use models::projects::project_skill;
use models::rooms::room_ai;
use sea_orm::{ColumnTrait, EntityTrait, QueryFilter};
use uuid::Uuid;

use super::AiRequest;
use super::message_builder::MessageBuilder;
use super::service::ProcessResult;
use super::session_recording::record_ai_session;
use crate::client::AiClientConfig;
use crate::client::types::{ChatRequestMessage, ToolCall};
use crate::error::Result;
use crate::perception::{SkillEntry, ToolCallEvent};
use crate::tool::{ToolCall as AgentToolCall, ToolContext, ToolExecutor};

pub async fn execute_process(
    request: AiRequest,
    message_builder: &MessageBuilder,
    tool_registry: &Option<crate::tool::registry::ToolRegistry>,
    ai_base_url: Option<String>,
    ai_api_key: Option<String>,
) -> Result<ProcessResult> {
    let tools: Vec<serde_json::Value> = request.tools.clone().unwrap_or_default();
    let tools_enabled = !tools.is_empty();
    let max_tool_depth = request.max_tool_depth;

    let mut messages = message_builder.build_messages(&request).await?;

    let room_ai_config = room_ai::Entity::find()
        .filter(room_ai::Column::Room.eq(request.room.id))
        .filter(room_ai::Column::Model.eq(request.model.id))
        .one(&request.db)
        .await?;

    let model_name = request.model.name.clone();
    let temperature = room_ai_config
        .as_ref()
        .and_then(|r| r.temperature.map(|v| v as f32))
        .unwrap_or(request.temperature as f32);
    let max_tokens = room_ai_config
        .as_ref()
        .and_then(|r| r.max_tokens.map(|v| v as u32))
        .unwrap_or(request.max_tokens as u32);
    let mut tool_depth = 0;
    let mut input_tokens = 0i64;
    let mut output_tokens = 0i64;
    let session_id = Uuid::now_v7();
    let session_start = std::time::Instant::now();
    let version_id = room_ai_config.as_ref().and_then(|r| r.version);

    let config = AiClientConfig::new(ai_api_key.unwrap_or_default())
        .with_base_url(ai_base_url.unwrap_or_else(|| "https://api.openai.com".into()));

    loop {
        let response = crate::client::call_with_params(
            &messages,
            &model_name,
            &config,
            temperature,
            max_tokens,
            None,
            if tools_enabled { Some(&tools) } else { None },
            if tools_enabled { None } else { Some("none") },
        )
        .await?;

        let text = response.content.clone();
        input_tokens += response.input_tokens;
        output_tokens += response.output_tokens;

        if tools_enabled && !response.tool_calls_finished.is_empty() {
            let tool_call_messages: Vec<_> = response
                .tool_calls_finished
                .iter()
                .map(|name| ToolCall {
                    id: Uuid::new_v4().to_string(),
                    type_: "function".into(),
                    function: crate::client::types::ToolCallFunction {
                        name: name.clone(),
                        arguments: "{}".into(),
                    },
                })
                .collect();

            messages.push(ChatRequestMessage::assistant(
                Some(text.clone()),
                Some(tool_call_messages.clone()),
            ));

            let calls: Vec<AgentToolCall> = tool_call_messages
                .into_iter()
                .map(|tc| AgentToolCall {
                    id: tc.id.clone(),
                    name: tc.function.name.clone(),
                    arguments: tc.function.arguments.clone(),
                })
                .collect();

            let tool_messages = execute_tools(
                &request,
                &calls,
                session_id,
                &response.tool_calls_finished,
                tool_registry,
                message_builder,
            )
            .await;
            messages.extend(tool_messages);
            inject_passive_skills(
                &request,
                message_builder,
                &response.tool_calls_finished,
                &mut messages,
            )
            .await;

            tool_depth += 1;
            if tool_depth >= max_tool_depth {
                let content = if text.is_empty() {
                    format!(
                        "[AI reached maximum tool depth ({}) — no final answer produced]",
                        max_tool_depth
                    )
                } else {
                    text
                };
                record_ai_session(
                    &request.cache,
                    &request.db,
                    request.project.id,
                    request.sender.uid,
                    session_id,
                    request.room.id,
                    request.model.id,
                    version_id.unwrap_or_default(),
                    input_tokens,
                    output_tokens,
                    session_start.elapsed().as_millis() as i64,
                )
                .await;
                return Ok(ProcessResult {
                    content,
                    input_tokens,
                    output_tokens,
                });
            }
            continue;
        }

