 400 bad request.



        Parameters

        ----------

        timeout: `float`, optional

            Request timeout of the client in seconds. Used to limit the max waiting time when sending a request.

        auto_close: `bool`, optional

            If `True`, the client will close connections and clear resource usage after a certain period

            of inactivity. Useful for always-on services.

        close_delay: `float`, optional

            Time to wait before auto-closing the client in seconds. Effective only if `auto_close` is `True`.

        auto_refresh: `bool`, optional

            If `True`, will schedule a task to automatically refresh cookies and access token in the background.

        refresh_interval: `float`, optional

            Time interval for background cookie and access token refresh in seconds.

            Effective only if `auto_refresh` is `True`.

        verbose: `bool`, optional

            If `True`, will print more infomation in logs.

        watchdog_timeout: `float`, optional

            Timeout in seconds for shadow retry watchdog. If no data receives from stream but connection is active,

            client will retry automatically after this duration.

        """

        async with self._lock:
            if self._running:
                return

            try:
                self.verbose = verbose
                self.watchdog_timeout = watchdog_timeout
                access_token, build_label, session_id, session = await get_access_token(
                    base_cookies=self.cookies,
                    proxy=self.proxy,
                    verbose=self.verbose,
                    verify=self.kwargs.get("verify", True),
                )

                session.timeout = timeout
                self.client = session
                self._cookies.update(self.client.cookies)
                self.access_token = access_token
                self.build_label = build_label
                self.session_id = session_id
                self._running = True
                self._reqid = random.randint(10000, 99999)

                self.timeout = timeout
                self.auto_close = auto_close
                self.close_delay = close_delay
                if self.auto_close:
                    await self.reset_close_task()

                self.auto_refresh = auto_refresh
                self.refresh_interval = refresh_interval

                if self.refresh_task:
                    self.refresh_task.cancel()
                    self.refresh_task = None

                if self.auto_refresh:
                    self.refresh_task = asyncio.create_task(self.start_auto_refresh())

                await self._init_rpc()

                if self.verbose:
                    logger.success("Gemini client initialized successfully.")
            except Exception:
                await self.close()
                raise

    async def close(self, delay: float = 0) -> None:
        """

        Close the client after a certain period of inactivity, or call manually to close immediately.



        Parameters

        ----------

        delay: `float`, optional

            Time to wait before closing the client in seconds.

        """

        if delay:
            await asyncio.sleep(delay)

        self._running = False

        if self.close_task:
            self.close_task.cancel()
            self.close_task = None

        if self.refresh_task:
            self.refresh_task.cancel()
            self.refresh_task = None

        if self.client:
            self._cookies.update(self.client.cookies)
            await self.client.close()
            self.client = None

    async def reset_close_task(self) -> None:
        """

        Reset the timer for closing the client when a new request is made.

        """

        if self.close_task:
            self.close_task.cancel()
            self.close_task = None

        self.close_task = asyncio.create_task(self.close(self.close_delay))

    async def start_auto_refresh(self) -> None:
        """

        Start the background task to automatically refresh cookies.

        """
        if self.refresh_interval < 60:
            self.refresh_interval = 60

        while self._running:
            await asyncio.sleep(self.refresh_interval)

            if not self._running:
                break

            try:
                async with self._lock:
                    # Refresh all cookies in the background to keep the session alive.
                    new_1psidts = await rotate_1psidts(self.client, self.verbose)

                    if new_1psidts:
                        logger.debug("Cookies refreshed (network update).")
                    else:
                        logger.warning(
                            "Rotation response did not contain a new __Secure-1PSIDTS. "
                            "Session might expire soon if this persists."
                        )
            except asyncio.CancelledError:
                raise
            except AuthError:
                logger.warning(
                    "AuthError: Failed to refresh cookies. Retrying in next interval."
                )
            except Exception:
                logger.warning(
                    "Unexpected error while refreshing cookies. Retrying in next interval."
                )

    async def _init_rpc(self) -> None:
        """

        Send initial RPC calls to set up the session.

        """
        await self._fetch_models()
        await self._send_bard_settings()
        await self._send_bard_activity()
        await self._fetch_recent_chats()

    async def _fetch_models(self) -> None:
        """

        Fetch and parse available models.

        """
        response = await self._batch_execute(
            [
                RPCData(
                    rpcid=GRPC.LIST_MODELS,
                    payload="[]",
                )
            ]
        )

        response_json = extract_json_from_response(response.text)

        available_models = []
        for part in response_json:
            part_body_str = get_nested_value(part, [2])
            if not part_body_str:
                continue

            part_body = json.loads(part_body_str)

            models_list = get_nested_value(part_body, [15])
            if isinstance(models_list, list):
                for model_data in models_list:
                    if isinstance(model_data, list) and len(model_data) > 2:
                        model_id = get_nested_value(model_data, [0], "")
                        name = get_nested_value(model_data, [10]) or get_nested_value(
                            model_data, [1], ""
                        )
                        description = get_nested_value(
                            model_data, [12]
                        ) or get_nested_value(model_data, [2], "")
                        core_model = Model.UNSPECIFIED
                        code_name = "unspecified"
                        for enum_model in Model:
                            val = enum_model.model_header.get(
                                "x-goog-ext-525001261-jspb", ""
                            )
                            if val and (model_id in val):
                                core_model = enum_model
                                code_name = enum_model.model_name
                                break

                        if model_id and name:
                            available_models.append(
                                AvailableModel(
                                    id=code_name,
                                    name=name,
                                    model=core_model,
                                    description=description,
                                )
                            )
                break

        self._available_models = available_models

    async def _fetch_recent_chats(self, recent: int = 13) -> None:
        """

        Fetch and parse recent chats.

        """
        response_chats1 = await self._batch_execute(
            [
                RPCData(
                    rpcid=GRPC.LIST_CHATS,
                    payload=json.dumps([recent, None, [1, None, 1]]).decode("utf-8"),
                ),
            ]
        )
        response_chats2 = await self._batch_execute(
            [
                RPCData(
                    rpcid=GRPC.LIST_CHATS,
                    payload=json.dumps([recent, None, [0, None, 1]]).decode("utf-8"),
                ),
            ]
        )

        recent_chats: list[ChatInfo] = []
        for response_chats in (response_chats1, response_chats2):
            chats_json = extract_json_from_response(response_chats.text)
            for part in chats_json:
                part_body_str = get_nested_value(part, [2])
                if not part_body_str:
                    continue

                try:
                    part_body = json.loads(part_body_str)
                except json.JSONDecodeError:
                    continue

                chat_list = get_nested_value(part_body, [2])
                if isinstance(chat_list, list):
                    for chat_data in chat_list:
                        if isinstance(chat_data, list) and len(chat_data) > 1:
                            cid = get_nested_value(chat_data, [0], "")
                            title = get_nested_value(chat_data, [1], "")
                            is_pinned = bool(get_nested_value(chat_data, [2]))

                            if cid and title:
                                if not any(c.cid == cid for c in recent_chats):
                                    recent_chats.append(
                                        ChatInfo(
                                            cid=cid, title=title, is_pinned=is_pinned
                                        )
                                    )
                    break

        self._recent_chats = recent_chats

    async def _send_bard_settings(self) -> None:
        """

        Send required setup activity to Gemini.

