use std::{
    borrow::Cow, collections::VecDeque, convert::Infallible, env, path::PathBuf, time::Duration,
};

use axum::{
    response::sse::{Event, KeepAlive, Sse},
    Json,
};
use futures_util::{stream, StreamExt};
use serde::{Deserialize, Serialize};
use tokio::sync::mpsc;
use tracing::{error, warn};

use crate::inference::python_bridge::PythonBridge;
use crate::inference::response_validator::{
    AutonomousSessionPayload, CodeGenerationPayload, ImageGenerationPayload, LiveSnapshotPayload,
    MusicGenerationPayload, OrchestrationRoutePayload, ProjectFilePayload, SessionContextPayload,
    TextGenerationPayload, ToolTracePayload, ValidateCoreResponse, VideoGenerationPayload,
    VisionAnalysisPayload,
};
use crate::utils::errors::MarisError;

// Keep chunks small enough for a natural "typing" feel without splitting almost
// every word into its own network event.
const CHAT_STREAM_CHUNK_CHARS: usize = 48;
// A short pause makes the SSE output feel live while keeping the total response
// time fast for already-generated backend results.
const CHAT_STREAM_INTERVAL_MS: u64 = 24;

#[derive(Clone, Deserialize)]
pub struct ChatRequest {
    pub message: String,
    pub history: Option<Vec<HistoryMessage>>,
    pub session_id: Option<String>,
    pub persona_id: Option<String>,
    pub autonomous_mode: Option<bool>,
    pub fallback_model: Option<String>,
    pub image_url: Option<String>,
    pub image_base64: Option<String>,
    pub camera_id: Option<String>,
    pub confidence_threshold: Option<f32>,
}

#[derive(Clone, Deserialize, Serialize)]
pub struct HistoryMessage {
    pub role: String,
    pub content: String,
}

#[derive(Clone, Serialize)]
pub struct ChatAutonomousTaskPayload {
    pub id: String,
    pub description: String,
    pub status: String,
    pub tool: String,
    pub result: Option<String>,
}

#[derive(Clone, Serialize)]
pub struct ChatProjectFilePayload {
    pub path: String,
    pub absolute_path: Option<String>,
}

#[derive(Clone, Serialize)]
pub struct ChatRouteInfo {
    pub capability: String,
    pub branch: String,
    pub profile: String,
    pub studio: String,
    pub reasoning: String,
    pub confidence: f32,
}

#[derive(Clone, Serialize)]
#[serde(tag = "type", rename_all = "snake_case")]
pub enum ChatHandoffPayload {
    Code {
        code: String,
        explanation: String,
        language: String,
        detected_stack: String,
        files: Vec<ChatProjectFilePayload>,
        workspace_artifact_dir: Option<String>,
        bundle_download_url: Option<String>,
        entrypoint: Option<String>,
        repo_path: Option<String>,
    },
    Image {
        image_url: String,
        prompt: String,
    },
    Music {
        audio_url: String,
        title: String,
        duration_seconds: u32,
    },
    Video {
        video_url: String,
        duration_seconds: u32,
        prompt: String,
    },
    Autonomous {
        session_id: String,
        status: String,
        progress_percent: u32,
        persona_id: String,
        persona_title: String,
        tasks: Vec<ChatAutonomousTaskPayload>,
    },
}

#[derive(Clone, Serialize)]
pub struct ChatResponse {
    pub response: String,
    pub model: String,
    pub route: ChatRouteInfo,
    pub handoff: Option<ChatHandoffPayload>,
    pub vision_context: Option<ChatVisionContext>,
    pub detected_emotion: String,
    pub emotion_confidence: f32,
    pub response_style: String,
    pub persona_id: Option<String>,
    pub persona_title: Option<String>,
    pub persona_summary: Option<String>,
    pub request_id: Option<String>,
    pub session_id: Option<String>,
    pub latency_ms: Option<u32>,
    pub tokens_used: Option<u32>,
    pub prompt_messages: Option<u32>,
    pub memory_matches: Option<u32>,
    pub tool_trace: Option<ToolTracePayload>,
}

#[derive(Clone, Serialize)]
pub struct ChatVisionContext {
    pub summary: String,
    pub source: String,
    pub model: String,
    pub fallback_used: bool,
    pub camera_id: Option<String>,
    pub image_url: Option<String>,
    pub width: u32,
    pub height: u32,
    pub detections: u32,
}

#[derive(Clone, Serialize)]
pub struct ChatStatusPayload {
    pub stage: String,
    pub message: String,
}

struct ChatExecutionResult {
    response: String,
    model: String,
    handoff: Option<ChatHandoffPayload>,
    vision_context: Option<ChatVisionContext>,
    detected_emotion: String,
    emotion_confidence: f32,
    response_style: String,
    persona_id: Option<String>,
    persona_title: Option<String>,
    persona_summary: Option<String>,
    request_id: Option<String>,
    session_id: Option<String>,
    latency_ms: Option<u32>,
    tokens_used: Option<u32>,
    prompt_messages: Option<u32>,
    memory_matches: Option<u32>,
    tool_trace: Option<ToolTracePayload>,
}

struct PreparedChatRequest {
    message: String,
    history: Vec<HistoryMessage>,
    session_id: Option<String>,
    persona_id: Option<String>,
    fallback_model: Option<String>,
    route: OrchestrationRoutePayload,
    vision_context: Option<ChatVisionContext>,
}

/// Resolved visual input for `/api/chat` after normalizing direct uploads or
/// dereferencing a live camera into its latest snapshot payload.
///
/// `image_url` is populated for URL-based uploads or resolved camera snapshots,
/// `image_base64` is populated for direct file uploads, and `camera_id` is only
/// carried through when the source originated from a camera-aware request.
struct ResolvedVisionInput {
    source: String,
    image_url: Option<String>,
    image_base64: Option<String>,
    camera_id: Option<String>,
}

