pub mod llm_generic;
pub mod llm_local;
pub mod llm_provider;

pub use llm_provider::*;

use actix_web::{post, web, HttpRequest, HttpResponse, Result};
use dotenv::dotenv;
use log::{error, info};
use reqwest::Client;
use serde::{Deserialize, Serialize};
use std::env;
use tokio::time::{sleep, Duration};

#[derive(Debug, Serialize, Deserialize)]
struct ChatMessage {
    role: String,
    content: String,
}

#[derive(Debug, Serialize, Deserialize)]
struct ChatCompletionRequest {
    model: String,
    messages: Vec<ChatMessage>,
    stream: Option<bool>,
}

#[derive(Debug, Serialize, Deserialize)]
struct ChatCompletionResponse {
    id: String,
    object: String,
    created: u64,
    model: String,
    choices: Vec<Choice>,
}

#[derive(Debug, Serialize, Deserialize)]
struct Choice {
    message: ChatMessage,
    finish_reason: String,
}

#[derive(Debug, Serialize, Deserialize)]
struct LlamaCppRequest {
    prompt: String,
    n_predict: Option<i32>,
    temperature: Option<f32>,
    top_k: Option<i32>,
    top_p: Option<f32>,
    stream: Option<bool>,
}

#[derive(Debug, Serialize, Deserialize)]
struct LlamaCppResponse {
    content: String,
    stop: bool,
    generation_settings: Option<serde_json::Value>,
}

pub async fn ensure_llama_servers_running() -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
    let llm_local = env::var("LLM_LOCAL").unwrap_or_else(|_| "false".to_string());

    if llm_local.to_lowercase() != "true" {
        info!("ℹ️  LLM_LOCAL is not enabled, skipping local server startup");
        return Ok(());
    }

    let llm_url = env::var("LLM_URL").unwrap_or_else(|_| "http://localhost:8081".to_string());
    let embedding_url =
        env::var("EMBEDDING_URL").unwrap_or_else(|_| "http://localhost:8082".to_string());
    let llama_cpp_path = env::var("LLM_CPP_PATH").unwrap_or_else(|_| "~/llama.cpp".to_string());
    let llm_model_path = env::var("LLM_MODEL_PATH").unwrap_or_else(|_| "".to_string());
    let embedding_model_path = env::var("EMBEDDING_MODEL_PATH").unwrap_or_else(|_| "".to_string());

    info!("🚀 Starting local llama.cpp servers...");
    info!("📋 Configuration:");
    info!("   LLM URL: {}", llm_url);
    info!("   Embedding URL: {}", embedding_url);
    info!("   LLM Model: {}", llm_model_path);
    info!("   Embedding Model: {}", embedding_model_path);

    let llm_running = is_server_running(&llm_url).await;
    let embedding_running = is_server_running(&embedding_url).await;

    if llm_running && embedding_running {
        info!("✅ Both LLM and Embedding servers are already running");
        return Ok(());
    }

    let mut tasks = vec![];

    if !llm_running && !llm_model_path.is_empty() {
        info!("🔄 Starting LLM server...");
        tasks.push(tokio::spawn(start_llm_server(
            llama_cpp_path.clone(),
            llm_model_path.clone(),
            llm_url.clone(),
        )));
    } else if llm_model_path.is_empty() {
        info!("⚠️  LLM_MODEL_PATH not set, skipping LLM server");
    }

    if !embedding_running && !embedding_model_path.is_empty() {
        info!("🔄 Starting Embedding server...");
        tasks.push(tokio::spawn(start_embedding_server(
            llama_cpp_path.clone(),
            embedding_model_path.clone(),
            embedding_url.clone(),
        )));
    } else if embedding_model_path.is_empty() {
        info!("⚠️  EMBEDDING_MODEL_PATH not set, skipping Embedding server");
    }

    for task in tasks {
        task.await??;
    }

    info!("⏳ Waiting for servers to become ready...");

    let mut llm_ready = llm_running || llm_model_path.is_empty();
    let mut embedding_ready = embedding_running || embedding_model_path.is_empty();

    let mut attempts = 0;
    let max_attempts = 60;

    while attempts < max_attempts && (!llm_ready || !embedding_ready) {
        sleep(Duration::from_secs(2)).await;

        info!(
            "🔍 Checking server health (attempt {}/{})...",
            attempts + 1,
            max_attempts
        );

        if !llm_ready && !llm_model_path.is_empty() {
            if is_server_running(&llm_url).await {
                info!("   ✅ LLM server ready at {}", llm_url);
                llm_ready = true;
            } else {
                info!("   ❌ LLM server not ready yet");
            }
        }