        """
        await self._batch_execute(
            [
                RPCData(
                    rpcid=GRPC.BARD_SETTINGS,
                    payload='[[["adaptive_device_responses_enabled","advanced_mode_theme_override_triggered","advanced_zs_upsell_dismissal_count","advanced_zs_upsell_last_dismissed","ai_transparency_notice_dismissed","audio_overview_discovery_dismissal_count","audio_overview_discovery_last_dismissed","bard_in_chrome_link_sharing_enabled","bard_sticky_mode_disabled_count","canvas_create_discovery_tooltip_seen_count","combined_files_button_tag_seen_count","indigo_banner_explicit_dismissal_count","indigo_banner_impression_count","indigo_banner_last_seen_sec","current_popup_id","deep_research_has_seen_file_upload_tooltip","deep_research_model_update_disclaimer_display_count","default_bot_id","disabled_discovery_card_feature_ids","disabled_model_discovery_tooltip_feature_ids","disabled_mode_disclaimers","disabled_new_model_badge_mode_ids","disabled_settings_discovery_tooltip_feature_ids","disablement_disclaimer_last_dismissed_sec","disable_advanced_beta_dialog","disable_advanced_beta_non_en_banner","disable_advanced_resubscribe_ui","disable_at_mentions_discovery_tooltip","disable_autorun_fact_check_u18","disable_bot_create_tips_card","disable_bot_docs_in_gems_disclaimer","disable_bot_onboarding_dialog","disable_bot_save_reminder_tips_card","disable_bot_send_prompt_tips_card","disable_bot_shared_in_drive_disclaimer","disable_bot_try_create_tips_card","disable_colab_tooltip","disable_collapsed_tool_menu_tooltip","disable_continue_discovery_tooltip","disable_debug_info_moved_tooltip_v2","disable_enterprise_mode_dialog","disable_export_python_tooltip","disable_extensions_discovery_dialog","disable_extension_one_time_badge","disable_fact_check_tooltip_v2","disable_free_file_upload_tips_card","disable_generated_image_download_dialog","disable_get_app_banner","disable_get_app_desktop_dialog","disable_googler_in_enterprise_mode","disable_human_review_disclosure","disable_ice_open_vega_editor_tooltip","disable_image_upload_tooltip","disable_legal_concern_tooltip","disable_llm_history_import_disclaimer","disable_location_popup","disable_memory_discovery","disable_memory_extraction_discovery","disable_new_conversation_dialog","disable_onboarding_experience","disable_personal_context_tooltip","disable_photos_upload_disclaimer","disable_power_up_intro_tooltip","disable_scheduled_actions_mobile_notification_snackbar","disable_storybook_listen_button_tooltip","disable_streaming_settings_tooltip","disable_take_control_disclaimer","disable_teens_only_english_language_dialog","disable_tier1_rebranding_tooltip","disable_try_advanced_mode_dialog","enable_advanced_beta_mode","enable_advanced_mode","enable_googler_in_enterprise_mode","enable_memory","enable_memory_extraction","enable_personal_context","enable_personal_context_gemini","enable_personal_context_gemini_using_photos","enable_personal_context_gemini_using_workspace","enable_personal_context_search","enable_personal_context_youtube","enable_token_streaming","enforce_default_to_fast_version","mayo_discovery_banner_dismissal_count","mayo_discovery_banner_last_dismissed_sec","gempix_discovery_banner_dismissal_count","gempix_discovery_banner_last_dismissed","get_app_banner_ack_count","get_app_banner_seen_count","get_app_mobile_dialog_ack_count","guided_learning_banner_dismissal_count","guided_learning_banner_last_dismissed","has_accepted_agent_mode_fre_disclaimer","has_received_streaming_response","has_seen_agent_mode_tooltip","has_seen_bespoke_tooltip","has_seen_deepthink_mustard_tooltip","has_seen_deepthink_v2_tooltip","has_seen_deep_think_tooltip","has_seen_first_youtube_video_disclaimer","has_seen_ggo_tooltip","has_seen_image_grams_discovery_banner","has_seen_image_preview_in_input_area_tooltip","has_seen_kallo_discovery_banner","has_seen_kallo_tooltip","has_seen_model_picker_in_input_area_tooltip","has_seen_model_tooltip_in_input_area_for_gempix","has_seen_redo_with_gempix2_tooltip","has_seen_veograms_discovery_banner","has_seen_video_generation_discovery_banner","is_imported_chats_panel_open_by_default","jumpstart_onboarding_dismissal_count","last_dismissed_deep_research_implicit_invite","last_dismissed_discovery_feature_implicit_invites","last_dismissed_immersives_canvas_implicit_invite","last_dismissed_immersive_share_disclaimer_sec","last_dismissed_strike_timestamp_sec","last_dismissed_zs_student_aip_banner_sec","last_get_app_banner_ack_timestamp_sec","last_get_app_mobile_dialog_ack_timestamp_sec","last_human_review_disclosure_ack","last_selected_mode_id_in_embedded","last_selected_mode_id_on_web","last_two_up_activation_timestamp_sec","last_winter_olympics_interaction_timestamp_sec","memory_extracted_greeting_name","mini_gemini_tos_closed","mode_switcher_soft_badge_disabled_ids","mode_switcher_soft_badge_seen_count","personalization_first_party_onboarding_cross_surface_clicked","personalization_first_party_onboarding_cross_surface_seen_count","personalization_one_p_discovery_card_seen_count","personalization_one_p_discovery_last_consented","personalization_zero_state_card_last_interacted","personalization_zero_state_card_seen_count","popup_zs_visits_cooldown","require_reconsent_setting_for_personalization_banner_seen_count","show_debug_info","side_nav_open_by_default","student_verification_dismissal_count","student_verification_last_dismissed","task_viewer_cc_banner_dismissed_count","task_viewer_cc_banner_dismissed_time_sec","tool_menu_new_badge_disabled_ids","tool_menu_new_badge_impression_counts","tool_menu_soft_badge_disabled_ids","tool_menu_soft_badge_impression_counts","upload_disclaimer_last_consent_time_sec","viewed_student_aip_upsell_campaign_ids","voice_language","voice_name","web_and_app_activity_enabled","wellbeing_nudge_notice_last_dismissed_sec","zs_student_aip_banner_dismissal_count"]]]',
                )
            ]
        )

    async def _send_bard_activity(self) -> None:
        """

        Send warmup RPC calls before querying.