        record_ai_session(
            &request.cache,
            &request.db,
            request.project.id,
            request.sender.uid,
            session_id,
            request.room.id,
            request.model.id,
            version_id.unwrap_or_default(),
            input_tokens,
            output_tokens,
            session_start.elapsed().as_millis() as i64,
        )
        .await;
        return Ok(ProcessResult {
            content: text,
            input_tokens,
            output_tokens,
        });
    }
}

async fn execute_tools(
    request: &AiRequest,
    calls: &[AgentToolCall],
    session_id: Uuid,
    tool_names: &[String],
    tool_registry: &Option<crate::tool::registry::ToolRegistry>,
    message_builder: &MessageBuilder,
) -> Vec<ChatRequestMessage> {
    let mut ctx = ToolContext::new(
        request.db.clone(),
        request.cache.clone(),
        request.config.clone(),
        request.room.id,
        Some(request.sender.uid),
    )
    .with_project(request.project.id);
    if let Some(ref es) = message_builder.embed_service {
        ctx = ctx.with_embed_service(es.clone());
    }
    if let Some(registry) = tool_registry {
        ctx.registry_mut().merge(registry.clone());
    }

    let recorder =
        crate::tool::recorder::ToolCallRecorder::with_session(request.db.clone(), session_id);
    let start = std::time::Instant::now();
    let executor = ToolExecutor::new();
    match executor.execute_batch(calls.to_vec(), &mut ctx).await {
        Ok(results) => {
            for (call, result) in tool_names.iter().zip(results.iter()) {
                let elapsed = start.elapsed().as_millis() as i64;
                let is_error = matches!(result.result, crate::tool::ToolResult::Error(_));
                let error_msg = match &result.result {
                    crate::tool::ToolResult::Error(msg) => Some(msg.clone()),
                    _ => None,
                };
                recorder.record(crate::tool::recorder::ToolCallRecord {
                    tool_call_id: Uuid::new_v4().to_string(),
                    session_id: recorder.session_id(),
                    tool_name: call.clone(),
                    caller: request.sender.uid,
                    arguments: serde_json::Value::Null,
                    status: if is_error {
                        models::ai::ToolCallStatus::Failed
                    } else {
                        models::ai::ToolCallStatus::Success
                    },
                    execution_time_ms: Some(elapsed),
                    error_message: error_msg,
                    error_stack: None,
                    retry_count: 0,
                });
            }
            crate::tool::ToolExecutor::to_tool_messages(&results)
        }
        Err(e) => {
            let elapsed = start.elapsed().as_millis() as i64;
            for call_name in tool_names {
                recorder.record(crate::tool::recorder::ToolCallRecord {
                    tool_call_id: Uuid::new_v4().to_string(),
                    session_id: recorder.session_id(),
                    tool_name: call_name.clone(),
                    caller: request.sender.uid,
                    arguments: serde_json::Value::Null,
                    status: models::ai::ToolCallStatus::Failed,
                    execution_time_ms: Some(elapsed),
                    error_message: Some(e.to_string()),
                    error_stack: None,
                    retry_count: 0,
                });
            }
            let err_msg = format!("[Tool call failed: {}]", e);
            tool_names
                .iter()
                .map(|_| ChatRequestMessage::tool(Uuid::new_v4().to_string(), &err_msg))
                .collect()
        }
    }
}

async fn inject_passive_skills(
    request: &AiRequest,
    message_builder: &MessageBuilder,
    tool_names: &[String],
    messages: &mut Vec<ChatRequestMessage>,
) {
    if let Ok(skills) = project_skill::Entity::find()
        .filter(project_skill::Column::ProjectUuid.eq(request.project.id))
        .filter(project_skill::Column::Enabled.eq(true))
        .all(&request.db)
        .await
    {
        let skill_entries: Vec<SkillEntry> = skills
            .into_iter()
            .map(|s| SkillEntry {
                slug: s.slug,
                name: s.name,
                description: s.description,
                content: s.content,
            })
            .collect();
        let tool_events: Vec<ToolCallEvent> = tool_names
            .iter()
            .map(|name| ToolCallEvent {
                tool_name: name.clone(),
                arguments: String::new(),
            })
            .collect();
        for event in &tool_events {
            if let Some(ctx) = message_builder
                .perception_service
                .passive
                .detect(event, &skill_entries)
            {
                messages.push(ctx.to_system_message());
            }
        }
    }
}