#[derive(Debug, Deserialize)]
struct CoreTextDeltaPayload {
    delta: Option<String>,
}

#[derive(Debug, Deserialize)]
struct CoreTextErrorPayload {
    error: Option<String>,
}

enum CoreTextStreamEvent {
    Delta(String),
    Complete(Box<TextGenerationPayload>),
    Error(String),
}

fn fallback_chat_model(capability: &str) -> String {
    match capability {
        "code_generation" => "Maris code handoff".to_string(),
        "image_generation" => "Maris image handoff".to_string(),
        "music_generation" => "Maris music handoff".to_string(),
        "video_generation" => "Maris video handoff".to_string(),
        "vision_analysis" => "Maris vision handoff".to_string(),
        _ => "Maris core text model".to_string(),
    }
}

fn default_emotion_metadata() -> (String, f32, String) {
    ("neutral".to_string(), 0.0, "clear_grounded".to_string())
}

fn default_route() -> OrchestrationRoutePayload {
    OrchestrationRoutePayload {
        capability: "text_chat".to_string(),
        branch: "master".to_string(),
        profile: "general".to_string(),
        target_endpoint: "text/generate".to_string(),
        target_studio: "/chat".to_string(),
        reasoning: "Izmanto noklusējuma sarunu atzaru, jo routing serviss nav pieejams."
            .to_string(),
        confidence: 0.5,
        session_context: SessionContextPayload::default(),
    }
}

fn generation_status_message(capability: &str) -> &'static str {
    match capability {
        "code_generation" => "Veidoju koda draftu.",
        "autonomous_tasks" => "Sāku izpildāmu uzdevumu plūsmu.",
        "image_generation" => "Salieku attēla handoff.",
        "music_generation" => "Komponēju audio handoff.",
        "video_generation" => "Sagatavoju video handoff.",
        _ => "Domāju un veidoju atbildi.",
    }
}

fn infer_code_language(message: &str) -> String {
    let normalized = message.to_lowercase();
    if normalized.contains("next.js") || normalized.contains("nextjs") {
        return "Next.js (TypeScript)".to_string();
    }
    if normalized.contains("react") {
        return "React (TypeScript)".to_string();
    }
    if normalized.contains("typescript") {
        return "TypeScript".to_string();
    }
    if normalized.contains("javascript") || normalized.contains("node") {
        return "JavaScript".to_string();
    }
    if normalized.contains("rust") {
        return "Rust".to_string();
    }
    if normalized.contains("sql") {
        return "SQL".to_string();
    }
    if normalized.contains("html")
        || normalized.contains("css")
        || normalized.contains("ui")
        || normalized.contains("frontend")
        || normalized.contains("landing page")
        || normalized.contains("web app")
        || normalized.contains("web-app")
        || normalized.contains("kalkulator")
        || normalized.contains("calculator")
    {
        return "HTML/CSS/JavaScript".to_string();
    }
    "Python".to_string()
}

fn build_code_context(message: &str, language: &str) -> String {
    let normalized = message.to_lowercase();
    if language == "Next.js (TypeScript)" {
        return "Atdod pilnu Next.js/TypeScript feature vai app router artefaktu ar pareizu failu struktūru, nevis tikai vienu komponenti.".to_string();
    }
    if language == "HTML/CSS/JavaScript" {
        return "Atdod vienu pilnīgu, uzreiz pārlūkā palaižamu artefaktu ar visu markup, stilu un loģiku bez TODO placeholderiem.".to_string();
    }
    if language == "React (TypeScript)" {
        return "Atdod pilnu React/TypeScript komponenti vai mazu feature moduli ar gatavu UI loģiku, nevis tikai skeletu.".to_string();
    }
    if normalized.contains("salabo")
        || normalized.contains("labo")
        || normalized.contains("uzlabo")
        || normalized.contains("improve")
        || normalized.contains("fix")
        || normalized.contains("refactor")
    {
        return "Ja pieprasījums ir par labojumu vai uzlabojumu, atdod gala kodu ar ielabotu rezultātu, nevis tikai komentāru par pieeju.".to_string();
    }
    String::new()
}

fn is_repo_edit_request(message: &str) -> bool {
    let normalized = message.to_lowercase();
    [
        "edit", "modify", "update", "refactor", "fix", "patch", "rewrite", "change", "labo",
        "salabo", "rediģē", "uzlabo", "esoš",
    ]
    .iter()
    .any(|keyword| normalized.contains(keyword))
}

fn infer_repo_root() -> Option<String> {
    let mut current = env::current_dir().ok()?;
    loop {
        if current.join(".git").exists()
            || (current.join("backend-rust").is_dir()
                && current.join("core-python").is_dir()
                && current.join("frontend").is_dir())
        {
            return Some(current.to_string_lossy().into_owned());
        }
        if !current.pop() {
            break;
        }
    }
    None
}

fn infer_repo_path(message: &str) -> Option<String> {
    for token in message.split_whitespace() {
        let trimmed = token.trim_matches(|ch: char| {
            matches!(
                ch,
                '"' | '\'' | '`' | ',' | '.' | ';' | ':' | '(' | ')' | '[' | ']' | '{' | '}'
            )
        });
        if trimmed.is_empty() {
            continue;
        }
        let candidate = PathBuf::from(trimmed);
        let resolved =
            if candidate.is_absolute() || trimmed.starts_with("./") || trimmed.starts_with("../") {
                candidate.canonicalize().ok()
            } else {
                None
            };
        let Some(path) = resolved else {
            continue;
        };
        if path.is_dir() {
            return Some(path.to_string_lossy().into_owned());
        }
        if let Some(parent) = path.parent() {
            return Some(parent.to_string_lossy().into_owned());
        }
    }
    if is_repo_edit_request(message) {
        return infer_repo_root();
    }
    None
}

fn bundle_download_url(workspace_artifact_dir: Option<&str>) -> Option<String> {
    workspace_artifact_dir.map(|artifact_dir| {
        format!(
            "/api/code/download?artifact_dir={}",
            urlencoding::encode(artifact_dir)
        )
    })
}

fn normalize_message(message: &str) -> Result<String, MarisError> {
    let trimmed = message.trim();

    if trimmed.is_empty() {
        return Err(MarisError::Validation(
            "Ziņa nedrīkst būt tukša.".to_string(),
        ));
    }