        """
        await self._batch_execute(
            [
                RPCData(
                    rpcid=GRPC.BARD_SETTINGS,
                    payload='[[["bard_activity_enabled"]]]',
                )
            ]
        )

    def list_models(self) -> list[AvailableModel] | None:
        """

        List all available models for the current account.



        Returns

        -------

        `list[gemini_webapi.types.AvailableModel]`

            List of models with their name and description. Returns `None` if the client holds no session cache.

        """
        return self._available_models

    async def generate_content(

        self,

        prompt: str,

        files: list[str | Path | bytes | io.BytesIO] | None = None,

        model: Model | str | dict = Model.UNSPECIFIED,

        gem: Gem | str | None = None,

        chat: Optional["ChatSession"] = None,

        temporary: bool = False,

        **kwargs,

    ) -> ModelOutput:
        """

        Generates contents with prompt.



        Parameters

        ----------

        prompt: `str`

            Text prompt provided by user.

        files: `list[str | Path | bytes | io.BytesIO]`, optional

            List of file paths or byte streams to be attached.

        model: `Model | str | dict`, optional

            Specify the model to use for generation.

            Pass either a `gemini_webapi.constants.Model` enum or a model name string to use predefined models.

            Pass a dictionary to use custom model header strings ("model_name" and "model_header" keys must be provided).

        gem: `Gem | str`, optional

            Specify a gem to use as system prompt for the chat session.

            Pass either a `gemini_webapi.types.Gem` object or a gem id string.

        chat: `ChatSession`, optional

            Chat data to retrieve conversation history.

            If None, will automatically generate a new chat id when sending post request.

        temporary: `bool`, optional

            If set to `True`, the ongoing conversation will not show up in Gemini history.

        kwargs: `dict`, optional

            Additional arguments which will be passed to the post request.

            Refer to `curl_cffi.requests.AsyncSession.request` for more information.



        Returns

        -------

        :class:`ModelOutput`

            Output data from gemini.google.com.



        Raises

        ------

        `AssertionError`

            If prompt is empty.

        `gemini_webapi.TimeoutError`

            If request timed out.

        `gemini_webapi.GeminiError`

            If no reply candidate found in response.

        `gemini_webapi.APIError`

            - If request failed with status code other than 200.

            - If response structure is invalid and failed to parse.

        """

        if self.auto_close:
            await self.reset_close_task()

        file_data = None
        if files:
            await self._send_bard_activity()

            uploaded_urls = await asyncio.gather(
                *(
                    upload_file(file, client=self.client, verbose=self.verbose)
                    for file in files
                )
            )
            file_data = [
                [[url], parse_file_name(file)]
                for url, file in zip(uploaded_urls, files)
            ]

        try:
            await self._send_bard_activity()

            session_state = {
                "last_texts": {},
                "last_thoughts": {},
                "last_progress_time": time.time(),
                "is_thinking": False,
                "is_queueing": False,
                "title": None,
            }
            output = None
            async for output in self._generate(
                prompt=prompt,
                req_file_data=file_data,
                model=model,
                gem=gem,
                chat=chat,
                temporary=temporary,
                session_state=session_state,
                **kwargs,
            ):
                pass

            if output is None:
                raise GeminiError(
                    "Failed to generate contents. No output data found in response."
                )

            if isinstance(chat, ChatSession):
                output.metadata = chat.metadata
                chat.last_output = output

            return output

        finally:
            if files:
                for file in files:
                    if isinstance(file, io.BytesIO):
                        file.close()

    async def generate_content_stream(

        self,

        prompt: str,

        files: list[str | Path | bytes | io.BytesIO] | None = None,

        model: Model | str | dict = Model.UNSPECIFIED,

        gem: Gem | str | None = None,

        chat: Optional["ChatSession"] = None,

        temporary: bool = False,

        **kwargs,

    ) -> AsyncGenerator[ModelOutput, None]:
        """

        Generates contents with prompt in streaming mode.



        This method sends a request to Gemini and yields partial responses as they arrive.

        It automatically calculates the text delta (new characters) to provide a smooth

        streaming experience. It also continuously updates chat metadata and candidate IDs.



        Parameters

        ----------

        prompt: `str`

            Text prompt provided by user.

        files: `list[str | Path | bytes | io.BytesIO]`, optional

            List of file paths or byte streams to be attached.

        model: `Model | str | dict`, optional

            Specify the model to use for generation.

        gem: `Gem | str`, optional

            Specify a gem to use as system prompt for the chat session.

        chat: `ChatSession`, optional

            Chat data to retrieve conversation history.

        temporary: `bool`, optional

            If set to `True`, the ongoing conversation will not show up in Gemini history.

        kwargs: `dict`, optional

            Additional arguments passed to `curl_cffi.requests.AsyncSession.stream`.



        Yields

        ------

        :class:`ModelOutput`

            Partial output data. The `text_delta` attribute contains only the NEW characters

            received since the last yield.



        Raises

        ------

        `gemini_webapi.APIError`

            If the request fails or response structure is invalid.

        `gemini_webapi.TimeoutError`

            If the stream request times out.

        """

        if self.auto_close:
            await self.reset_close_task()

        file_data = None
        if files:
            await self._send_bard_activity()

            uploaded_urls = await asyncio.gather(
                *(
                    upload_file(file, client=self.client, verbose=self.verbose)
                    for file in files
                )
            )
            file_data = [
                [[url], parse_file_name(file)]
                for url, file in zip(uploaded_urls, files)
            ]

        try:
            await self._send_bard_activity()

            session_state = {
                "last_texts": {},
                "last_thoughts": {},
                "last_progress_time": time.time(),
                "is_thinking": False,
                "is_queueing": False,
                "title": None,
            }
            output = None
            async for output in self._generate(
                prompt=prompt,
                req_file_data=file_data,
                model=model,
                gem=gem,
                chat=chat,
                temporary=temporary,
                session_state=session_state,
                **kwargs,
            ):
                yield output

            if output and isinstance(chat, ChatSession):
                output.metadata = chat.metadata
                chat.last_output = output

        finally:
            if files:
                for file in files:
                    if isinstance(file, io.BytesIO):
                        file.close()

    @running(retry=5)
    async def _generate(

        self,

        prompt: str,

        req_file_data: list[Any] | None = None,

        model: Model | str | dict = Model.UNSPECIFIED,

        gem: Gem | str | None = None,

        chat: Optional["ChatSession"] = None,

        temporary: bool = False,

        session_state: dict[str, Any] | None = None,

        **kwargs,

    ) -> AsyncGenerator[ModelOutput, None]:
        """

        Internal method which actually sends content generation requests.

