using System.Net.Http.Json;
using VectorSearchApp.Configuration;
using VectorSearchApp.Models;

namespace VectorSearchApp.Services;

public interface IEmbeddingService
{
    Task<float[]> GenerateEmbeddingAsync(string text, CancellationToken cancellationToken = default);
}

public class EmbeddingService : IEmbeddingService
{
    private readonly HttpClient _httpClient;
    private readonly string _modelName;
    private readonly int _dimension;

    public EmbeddingService(EmbeddingConfiguration config)
    {
        _modelName = config.ModelName;
        _dimension = config.Dimension;
        _httpClient = new HttpClient
        {
            BaseAddress = new Uri("https://api-inference.huggingface.co/models/")
        };
        _httpClient.DefaultRequestHeaders.Add("User-Agent", "VectorSearchApp");
    }

    public async Task<float[]> GenerateEmbeddingAsync(string text, CancellationToken cancellationToken = default)
    {
        var request = new
        {
            inputs = text
        };

        var response = await _httpClient.PostAsJsonAsync(_modelName, request, cancellationToken);
        
        if (!response.IsSuccessStatusCode)
        {
            throw new InvalidOperationException($"Failed to generate embedding: {response.StatusCode}");
        }

        var result = await response.Content.ReadFromJsonAsync<float[][]>(cancellationToken: cancellationToken);
        
        if (result?.Length > 0 && result[0].Length > 0)
        {
            return result[0];
        }

        throw new InvalidOperationException("Failed to generate embedding");
    }
}