        if !embedding_ready && !embedding_model_path.is_empty() {
            if is_server_running(&embedding_url).await {
                info!("   ✅ Embedding server ready at {}", embedding_url);
                embedding_ready = true;
            } else {
                info!("   ❌ Embedding server not ready yet");
            }
        }

        attempts += 1;

        if attempts % 10 == 0 {
            info!(
                "⏰ Still waiting for servers... (attempt {}/{})",
                attempts, max_attempts
            );
        }
    }

    if llm_ready && embedding_ready {
        info!("🎉 All llama.cpp servers are ready and responding!");
        Ok(())
    } else {
        let mut error_msg = "❌ Servers failed to start within timeout:".to_string();
        if !llm_ready && !llm_model_path.is_empty() {
            error_msg.push_str(&format!("\n   - LLM server at {}", llm_url));
        }
        if !embedding_ready && !embedding_model_path.is_empty() {
            error_msg.push_str(&format!("\n   - Embedding server at {}", embedding_url));
        }
        Err(error_msg.into())
    }
}

async fn start_llm_server(
    llama_cpp_path: String,
    model_path: String,
    url: String,
) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
    let port = url.split(':').last().unwrap_or("8081");

    std::env::set_var("OMP_NUM_THREADS", "20");
    std::env::set_var("OMP_PLACES", "cores");
    std::env::set_var("OMP_PROC_BIND", "close");

    let mut cmd = tokio::process::Command::new("sh");
    cmd.arg("-c").arg(format!(
        "cd {} && ./llama-server -m {} --host 0.0.0.0 --port {} --n-gpu-layers 99 &",
        llama_cpp_path, model_path, port
    ));

    cmd.spawn()?;
    Ok(())
}

async fn start_embedding_server(
    llama_cpp_path: String,
    model_path: String,
    url: String,
) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
    let port = url.split(':').last().unwrap_or("8082");

    let mut cmd = tokio::process::Command::new("sh");
    cmd.arg("-c").arg(format!(
        "cd {} && ./llama-server -m {} --host 0.0.0.0 --port {} --embedding --n-gpu-layers 99 &",
        llama_cpp_path, model_path, port
    ));

    cmd.spawn()?;
    Ok(())
}

async fn is_server_running(url: &str) -> bool {
    let client = reqwest::Client::new();
    match client.get(&format!("{}/health", url)).send().await {
        Ok(response) => response.status().is_success(),
        Err(_) => false,
    }
}

fn messages_to_prompt(messages: &[ChatMessage]) -> String {
    let mut prompt = String::new();

    for message in messages {
        match message.role.as_str() {
            "system" => {
                prompt.push_str(&format!("System: {}\n\n", message.content));
            }
            "user" => {
                prompt.push_str(&format!("User: {}\n\n", message.content));
            }
            "assistant" => {
                prompt.push_str(&format!("Assistant: {}\n\n", message.content));
            }
            _ => {
                prompt.push_str(&format!("{}: {}\n\n", message.role, message.content));
            }
        }
    }

    prompt.push_str("Assistant: ");
    prompt
}

#[post("/local/v1/chat/completions")]
pub async fn chat_completions_local(
    req_body: web::Json<ChatCompletionRequest>,
    _req: HttpRequest,
) -> Result<HttpResponse> {
    dotenv().ok();

    let llama_url = env::var("LLM_URL").unwrap_or_else(|_| "http://localhost:8081".to_string());

    let prompt = messages_to_prompt(&req_body.messages);

    let llama_request = LlamaCppRequest {
        prompt,
        n_predict: Some(500),
        temperature: Some(0.7),
        top_k: Some(40),
        top_p: Some(0.9),
        stream: req_body.stream,
    };

    let client = Client::builder()
        .timeout(Duration::from_secs(120))
        .build()
        .map_err(|e| {
            error!("Error creating HTTP client: {}", e);
            actix_web::error::ErrorInternalServerError("Failed to create HTTP client")
        })?;

    let response = client
        .post(&format!("{}/completion", llama_url))
        .header("Content-Type", "application/json")
        .json(&llama_request)
        .send()
        .await
        .map_err(|e| {
            error!("Error calling llama.cpp server: {}", e);
            actix_web::error::ErrorInternalServerError("Failed to call llama.cpp server")
        })?;

    let status = response.status();

    if status.is_success() {
        let llama_response: LlamaCppResponse = response.json().await.map_err(|e| {
            error!("Error parsing llama.cpp response: {}", e);
            actix_web::error::ErrorInternalServerError("Failed to parse llama.cpp response")
        })?;

        let openai_response = ChatCompletionResponse {
            id: format!("chatcmpl-{}", uuid::Uuid::new_v4()),
            object: "chat.completion".to_string(),
            created: std::time::SystemTime::now()
                .duration_since(std::time::UNIX_EPOCH)
                .unwrap()
                .as_secs(),
            model: req_body.model.clone(),
            choices: vec![Choice {
                message: ChatMessage {
                    role: "assistant".to_string(),
                    content: llama_response.content.trim().to_string(),
                },
                finish_reason: if llama_response.stop {
                    "stop".to_string()
                } else {
                    "length".to_string()
                },
            }],
        };