        """

        assert prompt, "Prompt cannot be empty."

        if isinstance(model, str):
            model = Model.from_name(model)
        elif isinstance(model, dict):
            model = Model.from_dict(model)
        elif not isinstance(model, Model):
            raise TypeError(
                f"'model' must be a `gemini_webapi.constants.Model` instance, "
                f"string, or dictionary; got `{type(model).__name__}`"
            )

        _reqid = self._reqid
        self._reqid += 100000

        gem_id = gem.id if isinstance(gem, Gem) else gem

        chat_backup: dict[str, Any] | None = None
        if chat:
            chat_backup = {
                "metadata": (
                    list(chat.metadata)
                    if getattr(chat, "metadata", None)
                    else list(_DEFAULT_METADATA)
                ),
                "cid": getattr(chat, "cid", ""),
                "rid": getattr(chat, "rid", ""),
                "rcid": getattr(chat, "rcid", ""),
            }

        if session_state is None:
            session_state = {
                "last_texts": {},
                "last_thoughts": {},
                "last_progress_time": time.time(),
                "is_thinking": False,
                "is_queueing": False,
                "title": None,
            }
        else:
            # Reset connection-specific states during a retry attempt
            session_state["last_progress_time"] = time.time()
            session_state["is_thinking"] = False
            session_state["is_queueing"] = False

        has_generated_text = False
        sleep_time = 10

        message_content = [
            prompt,
            0,
            None,
            req_file_data,
            None,
            None,
            0,
        ]

        params: dict[str, Any] = {"_reqid": _reqid, "rt": "c"}
        if self.build_label:
            params["bl"] = self.build_label
        if self.session_id:
            params["f.sid"] = self.session_id

        while True:
            try:
                inner_req_list: list[Any] = [None] * 69
                inner_req_list[0] = message_content
                inner_req_list[2] = chat.metadata if chat else list(_DEFAULT_METADATA)
                inner_req_list[STREAMING_FLAG_INDEX] = 1
                if gem_id:
                    inner_req_list[GEM_FLAG_INDEX] = gem_id
                if temporary:
                    inner_req_list[TEMPORARY_CHAT_FLAG_INDEX] = 1

                request_data = {
                    "at": self.access_token,
                    "f.req": json.dumps(
                        [
                            None,
                            json.dumps(inner_req_list).decode("utf-8"),
                        ]
                    ).decode("utf-8"),
                }

                async with self.client.stream(
                    "POST",
                    Endpoint.GENERATE,
                    params=params,
                    headers=model.model_header,
                    data=request_data,
                    **kwargs,
                ) as response:
                    if self.verbose:
                        logger.debug(
                            f"HTTP Request: POST {Endpoint.GENERATE} [{response.status_code}]"
                        )
                    if response.status_code != 200:
                        await self.close()
                        raise APIError(
                            f"Failed to generate contents. Status: {response.status_code}"
                        )

                    buffer = ""
                    decoder = codecs.getincrementaldecoder("utf-8")(errors="replace")

                    last_texts: dict[str, str] = session_state["last_texts"]
                    last_thoughts: dict[str, str] = session_state["last_thoughts"]
                    last_progress_time = session_state["last_progress_time"]

                    is_thinking = session_state["is_thinking"]
                    is_queueing = session_state["is_queueing"]
                    has_candidates = False
                    is_completed = False  # Check if this conversation turn has been fully answered.
                    is_final_chunk = False  # Check if this turn is saved to history and marked complete or still pending (e.g., video generation).
                    cid = chat.cid if chat else ""
                    rid = chat.rid if chat else ""

                    async def _process_parts(

                        parts: list[Any],

                    ) -> AsyncGenerator[ModelOutput, None]:
                        nonlocal is_thinking, is_queueing, has_candidates, is_completed, is_final_chunk, cid, rid
                        for part in parts:
                            # Check for fatal error codes
                            error_code = get_nested_value(part, [5, 2, 0, 1, 0])
                            if error_code:
                                await self.close()
                                match error_code:
                                    case ErrorCode.USAGE_LIMIT_EXCEEDED:
                                        raise UsageLimitExceeded(
                                            f"Usage limit exceeded for model '{model.model_name}'. Please wait a few minutes, "
                                            "switch to a different model (e.g., Gemini Flash), or check your account limits on gemini.google.com."
                                        )
                                    case ErrorCode.MODEL_INCONSISTENT:
                                        raise ModelInvalid(
                                            "The specified model is inconsistent with the conversation history. "
                                            "Please ensure you are using the same 'model' parameter throughout the entire ChatSession."
                                        )
                                    case ErrorCode.MODEL_HEADER_INVALID:
                                        raise ModelInvalid(
                                            f"The model '{model.model_name}' is currently unavailable or the request structure is outdated. "
                                            "Please update 'gemini_webapi' to the latest version or report this on GitHub if the problem persists."
                                        )
                                    case ErrorCode.IP_TEMPORARILY_BLOCKED:
                                        raise TemporarilyBlocked(
                                            "Your IP address has been temporarily flagged or blocked by Google. "
                                            "Please try using a proxy, a different network, or wait for a while before retrying."
                                        )
                                    case ErrorCode.TEMPORARY_ERROR_1013:
                                        raise APIError(
                                            "Gemini encountered a temporary error (1013). Retrying..."
                                        )
                                    case _:
                                        raise APIError(
                                            f"Failed to generate contents (stream). Unknown API error code: {error_code}. "
                                            "This might be a temporary Google service issue."
                                        )

                            # Check for queueing status
                            status = get_nested_value(part, [5])
                            if isinstance(status, list) and status:
                                if not is_thinking:
                                    is_queueing = True
                                    session_state["is_queueing"] = True
                                    if not has_candidates:
                                        logger.debug(
                                            "Model is in a waiting state (queueing)..."
                                        )

                            inner_json_str = get_nested_value(part, [2])
                            if inner_json_str:
                                try:
                                    part_json = json.loads(inner_json_str)
                                    m_data = get_nested_value(part_json, [1])
                                    cid = get_nested_value(m_data, [0], "")
                                    rid = get_nested_value(m_data, [1], "")
                                    if m_data and isinstance(chat, ChatSession):
                                        chat.metadata = m_data

                                    # Check for busy analyzing data
                                    tool_name = get_nested_value(part_json, [6, 1, 0])
                                    if tool_name == "data_analysis_tool":
                                        is_thinking = True
                                        session_state["is_thinking"] = True
                                        is_queueing = False
                                        session_state["is_queueing"] = False
                                        if not has_candidates:
                                            logger.debug(
                                                f"Model is active (thinking/analyzing)... Raw: {str(part_json)[:500]}"
                                            )

                                    context_str = get_nested_value(part_json, [25])
                                    if isinstance(context_str, str):
                                        is_final_chunk = True
                                        is_thinking = False
                                        session_state["is_thinking"] = False
                                        is_queueing = False
                                        session_state["is_queueing"] = False
                                        if isinstance(chat, ChatSession):
                                            chat.metadata = [None] * 9 + [context_str]

                                    title = get_nested_value(part_json, [10, 0])
                                    if title:
                                        session_state["title"] = title