    Ok(trimmed.to_string())
}

fn normalize_optional_text(value: Option<String>) -> Option<String> {
    value.and_then(|item| {
        let trimmed = item.trim().to_string();
        if trimmed.is_empty() {
            None
        } else {
            Some(trimmed)
        }
    })
}

fn is_autonomous_mode_enabled(value: Option<bool>) -> bool {
    value.unwrap_or(false)
}

fn force_autonomous_route(
    route: OrchestrationRoutePayload,
    session_id: Option<&str>,
    persona_id: Option<&str>,
) -> OrchestrationRoutePayload {
    let mut forced = route;
    forced.capability = "autonomous_tasks".to_string();
    forced.branch = "planner".to_string();
    forced.profile = "planner".to_string();
    forced.target_endpoint = "autonomous/start".to_string();
    forced.target_studio = "/autonomous".to_string();
    forced.reasoning = "Autonomous chat mode ir ieslēgts, tāpēc pieprasījums tiek izpildīts caur planner aģenta plūsmu.".to_string();
    forced.confidence = 1.0;
    if let Some(session_id) = session_id {
        forced.session_context.session_id = session_id.to_string();
    }
    if let Some(persona_id) = persona_id {
        forced.session_context.persona_id = persona_id.to_string();
    }
    forced
}

async fn resolve_vision_input(
    bridge: &PythonBridge,
    req: &ChatRequest,
) -> Result<Option<ResolvedVisionInput>, MarisError> {
    let image_url = normalize_optional_text(req.image_url.clone());
    let image_base64 = normalize_optional_text(req.image_base64.clone());
    let camera_id = normalize_optional_text(req.camera_id.clone());

    // Keep this early guard in Rust so `/api/chat` returns a clear validation
    // error before making any downstream bridge calls into the vision service.
    if image_url.is_some() && image_base64.is_some() {
        return Err(MarisError::Validation(
            "Norādi tikai vienu no image_url vai image_base64.".to_string(),
        ));
    }

    if image_url.is_some() || image_base64.is_some() {
        return Ok(Some(ResolvedVisionInput {
            source: "upload".to_string(),
            image_url,
            image_base64,
            camera_id,
        }));
    }

    let Some(camera_id) = camera_id else {
        return Ok(None);
    };

    let endpoint = format!("vision/live/{camera_id}/snapshot");
    let snapshot = bridge
        .get::<LiveSnapshotPayload>(&endpoint)
        .await
        .map_err(|err| {
            MarisError::Bridge(format!(
                "Neizdevās ielādēt snapshot kamerai {camera_id}: {err}"
            ))
        })?;
    let snapshot_data_url = snapshot.snapshot_data_url.ok_or_else(|| {
        MarisError::Validation(format!(
            "Kamerai {camera_id} pašlaik nav pieejams snapshot čatam."
        ))
    })?;

    Ok(Some(ResolvedVisionInput {
        source: "camera_snapshot".to_string(),
        image_url: Some(snapshot_data_url),
        image_base64: None,
        camera_id: Some(camera_id),
    }))
}

async fn analyze_vision_context(
    bridge: &PythonBridge,
    req: &ChatRequest,
) -> Result<Option<ChatVisionContext>, MarisError> {
    let Some(input) = resolve_vision_input(bridge, req).await? else {
        return Ok(None);
    };

    let payload = serde_json::json!({
        "image_url": input.image_url,
        "image_base64": input.image_base64,
        "camera_id": input.camera_id,
        "session_id": req.session_id,
        "confidence_threshold": req.confidence_threshold.unwrap_or(0.25),
    });
    let result = bridge
        .call::<VisionAnalysisPayload>("vision/analyze", &payload)
        .await
        .map_err(|err| {
            error!("Vision analysis failed: {err}");
            MarisError::Bridge("Vision analīzes serviss šobrīd nav pieejams.".to_string())
        })?;

    Ok(Some(ChatVisionContext {
        summary: result.summary,
        source: input.source,
        model: result.model,
        fallback_used: result.fallback_used,
        camera_id: input.camera_id,
        image_url: input.image_url,
        width: result.width,
        height: result.height,
        detections: result.detections.len() as u32,
    }))
}

async fn resolve_chat_route(
    bridge: &PythonBridge,
    message: &str,
    session_id: Option<&str>,
    persona_id: Option<&str>,
    has_vision_input: bool,
) -> OrchestrationRoutePayload {
    bridge
        .call::<OrchestrationRoutePayload>(
            "orchestrator/route",
            &serde_json::json!({
                "message": message,
                "session_id": session_id,
                "persona_id": persona_id,
                "has_vision_input": has_vision_input,
            }),
        )
        .await
        .map_err(|err| {
            warn!("Routing bridge call failed, falling back to master route: {err}");
            err
        })
        .unwrap_or_else(|_| default_route())
}

async fn handle_code_handoff(
    bridge: &PythonBridge,
    message: &str,
    fallback_model: Option<&str>,
) -> Result<(String, ChatHandoffPayload), MarisError> {
    let language = infer_code_language(message);
    let context = build_code_context(message, &language);
    let repo_path = infer_repo_path(message);
    let result = bridge
        .call::<CodeGenerationPayload>(
            "code/generate",
            &serde_json::json!({
                "prompt": message,
                "language": language,
                "context": context,
                "repo_path": repo_path,
                "fallback_model": fallback_model,
            }),
        )
        .await
        .map_err(|err| {
            error!("Code handoff failed: {err}");
            MarisError::Bridge("Koda ģenerēšanas serviss šobrīd nav pieejams.".to_string())
        })?;

    let explanation = if result.explanation.is_empty() {
        format!("Sagatavoju {} draftu pēc tava pieprasījuma.", language)
    } else {
        result.explanation
    };
    let files = result
        .files
        .into_iter()
        .map(
            |ProjectFilePayload {
                 path,
                 absolute_path,
                 ..
             }| ChatProjectFilePayload {
                path,
                absolute_path,
            },
        )
        .collect::<Vec<_>>();
    let workspace_artifact_dir = result.workspace_artifact_dir.clone();

