using System.Net.Http.Headers;
using System.Text.Json;
using System.Text.Json.Serialization;

using Toak.Api.Models;
using Toak.Serialization;
using Toak.Core;
using Toak.Core.Interfaces;

namespace Toak.Api;

public class GroqApiClient : ISpeechClient, ILlmClient
{
    private readonly HttpClient _httpClient;

    public GroqApiClient(string apiKey)
    {
        _httpClient = new HttpClient();
        _httpClient.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", apiKey);
        _httpClient.BaseAddress = new Uri("https://api.groq.com/openai/v1/");
    }

    public async Task<string> TranscribeAsync(string filePath, string language = "", string model = "whisper-large-v3-turbo")
    {
        using var content = new MultipartFormDataContent();
        using var fileStream = File.OpenRead(filePath);
        using var streamContent = new StreamContent(fileStream);
        
        streamContent.Headers.ContentType = new MediaTypeHeaderValue("audio/wav"); // or mpeg
        content.Add(streamContent, "file", Path.GetFileName(filePath));
        
        string modelToUse = string.IsNullOrWhiteSpace(model) ? "whisper-large-v3-turbo" : model;

        // according to docs whisper-large-v3-turbo requires the language to be provided if it is to be translated later potentially or if we need the most accurate behavior
        // Actually, if we want language param, we can pass it to either model
        content.Add(new StringContent(modelToUse), "model");

        if (!string.IsNullOrWhiteSpace(language))
        {
            var firstLang = language.Split(',')[0].Trim();
            content.Add(new StringContent(firstLang), "language");
        }

        Logger.LogDebug($"Sending Whisper API request ({modelToUse})...");
        var response = await _httpClient.PostAsync("audio/transcriptions", content);
        Logger.LogDebug($"Whisper API response status: {response.StatusCode}");
        
        if (!response.IsSuccessStatusCode)
        {
            var error = await response.Content.ReadAsStringAsync();
            throw new Exception($"Whisper API Error: {response.StatusCode} - {error}");
        }

        var json = await response.Content.ReadAsStringAsync();
        var result = JsonSerializer.Deserialize(json, AppJsonSerializerContext.Default.WhisperResponse);
        return result?.Text ?? string.Empty;
    }

    public async Task<string> RefineTextAsync(string rawTranscript, string systemPrompt, string model = "openai/gpt-oss-20b")
    {
        var requestBody = new LlamaRequest
        {
            Model = string.IsNullOrWhiteSpace(model) ? "openai/gpt-oss-20b" : model,
            Temperature = 0.0,
            Messages = new[]
            {
                new LlamaRequestMessage { Role = "system", Content = systemPrompt },
                new LlamaRequestMessage { Role = "user", Content = $"<transcript>{rawTranscript}</transcript>" }
            }
        };

        var jsonContent = new StringContent(JsonSerializer.Serialize(requestBody, AppJsonSerializerContext.Default.LlamaRequest), System.Text.Encoding.UTF8, "application/json");

        Logger.LogDebug($"Sending Llama API request (model: {requestBody.Model})...");
        var response = await _httpClient.PostAsync("chat/completions", jsonContent);
        Logger.LogDebug($"Llama API response status: {response.StatusCode}");

        if (!response.IsSuccessStatusCode)
        {
            var error = await response.Content.ReadAsStringAsync();
            throw new Exception($"Llama API Error: {response.StatusCode} - {error}");
        }

        var json = await response.Content.ReadAsStringAsync();
        var result = JsonSerializer.Deserialize(json, AppJsonSerializerContext.Default.LlamaResponse);

        return result?.Choices?.FirstOrDefault()?.Message?.Content ?? string.Empty;
    }

    public async IAsyncEnumerable<string> RefineTextStreamAsync(string rawTranscript, string systemPrompt, string model = "openai/gpt-oss-20b")
    {
        var requestBody = new LlamaRequest
        {
            Model = string.IsNullOrWhiteSpace(model) ? "openai/gpt-oss-20b" : model,
            Temperature = 0.0,
            Stream = true,
            Messages = new[]
            {
                new LlamaRequestMessage { Role = "system", Content = systemPrompt },
                new LlamaRequestMessage { Role = "user", Content = $"<transcript>{rawTranscript}</transcript>" }
            }
        };

        var jsonContent = new StringContent(JsonSerializer.Serialize(requestBody, AppJsonSerializerContext.Default.LlamaRequest), System.Text.Encoding.UTF8, "application/json");

        using var request = new HttpRequestMessage(HttpMethod.Post, "chat/completions") { Content = jsonContent };
        request.Headers.Accept.Add(new MediaTypeWithQualityHeaderValue("text/event-stream"));
        
        Logger.LogDebug($"Sending Llama Steam API request (model: {requestBody.Model})...");
        using var response = await _httpClient.SendAsync(request, HttpCompletionOption.ResponseHeadersRead);
        Logger.LogDebug($"Llama Stream API response status: {response.StatusCode}");

        if (!response.IsSuccessStatusCode)
        {
            var error = await response.Content.ReadAsStringAsync();
            throw new Exception($"Llama API Error: {response.StatusCode} - {error}");
        }

        using var stream = await response.Content.ReadAsStreamAsync();
        using var reader = new StreamReader(stream);

        string? line;
        while ((line = await reader.ReadLineAsync()) != null)
        {
            if (string.IsNullOrWhiteSpace(line)) continue;
            if (line.StartsWith("data: "))
            {
                var data = line.Substring("data: ".Length).Trim();
                if (data == "[DONE]") break;

                var chunk = JsonSerializer.Deserialize(data, AppJsonSerializerContext.Default.LlamaStreamResponse);
                var content = chunk?.Choices?.FirstOrDefault()?.Delta?.Content;
                if (!string.IsNullOrEmpty(content))
                {
                    yield return content;
                }
            }
        }
    }
}