                                    candidates_list = get_nested_value(
                                        part_json, [4], []
                                    )
                                    if candidates_list:
                                        output_candidates = []
                                        for i, candidate_data in enumerate(
                                            candidates_list
                                        ):
                                            rcid = get_nested_value(candidate_data, [0])
                                            if not rcid:
                                                continue
                                            if isinstance(chat, ChatSession):
                                                chat.rcid = rcid

                                            (
                                                text,
                                                thoughts,
                                                web_images,
                                                generated_images,
                                                generated_videos,
                                            ) = self._parse_candidate(
                                                candidate_data, cid, rid, rcid
                                            )

                                            # Check if this frame represents the complete state of the message
                                            is_completed = (
                                                get_nested_value(
                                                    candidate_data, [8, 0], 1
                                                )
                                                == 2
                                            )

                                            # Save this conversation turn to list_chats whenever it is stored in history.
                                            if is_final_chunk:
                                                cid = get_nested_value(
                                                    part_json, [1, 0]
                                                )
                                                if cid and isinstance(
                                                    self._recent_chats, list
                                                ):
                                                    chat_title = session_state.get(
                                                        "title"
                                                    )
                                                    if not chat_title:
                                                        for c in self._recent_chats:
                                                            if c.cid == cid:
                                                                chat_title = c.title
                                                                break

                                                    if chat_title:
                                                        is_pinned = False
                                                        for c in self._recent_chats:
                                                            if c.cid == cid:
                                                                is_pinned = c.is_pinned
                                                                break

                                                        expected_idx = (
                                                            0
                                                            if is_pinned
                                                            else sum(
                                                                1
                                                                for c in self._recent_chats
                                                                if c.cid != cid
                                                                and c.is_pinned
                                                            )
                                                        )

                                                        if not (
                                                            len(self._recent_chats)
                                                            > expected_idx
                                                            and self._recent_chats[
                                                                expected_idx
                                                            ].cid
                                                            == cid
                                                            and self._recent_chats[
                                                                expected_idx
                                                            ].title
                                                            == chat_title
                                                        ):
                                                            self._recent_chats = [
                                                                c
                                                                for c in self._recent_chats
                                                                if c.cid != cid
                                                            ]
                                                            self._recent_chats.insert(
                                                                expected_idx,
                                                                ChatInfo(
                                                                    cid=cid,
                                                                    title=chat_title,
                                                                    is_pinned=is_pinned,
                                                                ),
                                                            )

                                            last_sent_text = last_texts.get(
                                                rcid
                                            ) or last_texts.get(f"idx_{i}", "")
                                            text_delta, new_full_text = (
                                                get_delta_by_fp_len(
                                                    text,
                                                    last_sent_text,
                                                    is_final=is_completed,
                                                )
                                            )
                                            last_sent_thought = last_thoughts.get(
                                                rcid
                                            ) or last_thoughts.get(f"idx_{i}", "")
                                            if thoughts:
                                                thoughts_delta, new_full_thought = (
                                                    get_delta_by_fp_len(
                                                        thoughts,
                                                        last_sent_thought,
                                                        is_final=is_completed,
                                                    )
                                                )
                                            else:
                                                thoughts_delta = ""
                                                new_full_thought = ""

                                            if (
                                                text_delta
                                                or thoughts_delta
                                                or web_images
                                                or generated_images
                                            ):
                                                has_candidates = True
                                                if thoughts_delta:
                                                    logger.debug(f"[Thinking]: {thoughts_delta.strip()}")
                                                if text_delta:
                                                    logger.debug(f"[Generating]: {text_delta.strip()}")

                                            # Update state with the provider's cleaned state to handle drift
                                            last_texts[rcid] = last_texts[
                                                f"idx_{i}"
                                            ] = new_full_text

                                            last_thoughts[rcid] = last_thoughts[
                                                f"idx_{i}"
                                            ] = new_full_thought

                                            output_candidates.append(
                                                Candidate(
                                                    rcid=rcid,
                                                    text=text,
                                                    text_delta=text_delta,
                                                    thoughts=thoughts or None,
                                                    thoughts_delta=thoughts_delta,
                                                    web_images=web_images,
                                                    generated_images=generated_images,
                                                    generated_videos=generated_videos,
                                                )
                                            )

                                        if output_candidates:
                                            is_thinking = False
                                            session_state["is_thinking"] = False
                                            is_queueing = False
                                            session_state["is_queueing"] = False
                                            yield ModelOutput(
                                                metadata=get_nested_value(
                                                    part_json, [1], []
                                                ),
                                                candidates=output_candidates,
                                            )
                                except json.JSONDecodeError:
                                    continue

                    chunk_iterator = response.aiter_content().__aiter__()
                    while True:
                        try:
                            stall_threshold = (
                                self.timeout
                                if (is_thinking or is_queueing)
                                else min(self.timeout, self.watchdog_timeout)
                            )
                            chunk = await asyncio.wait_for(
                                chunk_iterator.__anext__(), timeout=stall_threshold + 5
                            )
                        except StopAsyncIteration:
                            break
                        except asyncio.TimeoutError:
                            logger.debug(
                                f"[Watchdog] Socket idle for {stall_threshold + 5}s. Refreshing connection..."
                            )
                            break

                        buffer += decoder.decode(chunk, final=False)
                        if buffer.startswith(")]}'"):
                            buffer = buffer[4:].lstrip()
                        parsed_parts, buffer = parse_response_by_frame(buffer)

                        got_update = False
                        async for out in _process_parts(parsed_parts):
                            has_generated_text = True
                            yield out
                            got_update = True

                        if got_update:
                            last_progress_time = time.time()
                            session_state["last_progress_time"] = last_progress_time
                        else:
                            stall_threshold = (
                                self.timeout
                                if (is_thinking or is_queueing)
                                else min(self.timeout, self.watchdog_timeout)
                            )
                            if (time.time() - last_progress_time) > stall_threshold:
                                if is_thinking:
                                    logger.debug(
                                        f"[Watchdog] Model is taking its time thinking ({int(time.time() - last_progress_time)}s). Reconnecting to poll..."
                                    )
                                    break
                                else:
                                    logger.debug(
                                        f"[Watchdog] Connection idle for {stall_threshold}s (queueing={is_queueing}). "
                                        "Attempting recovery..."
                                    )
                                    await self.close()
                                    break

                    # Final flush
                    buffer += decoder.decode(b"", final=True)
                    if buffer:
                        parsed_parts, _ = parse_response_by_frame(buffer)
                        async for out in _process_parts(parsed_parts):
                            has_generated_text = True
                            yield out

                    if not is_completed or is_thinking or is_queueing:
                        stall_threshold = (
                            self.timeout
                            if (is_thinking or is_queueing)
                            else min(self.timeout, self.watchdog_timeout)
                        )
                        if (time.time() - last_progress_time) > stall_threshold:
                            if not is_thinking:
                                logger.debug(
                                    f"[Watchdog] Stream ended after {stall_threshold}s without completing. Triggering recovery..."
                                )
                            else:
                                logger.debug(
                                    "[Watchdog] Stream finished but model is still thinking. Polling again..."
                                )