    Ok((
        explanation.clone(),
        ChatHandoffPayload::Code {
            code: result.code,
            explanation,
            language: result.language,
            detected_stack: result.detected_stack,
            files,
            workspace_artifact_dir,
            bundle_download_url: bundle_download_url(result.workspace_artifact_dir.as_deref()),
            entrypoint: result.entrypoint,
            repo_path: result.repo_path,
        },
    ))
}

async fn handle_autonomous_handoff(
    bridge: &PythonBridge,
    prepared: &PreparedChatRequest,
) -> Result<(String, ChatHandoffPayload), MarisError> {
    let session_id = prepared
        .session_id
        .clone()
        .unwrap_or_else(|| prepared.route.session_context.session_id.clone());
    let persona_id = prepared
        .persona_id
        .clone()
        .unwrap_or_else(|| prepared.route.session_context.persona_id.clone());
    let result = bridge
        .call::<AutonomousSessionPayload>(
            "autonomous/start",
            &serde_json::json!({
                "session_id": session_id,
                "goal": prepared.message,
                "max_steps": 8,
                "persona_id": persona_id,
            }),
        )
        .await
        .map_err(|err| {
            error!("Autonomous handoff failed: {err}");
            MarisError::Bridge("Autonomās izpildes serviss šobrīd nav pieejams.".to_string())
        })?;

    let tasks = result
        .tasks
        .iter()
        .take(4)
        .map(|task| ChatAutonomousTaskPayload {
            id: task.id.clone(),
            description: task.description.clone(),
            status: task.status.clone(),
            tool: task.tool.clone(),
            result: task.result.clone(),
        })
        .collect::<Vec<_>>();
    let summary = format!(
        "Palaidu autonomo izpildi: {} uzdevumi, progress {}%, statuss {}.",
        result.tasks.len(),
        result.progress_percent,
        result.status
    );

    Ok((
        summary,
        ChatHandoffPayload::Autonomous {
            session_id,
            status: result.status,
            progress_percent: result.progress_percent,
            persona_id: result.persona_id,
            persona_title: result.persona_title,
            tasks,
        },
    ))
}

async fn handle_image_handoff(
    bridge: &PythonBridge,
    message: &str,
) -> Result<(String, ChatHandoffPayload), MarisError> {
    let result = bridge
        .call::<ImageGenerationPayload>(
            "images/generate",
            &serde_json::json!({
                "prompt": message,
                "width": 1024,
                "height": 1024,
                "steps": 30,
            }),
        )
        .await
        .map_err(|err| {
            error!("Image handoff failed: {err}");
            MarisError::Bridge("Attēlu ģenerēšanas serviss šobrīd nav pieejams.".to_string())
        })?;

    Ok((
        "Sagatavoju attēla konceptu pēc tava pieprasījuma.".to_string(),
        ChatHandoffPayload::Image {
            image_url: result.image_url,
            prompt: message.to_string(),
        },
    ))
}

async fn handle_music_handoff(
    bridge: &PythonBridge,
    message: &str,
) -> Result<(String, ChatHandoffPayload), MarisError> {
    let result = bridge
        .call::<MusicGenerationPayload>(
            "audio/generate_music",
            &serde_json::json!({
                "prompt": message,
                "genre": "pop",
                "duration_seconds": 30,
            }),
        )
        .await
        .map_err(|err| {
            error!("Music handoff failed: {err}");
            MarisError::Bridge("Mūzikas ģenerēšanas serviss šobrīd nav pieejams.".to_string())
        })?;

    Ok((
        format!("Sagatavoju mūzikas draftu: {}.", result.title),
        ChatHandoffPayload::Music {
            audio_url: result.audio_url,
            title: result.title,
            duration_seconds: result.duration_seconds,
        },
    ))
}

async fn handle_video_handoff(
    bridge: &PythonBridge,
    message: &str,
) -> Result<(String, ChatHandoffPayload), MarisError> {
    let result = bridge
        .call::<VideoGenerationPayload>(
            "video/generate",
            &serde_json::json!({
                "prompt": message,
                "duration_seconds": 5,
                "fps": 24,
                "resolution": "720p",
            }),
        )
        .await
        .map_err(|err| {
            error!("Video handoff failed: {err}");
            MarisError::Bridge("Video ģenerēšanas serviss šobrīd nav pieejams.".to_string())
        })?;

    Ok((
        "Sagatavoju video draftu pēc tava pieprasījuma.".to_string(),
        ChatHandoffPayload::Video {
            video_url: result.video_url,
            duration_seconds: result.duration_seconds,
            prompt: message.to_string(),
        },
    ))
}

async fn prepare_chat_request(req: ChatRequest) -> Result<PreparedChatRequest, MarisError> {
    let bridge = PythonBridge::new();
    let message = normalize_message(&req.message)?;
    let session_id = normalize_optional_text(req.session_id.clone());
    let persona_id = normalize_optional_text(req.persona_id.clone());
    let vision_context = analyze_vision_context(&bridge, &req).await?;
    let route = resolve_chat_route(
        &bridge,
        &message,
        session_id.as_deref(),
        persona_id.as_deref(),
        vision_context.is_some(),
    )
    .await;
    let route = if is_autonomous_mode_enabled(req.autonomous_mode) {
        force_autonomous_route(route, session_id.as_deref(), persona_id.as_deref())
    } else {
        route
    };