        Ok(HttpResponse::Ok().json(openai_response))
    } else {
        let error_text = response
            .text()
            .await
            .unwrap_or_else(|_| "Unknown error".to_string());

        error!("Llama.cpp server error ({}): {}", status, error_text);

        let actix_status = actix_web::http::StatusCode::from_u16(status.as_u16())
            .unwrap_or(actix_web::http::StatusCode::INTERNAL_SERVER_ERROR);

        Ok(HttpResponse::build(actix_status).json(serde_json::json!({
            "error": {
                "message": error_text,
                "type": "server_error"
            }
        })))
    }
}

#[derive(Debug, Deserialize)]
pub struct EmbeddingRequest {
    #[serde(deserialize_with = "deserialize_input")]
    pub input: Vec<String>,
    pub model: String,
    #[serde(default)]
    pub _encoding_format: Option<String>,
}

fn deserialize_input<'de, D>(deserializer: D) -> Result<Vec<String>, D::Error>
where
    D: serde::Deserializer<'de>,
{
    use serde::de::{self, Visitor};
    use std::fmt;

    struct InputVisitor;

    impl<'de> Visitor<'de> for InputVisitor {
        type Value = Vec<String>;

        fn expecting(&self, formatter: &mut fmt::Formatter) -> fmt::Result {
            formatter.write_str("a string or an array of strings")
        }

        fn visit_str<E>(self, value: &str) -> Result<Self::Value, E>
        where
            E: de::Error,
        {
            Ok(vec![value.to_string()])
        }

        fn visit_string<E>(self, value: String) -> Result<Self::Value, E>
        where
            E: de::Error,
        {
            Ok(vec![value])
        }

        fn visit_seq<A>(self, mut seq: A) -> Result<Self::Value, A::Error>
        where
            A: de::SeqAccess<'de>,
        {
            let mut vec = Vec::new();
            while let Some(value) = seq.next_element::<String>()? {
                vec.push(value);
            }
            Ok(vec)
        }
    }

    deserializer.deserialize_any(InputVisitor)
}

#[derive(Debug, Serialize)]
pub struct EmbeddingResponse {
    pub object: String,
    pub data: Vec<EmbeddingData>,
    pub model: String,
    pub usage: Usage,
}

#[derive(Debug, Serialize)]
pub struct EmbeddingData {
    pub object: String,
    pub embedding: Vec<f32>,
    pub index: usize,
}

#[derive(Debug, Serialize)]
pub struct Usage {
    pub prompt_tokens: u32,
    pub total_tokens: u32,
}

#[derive(Debug, Serialize)]
struct LlamaCppEmbeddingRequest {
    pub content: String,
}

#[derive(Debug, Deserialize)]
struct LlamaCppEmbeddingResponseItem {
    pub index: usize,
    pub embedding: Vec<Vec<f32>>,
}

#[post("/v1/embeddings")]
pub async fn embeddings_local(
    req_body: web::Json<EmbeddingRequest>,
    _req: HttpRequest,
) -> Result<HttpResponse> {
    dotenv().ok();

    let llama_url =
        env::var("EMBEDDING_URL").unwrap_or_else(|_| "http://localhost:8082".to_string());

    let client = Client::builder()
        .timeout(Duration::from_secs(120))
        .build()
        .map_err(|e| {
            error!("Error creating HTTP client: {}", e);
            actix_web::error::ErrorInternalServerError("Failed to create HTTP client")
        })?;

    let mut embeddings_data = Vec::new();
    let mut total_tokens = 0;

    for (index, input_text) in req_body.input.iter().enumerate() {
        let llama_request = LlamaCppEmbeddingRequest {
            content: input_text.clone(),
        };

        let response = client
            .post(&format!("{}/embedding", llama_url))
            .header("Content-Type", "application/json")
            .json(&llama_request)
            .send()
            .await
            .map_err(|e| {
                error!("Error calling llama.cpp server for embedding: {}", e);
                actix_web::error::ErrorInternalServerError(
                    "Failed to call llama.cpp server for embedding",
                )
            })?;