                        if cid:
                            logger.debug(
                                f"Stream incomplete. Checking conversation history for {cid}..."
                            )

                            poll_start_time = time.time()

                            while True:
                                if (time.time() - poll_start_time) > self.timeout:
                                    logger.warning(
                                        f"[Recovery] Polling for {cid} timed out after {self.timeout}s."
                                    )
                                    if has_generated_text:
                                        raise GeminiError(
                                            "The connection to Gemini was lost while generating the response, and recovery timed out. "
                                            "Please try sending your prompt again."
                                        )
                                    else:
                                        raise APIError(
                                            "read_chat polling timed out waiting for the model to finish. "
                                            "The original request may have been silently aborted by Google."
                                        )
                                await self._send_bard_activity()
                                recovered_history = await self.read_chat(cid)
                                if (
                                    recovered_history
                                    and recovered_history.turns
                                    and recovered_history.turns[-1].role == "model"
                                ):
                                    recovered = recovered_history.turns[-1].info
                                    if (
                                        recovered
                                        and recovered.candidates
                                        and (
                                            recovered.candidates[0].text.strip()
                                            or recovered.candidates[0].generated_images
                                            or recovered.candidates[0].web_images
                                        )
                                    ):
                                        rec_rcid = recovered.candidates[0].rcid
                                        prev_rcid = (
                                            chat_backup["rcid"] if chat_backup else ""
                                        )
                                        current_expected_rcid = (
                                            getattr(chat, "rcid", "") if chat else ""
                                        )

                                        is_new_turn = (
                                            rec_rcid == current_expected_rcid
                                            if current_expected_rcid
                                            else rec_rcid != prev_rcid
                                        )

                                        if is_new_turn:
                                            logger.debug(
                                                f"[Recovery] Successfully recovered response for CID: {cid} (RCID: {rec_rcid})"
                                            )
                                            if chat:
                                                recovered.metadata = chat.metadata
                                                chat.rcid = rec_rcid
                                            yield recovered
                                            break
                                        else:
                                            logger.debug(
                                                f"[Recovery] Recovered turn is not the target turn (target: {current_expected_rcid or 'NEW'}, got {rec_rcid}). Waiting..."
                                            )

                                logger.debug(
                                    f"[Recovery] Response not ready, waiting {sleep_time}s..."
                                )
                                await asyncio.sleep(sleep_time)
                            break
                        else:
                            logger.debug(
                                f"Stream suspended (completed={is_completed}, final_chunk={is_final_chunk}, thinking={is_thinking}, queueing={is_queueing}). "
                                f"No CID found to recover. (Request ID: {_reqid})"
                            )
                            raise APIError(
                                "The original request may have been silently aborted by Google."
                            )

                break

            except ReadTimeout:
                raise TimeoutError(
                    "The request timed out while waiting for Gemini to respond. This often happens with very long prompts "
                    "or complex file analysis. Try increasing the 'timeout' value when initializing GeminiClient."
                )
            except (GeminiError, APIError):
                if not has_generated_text and chat and chat_backup:
                    chat.metadata = list(chat_backup["metadata"])  # type: ignore
                    chat.cid = chat_backup["cid"]
                    chat.rid = chat_backup["rid"]
                    chat.rcid = chat_backup["rcid"]
                raise
            except Exception:
                if not has_generated_text and chat and chat_backup:
                    chat.metadata = list(chat_backup["metadata"])  # type: ignore
                    chat.cid = chat_backup["cid"]
                    chat.rid = chat_backup["rid"]
                    chat.rcid = chat_backup["rcid"]
                logger.debug(
                    "Stream parsing interrupted. Attempting to recover conversation context..."
                )
                raise APIError(
                    "Failed to parse response body from Google. This might be a temporary API change or invalid data."
                )

    def start_chat(self, **kwargs) -> "ChatSession":
        """

        Returns a `ChatSession` object attached to this client.



        Parameters

        ----------

        kwargs: `dict`, optional

            Additional arguments which will be passed to the chat session.

            Refer to `gemini_webapi.ChatSession` for more information.



        Returns

        -------

        :class:`ChatSession`

            Empty chat session object for retrieving conversation history.

        """

        return ChatSession(geminiclient=self, **kwargs)

    async def delete_chat(self, cid: str) -> None:
        """

        Delete a specific conversation by chat id.



        Parameters

        ----------

        cid: `str`

            The ID of the chat requiring deletion (e.g. "c_...").

        """

        await self._batch_execute(
            [
                RPCData(
                    rpcid=GRPC.DELETE_CHAT,
                    payload=json.dumps([cid]).decode("utf-8"),
                ),
            ]
        )
        await self._batch_execute(
            [
                RPCData(
                    rpcid=GRPC.DELETE_CHAT_SECOND,
                    payload=json.dumps([cid, [1, None, 0, 1]]).decode("utf-8"),
                ),
            ]
        )

    def list_chats(self) -> list[ChatInfo] | None:
        """

        List all conversations.



        Returns

        -------

        `list[gemini_webapi.types.ChatInfo] | None`

            The list of conversations. Returns `None` if the client holds no session cache.

        """
        return self._recent_chats

    async def read_chat(self, cid: str, limit: int = 10) -> ChatHistory | None:
        """

        Fetch the full conversation history by chat id.



        Parameters

        ----------

        cid: `str`

            The ID of the conversation to read (e.g. "c_...").

        limit: `int`, optional

            The maximum number of turns to fetch, by default 10.



        Returns

        -------

        :class:`ChatHistory` | None

            The conversation history, or None if reading failed.

        """
        try:
            response = await self._batch_execute(
                [
                    RPCData(
                        rpcid=GRPC.READ_CHAT,
                        payload=json.dumps(
                            [cid, limit, None, 1, [1], [4], None, 1]
                        ).decode("utf-8"),
                    ),
                ]
            )

            response_json = extract_json_from_response(response.text)

            for part in response_json:
                part_body_str = get_nested_value(part, [2])
                if not part_body_str:
                    continue

                part_body = json.loads(part_body_str)
                turns_data = get_nested_value(part_body, [0])
                if not turns_data:
                    continue

                chat_turns = []
                for conv_turn in turns_data:
                    # User turn
                    user_text = get_nested_value(conv_turn, [2, 0, 0], "")
                    if user_text:
                        chat_turns.append(ChatTurn(role="user", text=user_text))

                    # Model turn
                    candidates_list = get_nested_value(conv_turn, [3, 0])
                    if candidates_list:
                        output_candidates = []
                        rid = get_nested_value(conv_turn, [1], "")
                        for candidate_data in candidates_list:
                            rcid = get_nested_value(candidate_data, [0], "")
                            (
                                text,
                                thoughts,
                                web_images,
                                generated_images,
                                generated_videos,
                            ) = self._parse_candidate(candidate_data, cid, rid, rcid)
                            output_candidates.append(
                                Candidate(
                                    rcid=rcid,
                                    text=text,
                                    thoughts=thoughts,
                                    web_images=web_images,
                                    generated_images=generated_images,
                                    generated_videos=generated_videos,
                                )
                            )