    Ok(PreparedChatRequest {
        message,
        history: req.history.unwrap_or_default(),
        session_id,
        persona_id,
        fallback_model: req.fallback_model,
        route,
        vision_context,
    })
}

async fn execute_prepared_chat(
    prepared: &PreparedChatRequest,
) -> Result<ChatExecutionResult, MarisError> {
    let bridge = PythonBridge::new();
    match prepared.route.capability.as_str() {
        "code_generation" => {
            let (summary, handoff) = handle_code_handoff(
                &bridge,
                &prepared.message,
                prepared.fallback_model.as_deref(),
            )
            .await?;
            let (detected_emotion, emotion_confidence, response_style) = default_emotion_metadata();
            Ok(ChatExecutionResult {
                response: summary,
                model: fallback_chat_model(&prepared.route.capability),
                handoff: Some(handoff),
                vision_context: None,
                detected_emotion,
                emotion_confidence,
                response_style,
                persona_id: None,
                persona_title: None,
                persona_summary: None,
                request_id: None,
                session_id: None,
                latency_ms: None,
                tokens_used: None,
                prompt_messages: None,
                memory_matches: None,
                tool_trace: None,
            })
        }
        "autonomous_tasks" => {
            let (summary, handoff) = handle_autonomous_handoff(&bridge, prepared).await?;
            let (detected_emotion, emotion_confidence, response_style) = default_emotion_metadata();
            Ok(ChatExecutionResult {
                response: summary,
                model: fallback_chat_model(&prepared.route.capability),
                handoff: Some(handoff),
                vision_context: None,
                detected_emotion,
                emotion_confidence,
                response_style,
                persona_id: prepared.persona_id.clone(),
                persona_title: None,
                persona_summary: None,
                request_id: None,
                session_id: prepared.session_id.clone(),
                latency_ms: None,
                tokens_used: None,
                prompt_messages: None,
                memory_matches: None,
                tool_trace: None,
            })
        }
        "image_generation" => {
            let (summary, handoff) = handle_image_handoff(&bridge, &prepared.message).await?;
            let (detected_emotion, emotion_confidence, response_style) = default_emotion_metadata();
            Ok(ChatExecutionResult {
                response: summary,
                model: fallback_chat_model(&prepared.route.capability),
                handoff: Some(handoff),
                vision_context: None,
                detected_emotion,
                emotion_confidence,
                response_style,
                persona_id: None,
                persona_title: None,
                persona_summary: None,
                request_id: None,
                session_id: None,
                latency_ms: None,
                tokens_used: None,
                prompt_messages: None,
                memory_matches: None,
                tool_trace: None,
            })
        }
        "music_generation" => {
            let (summary, handoff) = handle_music_handoff(&bridge, &prepared.message).await?;
            let (detected_emotion, emotion_confidence, response_style) = default_emotion_metadata();
            Ok(ChatExecutionResult {
                response: summary,
                model: fallback_chat_model(&prepared.route.capability),
                handoff: Some(handoff),
                vision_context: None,
                detected_emotion,
                emotion_confidence,
                response_style,
                persona_id: None,
                persona_title: None,
                persona_summary: None,
                request_id: None,
                session_id: None,
                latency_ms: None,
                tokens_used: None,
                prompt_messages: None,
                memory_matches: None,
                tool_trace: None,
            })
        }
        "video_generation" => {
            let (summary, handoff) = handle_video_handoff(&bridge, &prepared.message).await?;
            let (detected_emotion, emotion_confidence, response_style) = default_emotion_metadata();
            Ok(ChatExecutionResult {
                response: summary,
                model: fallback_chat_model(&prepared.route.capability),
                handoff: Some(handoff),
                vision_context: None,
                detected_emotion,
                emotion_confidence,
                response_style,
                persona_id: None,
                persona_title: None,
                persona_summary: None,
                request_id: None,
                session_id: None,
                latency_ms: None,
                tokens_used: None,
                prompt_messages: None,
                memory_matches: None,
                tool_trace: None,
            })
        }
        _ => {
            let payload = build_text_generation_payload(prepared);

            let result = bridge
                .call::<TextGenerationPayload>("text/generate", &payload)
                .await
                .map_err(|err| {
                    error!("Chat bridge call failed: {err}");
                    MarisError::Bridge(
                        "Čata serviss šobrīd nav pieejams. Lūdzu mēģiniet vēlreiz.".to_string(),
                    )
                })?;
            Ok(ChatExecutionResult {
                response: result.response,
                model: if result.model.is_empty() {
                    fallback_chat_model(&prepared.route.capability)
                } else {
                    result.model
                },
                handoff: None,
                vision_context: prepared.vision_context.clone(),
                detected_emotion: result.detected_emotion,
                emotion_confidence: result.emotion_confidence,
                response_style: result.response_style,
                persona_id: (!result.persona_id.is_empty()).then_some(result.persona_id),
                persona_title: (!result.persona_title.is_empty()).then_some(result.persona_title),
                persona_summary: (!result.persona_summary.is_empty())
                    .then_some(result.persona_summary),
                request_id: (!result.request_id.is_empty()).then_some(result.request_id),
                session_id: (!result.session_id.is_empty()).then_some(result.session_id),
                latency_ms: (result.latency_ms > 0).then_some(result.latency_ms),
                tokens_used: (result.tokens_used > 0).then_some(result.tokens_used),
                prompt_messages: (result.prompt_messages > 0).then_some(result.prompt_messages),
                memory_matches: (result.memory_matches > 0).then_some(result.memory_matches),
                tool_trace: result.tool_trace,
            })
        }
    }
}