        let status = response.status();

        if status.is_success() {
            let raw_response = response.text().await.map_err(|e| {
                error!("Error reading response text: {}", e);
                actix_web::error::ErrorInternalServerError("Failed to read response")
            })?;

            let llama_response: Vec<LlamaCppEmbeddingResponseItem> =
                serde_json::from_str(&raw_response).map_err(|e| {
                    error!("Error parsing llama.cpp embedding response: {}", e);
                    error!("Raw response: {}", raw_response);
                    actix_web::error::ErrorInternalServerError(
                        "Failed to parse llama.cpp embedding response",
                    )
                })?;

            if let Some(item) = llama_response.get(0) {
                let flattened_embedding = if !item.embedding.is_empty() {
                    item.embedding[0].clone()
                } else {
                    vec![]
                };

                let estimated_tokens = (input_text.len() as f32 / 4.0).ceil() as u32;
                total_tokens += estimated_tokens;

                embeddings_data.push(EmbeddingData {
                    object: "embedding".to_string(),
                    embedding: flattened_embedding,
                    index,
                });
            } else {
                error!("No embedding data returned for input: {}", input_text);
                return Ok(HttpResponse::InternalServerError().json(serde_json::json!({
                    "error": {
                        "message": format!("No embedding data returned for input {}", index),
                        "type": "server_error"
                    }
                })));
            }
        } else {
            let error_text = response
                .text()
                .await
                .unwrap_or_else(|_| "Unknown error".to_string());

            error!("Llama.cpp server error ({}): {}", status, error_text);

            let actix_status = actix_web::http::StatusCode::from_u16(status.as_u16())
                .unwrap_or(actix_web::http::StatusCode::INTERNAL_SERVER_ERROR);

            return Ok(HttpResponse::build(actix_status).json(serde_json::json!({
                "error": {
                    "message": format!("Failed to get embedding for input {}: {}", index, error_text),
                    "type": "server_error"
                }
            })));
        }
    }

    let openai_response = EmbeddingResponse {
        object: "list".to_string(),
        data: embeddings_data,
        model: req_body.model.clone(),
        usage: Usage {
            prompt_tokens: total_tokens,
            total_tokens,
        },
    };

    Ok(HttpResponse::Ok().json(openai_response))
}

#[actix_web::get("/health")]
pub async fn health() -> Result<HttpResponse> {
    let llama_url = env::var("LLM_URL").unwrap_or_else(|_| "http://localhost:8081".to_string());

    if is_server_running(&llama_url).await {
        Ok(HttpResponse::Ok().json(serde_json::json!({
            "status": "healthy",
            "llama_server": "running"
        })))
    } else {
        Ok(HttpResponse::ServiceUnavailable().json(serde_json::json!({
            "status": "unhealthy",
            "llama_server": "not running"
        })))
    }
}

use regex::Regex;

#[derive(Debug, Serialize, Deserialize)]
struct GenericChatCompletionRequest {
    model: String,
    messages: Vec<ChatMessage>,
    stream: Option<bool>,
}

#[post("/v1/chat/completions")]
pub async fn generic_chat_completions(body: web::Bytes, _req: HttpRequest) -> Result<HttpResponse> {
    let body_str = std::str::from_utf8(&body).unwrap_or_default();
    info!("Original POST Data: {}", body_str);

    dotenv().ok();

    let api_key = env::var("AI_KEY")
        .map_err(|_| actix_web::error::ErrorInternalServerError("AI_KEY not set."))?;
    let model = env::var("AI_LLM_MODEL")
        .map_err(|_| actix_web::error::ErrorInternalServerError("AI_LLM_MODEL not set."))?;
    let endpoint = env::var("AI_ENDPOINT")
        .map_err(|_| actix_web::error::ErrorInternalServerError("AI_ENDPOINT not set."))?;

    let mut json_value: serde_json::Value = serde_json::from_str(body_str)
        .map_err(|_| actix_web::error::ErrorInternalServerError("Failed to parse JSON"))?;

    if let Some(obj) = json_value.as_object_mut() {
        obj.insert("model".to_string(), serde_json::Value::String(model));
    }

    let modified_body_str = serde_json::to_string(&json_value)
        .map_err(|_| actix_web::error::ErrorInternalServerError("Failed to serialize JSON"))?;

    info!("Modified POST Data: {}", modified_body_str);