                        if output_candidates:
                            model_output = ModelOutput(
                                metadata=[cid, rid, output_candidates[0].rcid],
                                candidates=output_candidates,
                            )
                            chat_turns.append(
                                ChatTurn(
                                    role="model",
                                    text=output_candidates[0].text,
                                    info=model_output,
                                )
                            )

                return ChatHistory(cid=cid, metadata=[cid], turns=chat_turns)

            return None
        except Exception:
            logger.debug(
                f"[read_chat] Response data for {cid!r} is still incomplete (model is still processing)..."
            )
            return None

    def _parse_candidate(

        self, candidate_data: list[Any], cid: str, rid: str, rcid: str

    ) -> tuple[str, str, list[WebImage], list[GeneratedImage], list[GeneratedVideo]]:
        """

        Parses individual candidate data from the Gemini response.



        Args:

            candidate_data (list[Any]): The raw candidate list from the API response.

            cid (str): Conversation ID.

            rid (str): Response ID.

            rcid (str): Response Candidate ID.



        Returns:

            tuple: A tuple containing:

                - text (str): The main response text.

                - thoughts (str): The model's reasoning or internal thoughts.

                - web_images (list[WebImage]): List of images found on the web.

                - generated_images (list[GeneratedImage]): List of images generated by the model.

                - generated_videos (list[GeneratedVideo]): List of videos generated by the model.

        """
        text = get_nested_value(candidate_data, [1, 0], "")
        if _CARD_CONTENT_RE.match(text):
            text = get_nested_value(candidate_data, [22, 0]) or text

        # Cleanup googleusercontent artifacts
        text = _ARTIFACTS_RE.sub("", text)

        thoughts = get_nested_value(candidate_data, [37, 0, 0]) or ""

        # Image handling
        web_images = []
        for img_idx, web_img_data in enumerate(
            get_nested_value(candidate_data, [12, 1], [])
        ):
            url = get_nested_value(web_img_data, [0, 0, 0])
            if url:
                web_images.append(
                    WebImage(
                        url=url,
                        title=f"[Image {img_idx + 1}]",
                        alt=get_nested_value(web_img_data, [0, 4], ""),
                        proxy=self.proxy,
                        client=self.client,
                    )
                )

        generated_images = []
        for img_idx, gen_img_data in enumerate(
            get_nested_value(candidate_data, [12, 7, 0], [])
        ):
            url = get_nested_value(gen_img_data, [0, 3, 3])
            if url:
                image_id = get_nested_value(gen_img_data, [1, 0])
                if not image_id:
                    image_id = f"http://googleusercontent.com/image_generation_content/{img_idx}"

                generated_images.append(
                    GeneratedImage(
                        url=url,
                        title=f"[Generated Image {img_idx}]",
                        alt=get_nested_value(gen_img_data, [0, 3, 2], ""),
                        proxy=self.proxy,
                        client=self.client,
                        client_ref=self,
                        cid=cid,
                        rid=rid,
                        rcid=rcid,
                        image_id=image_id,
                    )
                )

        # Video handling
        generated_videos = []
        for video_root in get_nested_value(candidate_data, [12, 59, 0], []):
            video_info = get_nested_value(video_root, [0])
            if video_info:
                urls = get_nested_value(video_info, [0, 7], [])
                if len(urls) >= 2:
                    generated_videos.append(
                        GeneratedVideo(
                            url=urls[1],
                            thumbnail=urls[0],
                            cid=cid,
                            rid=rid,
                            rcid=rcid,
                            client_ref=self,
                            proxy=self.proxy,
                        )
                    )

        return text, thoughts, web_images, generated_images, generated_videos

    async def _get_image_full_size(

        self, cid: str, rid: str, rcid: str, image_id: str

    ) -> str | None:
        """

        Get the full size URL of an image.

        """
        try:
            payload = [
                [
                    [None, None, None, [None, None, None, None, None, ""]],
                    [image_id, 0],
                    None,
                    [19, ""],
                    None,
                    None,
                    None,
                    None,
                    None,
                    "",
                ],
                [rid, rcid, cid, None, ""],
                1,
                0,
                1,
            ]

            response = await self._batch_execute(
                [
                    RPCData(
                        rpcid=GRPC.IMAGE_FULL_SIZE,
                        payload=json.dumps(payload).decode("utf-8"),
                    ),
                ]
            )

            response_data = extract_json_from_response(response.text)
            return get_nested_value(
                json.loads(get_nested_value(response_data, [0, 2], "[]")), [0]
            )
        except Exception:
            logger.debug(
                "[_get_image_full_size] Could not retrieve full size URL via RPC."
            )
            return None

    @running(retry=2)
    async def _batch_execute(self, payloads: list[RPCData], **kwargs) -> Response:
        """

        Execute a batch of requests to Gemini API.



        Parameters

        ----------

        payloads: `list[RPCData]`

            List of `gemini_webapi.types.RPCData` objects to be executed.

        kwargs: `dict`, optional

            Additional arguments which will be passed to the post request.

            Refer to `curl_cffi.requests.AsyncSession.request` for more information.



        Returns

        -------

        :class:`curl_cffi.requests.Response`

            Response object containing the result of the batch execution.

        """

        _reqid = self._reqid
        self._reqid += 100000

        try:
            params: dict[str, Any] = {
                "rpcids": ",".join([p.rpcid for p in payloads]),
                "_reqid": _reqid,
                "rt": "c",
                "source-path": "/app",
            }
            if self.build_label:
                params["bl"] = self.build_label
            if self.session_id:
                params["f.sid"] = self.session_id

            response = await self.client.post(
                Endpoint.BATCH_EXEC,
                params=params,
                data={
                    "at": self.access_token,
                    "f.req": json.dumps(
                        [[payload.serialize() for payload in payloads]]
                    ).decode("utf-8"),
                },
                **kwargs,
            )
            if self.verbose:
                logger.debug(
                    f"HTTP Request: POST {Endpoint.BATCH_EXEC} [{response.status_code}]"
                )
        except ReadTimeout:
            raise TimeoutError(
                "The request timed out while waiting for Gemini to respond. This often happens with very long prompts "
                "or complex file analysis. Try increasing the 'timeout' value when initializing GeminiClient."
            )

        if response.status_code != 200:
            await self.close()
            raise APIError(
                f"Batch execution failed with status code {response.status_code}"
            )

        return response


class ChatSession:
    """

    Chat data to retrieve conversation history. Only if all 3 ids are provided will the conversation history be retrieved.