fn build_text_generation_payload(prepared: &PreparedChatRequest) -> serde_json::Value {
    serde_json::json!({
        "message": &prepared.message,
        "history": &prepared.history,
        "profile": &prepared.route.profile,
        "persona_id": &prepared.persona_id,
        "session_id": &prepared.session_id,
        "fallback_model": &prepared.fallback_model,
        "vision_context": prepared.vision_context.as_ref().map(|context| {
            serde_json::json!({
                "summary": &context.summary,
                "source": &context.source,
                "model": &context.model,
                "fallback_used": context.fallback_used,
                "camera_id": &context.camera_id,
                "image_url": &context.image_url,
                "width": context.width,
                "height": context.height,
                "detections": context.detections,
            })
        }),
    })
}

fn parse_core_text_stream_event(
    raw_event: &str,
) -> Result<Option<CoreTextStreamEvent>, MarisError> {
    let normalized = if raw_event.contains("\r\n") {
        Cow::Owned(raw_event.replace("\r\n", "\n"))
    } else {
        Cow::Borrowed(raw_event)
    };
    let trimmed = normalized.trim();
    if trimmed.is_empty() || trimmed.starts_with(':') {
        return Ok(None);
    }

    let mut event_name = "message";
    let mut data_lines = Vec::new();

    for line in normalized.lines() {
        if let Some(value) = line.strip_prefix("event:") {
            event_name = value.trim();
            continue;
        }
        if let Some(value) = line.strip_prefix("data:") {
            data_lines.push(value.trim_start());
        }
    }

    if data_lines.is_empty() {
        return Ok(None);
    }

    let payload = data_lines.join("\n");
    match event_name {
        "delta" => {
            let parsed: CoreTextDeltaPayload = serde_json::from_str(&payload).map_err(|err| {
                MarisError::Bridge(format!("Neizdevās parsēt core text delta eventu: {err}"))
            })?;
            Ok(parsed.delta.map(CoreTextStreamEvent::Delta))
        }
        "complete" => {
            let parsed: TextGenerationPayload = serde_json::from_str(&payload).map_err(|err| {
                MarisError::Bridge(format!("Neizdevās parsēt core text complete eventu: {err}"))
            })?;
            Ok(Some(CoreTextStreamEvent::Complete(Box::new(
                parsed.validate().map_err(|err| {
                    MarisError::Bridge(format!("Core text stream atgrieza nederīgu payload: {err}"))
                })?,
            ))))
        }
        "error" => {
            let parsed: CoreTextErrorPayload = serde_json::from_str(&payload).map_err(|err| {
                MarisError::Bridge(format!("Neizdevās parsēt core text error eventu: {err}"))
            })?;
            Ok(Some(CoreTextStreamEvent::Error(
                parsed
                    .error
                    .unwrap_or_else(|| "Core text stream kļūda.".to_string()),
            )))
        }
        _ => Ok(None),
    }
}

fn take_next_core_text_stream_frame(buffer: &mut String) -> Option<String> {
    if buffer.contains("\r\n") {
        *buffer = buffer.replace("\r\n", "\n");
    }
    if buffer.contains('\r') {
        *buffer = buffer.replace('\r', "\n");
    }

    let index = buffer.find("\n\n")?;
    let raw_event = buffer[..index].to_string();
    *buffer = buffer[index + 2..].to_string();
    Some(raw_event)
}

fn build_stream_text_chat_result(
    prepared: &PreparedChatRequest,
    result: TextGenerationPayload,
) -> ChatExecutionResult {
    ChatExecutionResult {
        response: result.response,
        model: if result.model.is_empty() {
            fallback_chat_model(&prepared.route.capability)
        } else {
            result.model
        },
        handoff: None,
        vision_context: prepared.vision_context.clone(),
        detected_emotion: result.detected_emotion,
        emotion_confidence: result.emotion_confidence,
        response_style: result.response_style,
        persona_id: (!result.persona_id.is_empty()).then_some(result.persona_id),
        persona_title: (!result.persona_title.is_empty()).then_some(result.persona_title),
        persona_summary: (!result.persona_summary.is_empty()).then_some(result.persona_summary),
        request_id: (!result.request_id.is_empty()).then_some(result.request_id),
        session_id: (!result.session_id.is_empty()).then_some(result.session_id),
        latency_ms: (result.latency_ms > 0).then_some(result.latency_ms),
        tokens_used: (result.tokens_used > 0).then_some(result.tokens_used),
        prompt_messages: (result.prompt_messages > 0).then_some(result.prompt_messages),
        memory_matches: (result.memory_matches > 0).then_some(result.memory_matches),
        tool_trace: result.tool_trace,
    }
}

async fn stream_text_chat_response(
    bridge: &PythonBridge,
    prepared: &PreparedChatRequest,
    tx: &mpsc::Sender<Result<Event, Infallible>>,
) -> Result<Option<ChatExecutionResult>, MarisError> {
    let payload = build_text_generation_payload(prepared);
    let response = bridge
        .stream("text/generate/stream", &payload)
        .await
        .map_err(|err| {
            error!("Chat bridge stream call failed: {err}");
            MarisError::Bridge(
                "Čata serviss šobrīd nav pieejams. Lūdzu mēģiniet vēlreiz.".to_string(),
            )
        })?;

    let mut stream = response.bytes_stream();
    let mut buffer = String::new();

    while let Some(next_chunk) = tokio::select! {
        _ = tx.closed() => return Ok(None),
        next = stream.next() => next,
    } {
        let chunk = next_chunk.map_err(|err| {
            MarisError::Bridge(format!("Neizdevās nolasīt core text straumi: {err}"))
        })?;
        buffer.push_str(&String::from_utf8_lossy(&chunk));

        while let Some(raw_event) = take_next_core_text_stream_frame(&mut buffer) {
            match parse_core_text_stream_event(&raw_event)? {
                Some(CoreTextStreamEvent::Delta(delta)) => {
                    if !try_send_stream_event(
                        tx,
                        build_sse_event("delta", &serde_json::json!({ "delta": delta })),
                    )
                    .await
                    {
                        return Ok(None);
                    }
                }
                Some(CoreTextStreamEvent::Complete(payload)) => {
                    return Ok(Some(build_stream_text_chat_result(prepared, *payload)));
                }
                Some(CoreTextStreamEvent::Error(message)) => {
                    return Err(MarisError::Bridge(message));
                }
                None => {}
            }
        }
    }