    let mut headers = reqwest::header::HeaderMap::new();
    headers.insert(
        "Authorization",
        reqwest::header::HeaderValue::from_str(&format!("Bearer {}", api_key))
            .map_err(|_| actix_web::error::ErrorInternalServerError("Invalid API key format"))?,
    );
    headers.insert(
        "Content-Type",
        reqwest::header::HeaderValue::from_static("application/json"),
    );

    let client = Client::new();
    let response = client
        .post(&endpoint)
        .headers(headers)
        .body(modified_body_str)
        .send()
        .await
        .map_err(actix_web::error::ErrorInternalServerError)?;

    let status = response.status();
    let raw_response = response
        .text()
        .await
        .map_err(actix_web::error::ErrorInternalServerError)?;

    info!("Provider response status: {}", status);
    info!("Provider response body: {}", raw_response);

    if status.is_success() {
        match convert_to_openai_format(&raw_response) {
            Ok(openai_response) => Ok(HttpResponse::Ok()
                .content_type("application/json")
                .body(openai_response)),
            Err(e) => {
                error!("Failed to convert response format: {}", e);
                Ok(HttpResponse::Ok()
                    .content_type("application/json")
                    .body(raw_response))
            }
        }
    } else {
        let actix_status = actix_web::http::StatusCode::from_u16(status.as_u16())
            .unwrap_or(actix_web::http::StatusCode::INTERNAL_SERVER_ERROR);

        Ok(HttpResponse::build(actix_status)
            .content_type("application/json")
            .body(raw_response))
    }
}

fn convert_to_openai_format(provider_response: &str) -> Result<String, Box<dyn std::error::Error>> {
    #[derive(serde::Deserialize)]
    struct ProviderChoice {
        message: ProviderMessage,
        #[serde(default)]
        finish_reason: Option<String>,
    }

    #[derive(serde::Deserialize)]
    struct ProviderMessage {
        role: Option<String>,
        content: String,
    }

    #[derive(serde::Deserialize)]
    struct ProviderResponse {
        id: Option<String>,
        object: Option<String>,
        created: Option<u64>,
        model: Option<String>,
        choices: Vec<ProviderChoice>,
        usage: Option<ProviderUsage>,
    }

    #[derive(serde::Deserialize, Default)]
    struct ProviderUsage {
        prompt_tokens: Option<u32>,
        completion_tokens: Option<u32>,
        total_tokens: Option<u32>,
    }

    #[derive(serde::Serialize)]
    struct OpenAIResponse {
        id: String,
        object: String,
        created: u64,
        model: String,
        choices: Vec<OpenAIChoice>,
        usage: OpenAIUsage,
    }

    #[derive(serde::Serialize)]
    struct OpenAIChoice {
        index: u32,
        message: OpenAIMessage,
        finish_reason: String,
    }

    #[derive(serde::Serialize)]
    struct OpenAIMessage {
        role: String,
        content: String,
    }

    #[derive(serde::Serialize)]
    struct OpenAIUsage {
        prompt_tokens: u32,
        completion_tokens: u32,
        total_tokens: u32,
    }

    let provider: ProviderResponse = serde_json::from_str(provider_response)?;

    let first_choice = provider.choices.get(0).ok_or("No choices in response")?;
    let content = first_choice.message.content.clone();
    let role = first_choice
        .message
        .role
        .clone()
        .unwrap_or_else(|| "assistant".to_string());

    let usage = provider.usage.unwrap_or_default();
    let prompt_tokens = usage.prompt_tokens.unwrap_or(0);
    let completion_tokens = usage
        .completion_tokens
        .unwrap_or_else(|| content.split_whitespace().count() as u32);
    let total_tokens = usage
        .total_tokens
        .unwrap_or(prompt_tokens + completion_tokens);

    let openai_response = OpenAIResponse {
        id: provider
            .id
            .unwrap_or_else(|| format!("chatcmpl-{}", uuid::Uuid::new_v4().simple())),
        object: provider
            .object
            .unwrap_or_else(|| "chat.completion".to_string()),
        created: provider.created.unwrap_or_else(|| {
            std::time::SystemTime::now()
                .duration_since(std::time::UNIX_EPOCH)
                .unwrap()
                .as_secs()
        }),
        model: provider.model.unwrap_or_else(|| "llama".to_string()),
        choices: vec![OpenAIChoice {
            index: 0,
            message: OpenAIMessage { role, content },
            finish_reason: first_choice
                .finish_reason
                .clone()
                .unwrap_or_else(|| "stop".to_string()),
        }],
        usage: OpenAIUsage {
            prompt_tokens,
            completion_tokens,
            total_tokens,
        },
    };

    serde_json::to_string(&openai_response).map_err(|e| e.into())
}