    Parameters

    ----------

    geminiclient: `GeminiClient`

        Async requests client interface for gemini.google.com.

    metadata: `list[str]`, optional

        List of chat metadata `[cid, rid, rcid]`, can be shorter than 3 elements, like `[cid, rid]` or `[cid]` only.

    cid: `str`, optional

        Chat id, if provided together with metadata, will override the first value in it.

    rid: `str`, optional

        Reply id, if provided together with metadata, will override the second value in it.

    rcid: `str`, optional

        Reply candidate id, if provided together with metadata, will override the third value in it.

    model: `Model | str | dict`, optional

        Specify the model to use for generation.

        Pass either a `gemini_webapi.constants.Model` enum or a model name string to use predefined models.

        Pass a dictionary to use custom model header strings ("model_name" and "model_header" keys must be provided).

    gem: `Gem | str`, optional

        Specify a gem to use as system prompt for the chat session.

        Pass either a `gemini_webapi.types.Gem` object or a gem id string.

    """

    __slots__ = [
        "__metadata",
        "geminiclient",
        "last_output",
        "model",
        "gem",
    ]

    def __init__(

        self,

        geminiclient: GeminiClient,

        metadata: list[str | None] | None = None,

        cid: str = "",  # chat id

        rid: str = "",  # reply id

        rcid: str = "",  # reply candidate id

        model: Model | str | dict = Model.UNSPECIFIED,

        gem: Gem | str | None = None,

    ):
        self.__metadata: list[Any] = list(_DEFAULT_METADATA)
        self.geminiclient: GeminiClient = geminiclient
        self.last_output: ModelOutput | None = None
        self.model: Model | str | dict = model
        self.gem: Gem | str | None = gem

        if metadata:
            self.metadata = metadata
        if cid:
            self.cid = cid
        if rid:
            self.rid = rid
        if rcid:
            self.rcid = rcid

    def __str__(self):
        return f"ChatSession(cid='{self.cid}', rid='{self.rid}', rcid='{self.rcid}')"

    __repr__ = __str__

    def __setattr__(self, name: str, value: Any) -> None:
        super().__setattr__(name, value)
        # update conversation history when last output is updated
        if name == "last_output" and isinstance(value, ModelOutput):
            self.metadata = value.metadata
            self.rcid = value.rcid

    async def send_message(

        self,

        prompt: str,

        files: list[str | Path | bytes | io.BytesIO] | None = None,

        temporary: bool = False,

        **kwargs,

    ) -> ModelOutput:
        """

        Generates contents with prompt.

        Use as a shortcut for `GeminiClient.generate_content(prompt, files, self)`.



        Parameters

        ----------

        prompt: `str`

            Text prompt provided by user.

        files: `list[str | Path | bytes | io.BytesIO]`, optional

            List of file paths or byte streams to be attached.

        temporary: `bool`, optional

            If set to `True`, the ongoing conversation will not show up in Gemini history.

            Switching temporary mode within a chat session will clear the previous context

            and create a new chat session under the hood.

        kwargs: `dict`, optional

            Additional arguments which will be passed to the post request.

            Refer to `curl_cffi.requests.AsyncSession.request` for more information.



        Returns

        -------

        :class:`ModelOutput`

            Output data from gemini.google.com.



        Raises

        ------

        `AssertionError`

            If prompt is empty.

        `gemini_webapi.TimeoutError`

            If request timed out.

        `gemini_webapi.GeminiError`

            If no reply candidate found in response.

        `gemini_webapi.APIError`

            - If request failed with status code other than 200.

            - If response structure is invalid and failed to parse.

        """

        return await self.geminiclient.generate_content(
            prompt=prompt,
            files=files,
            model=self.model,
            gem=self.gem,
            chat=self,
            temporary=temporary,
            **kwargs,
        )

    async def send_message_stream(

        self,

        prompt: str,

        files: list[str | Path | bytes | io.BytesIO] | None = None,

        temporary: bool = False,

        **kwargs,

    ) -> AsyncGenerator[ModelOutput, None]:
        """

        Generates contents with prompt in streaming mode within this chat session.



        This is a shortcut for `GeminiClient.generate_content_stream(prompt, files, self)`.

        The session's metadata and conversation history are automatically managed.



        Parameters

        ----------

        prompt: `str`

            Text prompt provided by user.

        files: `list[str | Path | bytes | io.BytesIO]`, optional

            List of file paths or byte streams to be attached.

        temporary: `bool`, optional

            If set to `True`, the ongoing conversation will not show up in Gemini history.

            Switching temporary mode within a chat session will clear the previous context

            and create a new chat session under the hood.

        kwargs: `dict`, optional

            Additional arguments passed to the streaming request.



        Yields

        ------

        :class:`ModelOutput`

            Partial output data containing text deltas.

        """

        async for output in self.geminiclient.generate_content_stream(
            prompt=prompt,
            files=files,
            model=self.model,
            gem=self.gem,
            chat=self,
            temporary=temporary,
            **kwargs,
        ):
            yield output

    def choose_candidate(self, index: int) -> ModelOutput:
        """

        Choose a candidate from the last `ModelOutput` to control the ongoing conversation flow.



        Parameters

        ----------

        index: `int`

            Index of the candidate to choose, starting from 0.



        Returns

        -------

        :class:`ModelOutput`

            Output data of the chosen candidate.



        Raises

        ------

        `ValueError`

            If no previous output data found in this chat session, or if index exceeds the number of candidates in last model output.

        """

        if not self.last_output:
            raise ValueError("No previous output data found in this chat session.")

        if index >= len(self.last_output.candidates):
            raise ValueError(
                f"Index {index} exceeds the number of candidates in last model output."
            )

        self.last_output.chosen = index
        self.rcid = self.last_output.rcid
        return self.last_output

    async def read_history(self, limit: int = 10) -> ChatHistory | None:
        """

        Fetch the conversation history for this session.



        Parameters

        ----------

        limit: `int`, optional

            The maximum number of turns to fetch, by default 10.



        Returns

        -------

        :class:`ChatHistory` | None

            The conversation history, or None if reading failed or cid is missing.

        """
        if not self.cid:
            return None
        return await self.geminiclient.read_chat(self.cid, limit=limit)

    @property
    def metadata(self):
        return self.__metadata

    @metadata.setter
    def metadata(self, value: list[str]):
        if not isinstance(value, list):
            return

        # Update only non-None elements to preserve existing CID/RID/RCID/Context
        for i, val in enumerate(value):
            if i < 10 and val is not None:
                self.__metadata[i] = val

    @property
    def cid(self):
        return self.__metadata[0]

    @cid.setter
    def cid(self, value: str):
        self.__metadata[0] = value

    @property
    def rcid(self):
        return self.__metadata[2]

    @rcid.setter
    def rcid(self, value: str):
        self.__metadata[2] = value

    @property
    def rid(self):
        return self.__metadata[1]

    @rid.setter
    def rid(self, value: str):
        self.__metadata[1] = value