    if !buffer.trim().is_empty() {
        match parse_core_text_stream_event(&buffer)? {
            Some(CoreTextStreamEvent::Delta(delta)) => {
                if !try_send_stream_event(
                    tx,
                    build_sse_event("delta", &serde_json::json!({ "delta": delta })),
                )
                .await
                {
                    return Ok(None);
                }
            }
            Some(CoreTextStreamEvent::Complete(payload)) => {
                return Ok(Some(build_stream_text_chat_result(prepared, *payload)));
            }
            Some(CoreTextStreamEvent::Error(message)) => {
                return Err(MarisError::Bridge(message));
            }
            None => {}
        }
    }

    Err(MarisError::Bridge(
        "Core text straume noslēdzās bez final payload.".to_string(),
    ))
}

fn build_chat_response(
    route: OrchestrationRoutePayload,
    chat_result: ChatExecutionResult,
) -> ChatResponse {
    ChatResponse {
        response: chat_result.response,
        model: chat_result.model,
        route: ChatRouteInfo {
            capability: route.capability,
            branch: route.branch,
            profile: route.profile,
            studio: route.target_studio,
            reasoning: route.reasoning,
            confidence: route.confidence,
        },
        handoff: chat_result.handoff,
        vision_context: chat_result.vision_context,
        detected_emotion: chat_result.detected_emotion,
        emotion_confidence: chat_result.emotion_confidence,
        response_style: chat_result.response_style,
        persona_id: chat_result.persona_id,
        persona_title: chat_result.persona_title,
        persona_summary: chat_result.persona_summary,
        request_id: chat_result.request_id,
        session_id: chat_result.session_id,
        latency_ms: chat_result.latency_ms,
        tokens_used: chat_result.tokens_used,
        prompt_messages: chat_result.prompt_messages,
        memory_matches: chat_result.memory_matches,
        tool_trace: chat_result.tool_trace,
    }
}

fn build_sse_event<T: Serialize>(event_name: &str, payload: &T) -> Event {
    Event::default()
        .event(event_name)
        .data(serde_json::to_string(payload).unwrap_or_else(|_| "{}".to_string()))
}

fn build_status_event(stage: &str, message: &str) -> Event {
    build_sse_event(
        "status",
        &ChatStatusPayload {
            stage: stage.to_string(),
            message: message.to_string(),
        },
    )
}

async fn try_send_stream_event(tx: &mpsc::Sender<Result<Event, Infallible>>, event: Event) -> bool {
    tx.send(Ok(event)).await.is_ok()
}

fn chunk_response_for_streaming(text: &str) -> Vec<String> {
    let mut chunks = Vec::new();
    let mut current = String::new();
    let mut last_boundary = None;

    for ch in text.chars() {
        current.push(ch);
        if ch.is_whitespace() {
            last_boundary = Some(current.len());
        }
        if current.len() >= CHAT_STREAM_CHUNK_CHARS {
            if let Some(boundary) = last_boundary
                .take()
                .filter(|boundary| *boundary < current.len())
            {
                let remainder = current.split_off(boundary);
                chunks.push(std::mem::take(&mut current));
                current = remainder;
            } else {
                chunks.push(std::mem::take(&mut current));
            }
        }
    }

    if !current.is_empty() {
        chunks.push(current);
    }

    chunks
}

fn build_stream_delta_events(response: &str) -> VecDeque<Event> {
    chunk_response_for_streaming(response)
        .into_iter()
        .map(|delta| build_sse_event("delta", &serde_json::json!({ "delta": delta })))
        .collect()
}

pub async fn chat(Json(req): Json<ChatRequest>) -> Result<Json<ChatResponse>, MarisError> {
    let prepared = prepare_chat_request(req).await?;
    let chat_result = execute_prepared_chat(&prepared).await?;
    Ok(Json(build_chat_response(prepared.route, chat_result)))
}

pub async fn chat_stream(
    Json(req): Json<ChatRequest>,
) -> Result<Sse<impl futures_util::Stream<Item = Result<Event, Infallible>>>, MarisError> {
    let message = normalize_message(&req.message)?;
    let (tx, rx) = mpsc::channel::<Result<Event, Infallible>>(32);
    let history = req.history.clone().unwrap_or_default();

    tokio::spawn(async move {
        let bridge = PythonBridge::new();
        let session_id = normalize_optional_text(req.session_id.clone());
        let persona_id = normalize_optional_text(req.persona_id.clone());
        let autonomous_mode = is_autonomous_mode_enabled(req.autonomous_mode);
        let fallback_model = normalize_optional_text(req.fallback_model.clone());

        if !try_send_stream_event(
            &tx,
            build_status_event("presence", "Savienojums izveidots — Maris ir klāt."),
        )
        .await
        {
            return;
        }

        let has_vision_input = req.image_url.is_some()
            || req.image_base64.is_some()
            || req
                .camera_id
                .as_ref()
                .is_some_and(|camera_id| !camera_id.trim().is_empty());

        if has_vision_input
            && !try_send_stream_event(
                &tx,
                build_status_event("vision", "Analizēju vizuālo kontekstu."),
            )
            .await
        {
            return;
        }

        let vision_context = match analyze_vision_context(&bridge, &req).await {
            Ok(vision_context) => vision_context,
            Err(err) => {
                let _ = try_send_stream_event(
                    &tx,
                    build_sse_event("error", &serde_json::json!({ "error": err.to_string() })),
                )
                .await;
                return;
            }
        };

        if let Some(vision_context) = vision_context.as_ref() {
            if !try_send_stream_event(&tx, build_sse_event("vision_context", vision_context)).await
            {
                return;
            }
        }

