use anyhow::{anyhow, Result};
use async_trait::async_trait;
use futures::AsyncReadExt;
use gpui::serde_json;
use isahc::prelude::Configurable;
use lazy_static::lazy_static;
use serde::{Deserialize, Serialize};
use std::sync::Arc;
use std::{env, time::Instant};
use util::http::{HttpClient, Request};

lazy_static! {
    static ref OPENAI_API_KEY: Option<String> = env::var("OPENAI_API_KEY").ok();
}

#[derive(Clone)]
pub struct OpenAIEmbeddings {
    pub client: Arc<dyn HttpClient>,
}

#[derive(Serialize)]
struct OpenAIEmbeddingRequest<'a> {
    model: &'static str,
    input: Vec<&'a str>,
}

#[derive(Deserialize)]
struct OpenAIEmbeddingResponse {
    data: Vec<OpenAIEmbedding>,
    usage: OpenAIEmbeddingUsage,
}

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

#[derive(Deserialize)]
struct OpenAIEmbeddingUsage {
    prompt_tokens: usize,
    total_tokens: usize,
}

#[async_trait]
pub trait EmbeddingProvider: Sync + Send {
    async fn embed_batch(&self, spans: Vec<&str>) -> Result<Vec<Vec<f32>>>;
}

pub struct DummyEmbeddings {}

#[async_trait]
impl EmbeddingProvider for DummyEmbeddings {
    async fn embed_batch(&self, spans: Vec<&str>) -> Result<Vec<Vec<f32>>> {
        // 1024 is the OpenAI Embeddings size for ada models.
        // the model we will likely be starting with.
        let dummy_vec = vec![0.32 as f32; 1536];
        return Ok(vec![dummy_vec; spans.len()]);
    }
}

// impl OpenAIEmbeddings {
//     async fn truncate(span: &str) -> String {
//         let bpe = cl100k_base().unwrap();
//         let mut tokens = bpe.encode_with_special_tokens(span);
//         if tokens.len() > 8192 {
//             tokens.truncate(8192);
//             let result = bpe.decode(tokens);
//             if result.is_ok() {
//                 return result.unwrap();
//             }
//         }

//         return span.to_string();
//     }
// }

#[async_trait]
impl EmbeddingProvider for OpenAIEmbeddings {
    async fn embed_batch(&self, spans: Vec<&str>) -> Result<Vec<Vec<f32>>> {
        // Truncate spans to 8192 if needed
        // let t0 = Instant::now();
        // let mut truncated_spans = vec![];
        // for span in spans {
        //     truncated_spans.push(Self::truncate(span));
        // }
        // let spans = futures::future::join_all(truncated_spans).await;
        // log::info!("Truncated Spans in {:?}", t0.elapsed().as_secs());

        let api_key = OPENAI_API_KEY
            .as_ref()
            .ok_or_else(|| anyhow!("no api key"))?;

        let request = Request::post("https://api.openai.com/v1/embeddings")
            .redirect_policy(isahc::config::RedirectPolicy::Follow)
            .header("Content-Type", "application/json")
            .header("Authorization", format!("Bearer {}", api_key))
            .body(
                serde_json::to_string(&OpenAIEmbeddingRequest {
                    input: spans,
                    model: "text-embedding-ada-002",
                })
                .unwrap()
                .into(),
            )?;

        let mut response = self.client.send(request).await?;
        if !response.status().is_success() {
            return Err(anyhow!("openai embedding failed {}", response.status()));
        }

        let mut body = String::new();
        response.body_mut().read_to_string(&mut body).await?;
        let response: OpenAIEmbeddingResponse = serde_json::from_str(&body)?;

        log::info!(
            "openai embedding completed. tokens: {:?}",
            response.usage.total_tokens
        );

        Ok(response
            .data
            .into_iter()
            .map(|embedding| embedding.embedding)
            .collect())
    }
}