        if !try_send_stream_event(
            &tx,
            build_status_event("routing", "Izvēlos labāko atbildes režīmu."),
        )
        .await
        {
            return;
        }

        let route = resolve_chat_route(
            &bridge,
            &message,
            session_id.as_deref(),
            persona_id.as_deref(),
            vision_context.is_some(),
        )
        .await;
        let route = if autonomous_mode {
            force_autonomous_route(route, session_id.as_deref(), persona_id.as_deref())
        } else {
            route
        };

        if !try_send_stream_event(&tx, build_sse_event("route", &route)).await {
            return;
        }

        if !try_send_stream_event(
            &tx,
            build_status_event("generation", generation_status_message(&route.capability)),
        )
        .await
        {
            return;
        }

        let prepared = PreparedChatRequest {
            message,
            history,
            session_id,
            persona_id,
            fallback_model,
            route,
            vision_context,
        };

        if prepared.route.capability == "text_chat" {
            match stream_text_chat_response(&bridge, &prepared, &tx).await {
                Ok(Some(chat_result)) => {
                    let response = build_chat_response(prepared.route, chat_result);
                    let _ =
                        try_send_stream_event(&tx, build_sse_event("complete", &response)).await;
                }
                Ok(None) => {}
                Err(err) => {
                    let _ = try_send_stream_event(
                        &tx,
                        build_sse_event("error", &serde_json::json!({ "error": err.to_string() })),
                    )
                    .await;
                }
            }
            return;
        }

        match execute_prepared_chat(&prepared).await {
            Ok(chat_result) => {
                let response = build_chat_response(prepared.route, chat_result);
                for delta in build_stream_delta_events(&response.response) {
                    if !try_send_stream_event(&tx, delta).await {
                        return;
                    }
                    tokio::time::sleep(Duration::from_millis(CHAT_STREAM_INTERVAL_MS)).await;
                }
                let _ = try_send_stream_event(&tx, build_sse_event("complete", &response)).await;
            }
            Err(err) => {
                let _ = try_send_stream_event(
                    &tx,
                    build_sse_event("error", &serde_json::json!({ "error": err.to_string() })),
                )
                .await;
            }
        }
    });

    let stream = stream::unfold(rx, |mut rx| async move {
        rx.recv().await.map(|event| (event, rx))
    });

    Ok(Sse::new(stream).keep_alive(
        KeepAlive::new()
            .interval(Duration::from_secs(10))
            .text("keepalive"),
    ))
}

#[cfg(test)]
mod tests {
    use super::{
        chunk_response_for_streaming, generation_status_message, normalize_message,
        parse_core_text_stream_event, take_next_core_text_stream_frame, CoreTextStreamEvent,
    };
    use crate::utils::errors::MarisError;

    #[test]
    fn rejects_empty_messages() {
        let err = normalize_message("   ").unwrap_err();
        assert!(matches!(err, MarisError::Validation(_)));
        assert_eq!(err.to_string(), "Derīguma kļūda: Ziņa nedrīkst būt tukša.");
    }

    #[test]
    fn trims_valid_messages() {
        assert_eq!(
            normalize_message("  Sveiki, Maris!  ").unwrap(),
            "Sveiki, Maris!"
        );
    }

    #[test]
    fn chunks_streaming_response_without_losing_text() {
        let text =
            "Šī ir garāka atbilde ar vairākiem vārdiem, lai pārbaudītu straumēšanas chunkus.";
        let chunks = chunk_response_for_streaming(text);
        assert!(chunks.len() >= 2);
        assert_eq!(chunks.concat(), text);
    }

    #[test]
    fn maps_generation_status_by_capability() {
        assert_eq!(
            generation_status_message("code_generation"),
            "Veidoju koda draftu."
        );
        assert_eq!(
            generation_status_message("text_chat"),
            "Domāju un veidoju atbildi."
        );
    }

    #[test]
    fn parses_core_text_delta_event() {
        let event = parse_core_text_stream_event("event: delta\ndata: {\"delta\":\"Sveiki\"}\n")
            .unwrap()
            .unwrap();

        match event {
            CoreTextStreamEvent::Delta(delta) => assert_eq!(delta, "Sveiki"),
            _ => panic!("expected delta event"),
        }
    }

    #[test]
    fn parses_core_text_complete_event() {
        let event = parse_core_text_stream_event(
            "event: complete\ndata: {\"response\":\"Sveiki\",\"model\":\"m\",\"detected_emotion\":\"neutral\",\"emotion_confidence\":0.1,\"response_style\":\"clear_grounded\"}\n",
        )
        .unwrap()
        .unwrap();

        match event {
            CoreTextStreamEvent::Complete(payload) => {
                assert_eq!(payload.response, "Sveiki");
                assert_eq!(payload.model, "m");
            }
            _ => panic!("expected complete event"),
        }
    }

    #[test]
    fn extracts_multiple_core_text_events_with_crlf_delimiters() {
        let mut buffer = concat!(
            "event: delta\r\n",
            "data: {\"delta\":\"Sveiki\"}\r\n\r\n",
            "event: complete\r\n",
            "data: {\"response\":\"Sveiki\",\"model\":\"m\",\"detected_emotion\":\"neutral\",\"emotion_confidence\":0.1,\"response_style\":\"clear_grounded\"}\r\n\r\n"
        )
        .to_string();

        let first = take_next_core_text_stream_frame(&mut buffer).expect("first event");
        let second = take_next_core_text_stream_frame(&mut buffer).expect("second event");

        assert!(buffer.is_empty());
        assert!(matches!(
            parse_core_text_stream_event(&first).unwrap(),
            Some(CoreTextStreamEvent::Delta(delta)) if delta == "Sveiki"
        ));
        assert!(matches!(
            parse_core_text_stream_event(&second).unwrap(),
            Some(CoreTextStreamEvent::Complete(payload)) if payload.response == "Sveiki"
        ));
    }
}