const std = @import("std");
const deepseek_core = @import("deepseek_core");
const openai = @import("openai.zig");

const Allocator = std.mem.Allocator;
const http = std.http;

/// Handle chat completions endpoint (OpenAI compatible)
pub fn chatCompletions(
    allocator: Allocator,
    model: *deepseek_core.Model,
    request: *http.Server.Request,
) !void {
    _ = allocator;
    _ = model;
    
    // For now, send a simple placeholder response
    const response_json = 
        \\{
        \\  "id": "chatcmpl-123",
        \\  "object": "chat.completion", 
        \\  "created": 1677652288,
        \\  "model": "deepzig-v3",
        \\  "choices": [{
        \\    "index": 0,
        \\    "message": {
        \\      "role": "assistant",
        \\      "content": "Hello! This is a placeholder response from DeepZig V3."
        \\    },
        \\    "finish_reason": "stop"
        \\  }],
        \\  "usage": {
        \\    "prompt_tokens": 10,
        \\    "completion_tokens": 15,
        \\    "total_tokens": 25
        \\  }
        \\}
    ;
    
    try request.respond(response_json, .{
        .extra_headers = &.{
            .{ .name = "content-type", .value = "application/json" },
        },
    });
}

/// Handle text completions endpoint
pub fn completions(
    allocator: Allocator,
    model: *deepseek_core.Model,
    request: *http.Server.Request,
) !void {
    _ = allocator;
    _ = model;
    
    try request.respond("Text completions not yet implemented", .{
        .status = .not_implemented,
    });
}

/// Handle models list endpoint
pub fn models(
    allocator: Allocator,
    model: *deepseek_core.Model,
    request: *http.Server.Request,
) !void {
    _ = allocator;
    _ = model;
    
    const response_json = 
        \\{
        \\  "object": "list",
        \\  "data": [{
        \\    "id": "deepzig-v3",
        \\    "object": "model",
        \\    "created": 1677652288,
        \\    "owned_by": "deepzig"
        \\  }]
        \\}
    ;
    
    try request.respond(response_json, .{
        .extra_headers = &.{
            .{ .name = "content-type", .value = "application/json" },
        },
    });
}

/// Handle health check endpoint
pub fn health(allocator: Allocator, request: *http.Server.Request) !void {
    _ = allocator;
    
    const response_json = 
        \\{
        \\  "status": "healthy",
        \\  "timestamp": 1677652288,
        \\  "version": "0.1.0"
        \\}
    ;
    
    try request.respond(response_json, .{
        .extra_headers = &.{
            .{ .name = "content-type", .value = "application/json" },
        },
    });
}

/// Handle WebSocket endpoint
pub fn websocket(
    allocator: Allocator,
    model: *deepseek_core.Model,
    request: *http.Server.Request,
) !void {
    _ = allocator;
    _ = model;
    
    try request.respond("WebSocket not yet implemented", .{
        .status = .not_implemented,
    });
}

/// Generate chat completion response (helper function)
fn generateChatCompletion(
    allocator: Allocator,
    model: *deepseek_core.Model,
    chat_request: openai.ChatCompletionRequest,
) !*openai.ChatCompletionResponse {
    // TODO: Implement actual generation
    _ = model;
    _ = chat_request;
    
    const response = try allocator.create(openai.ChatCompletionResponse);
    response.* = openai.ChatCompletionResponse{
        .id = "chatcmpl-123",
        .object = "chat.completion",
        .created = std.time.timestamp(),
        .model = "deepzig-v3",
        .choices = &[_]openai.Choice{
            .{
                .index = 0,
                .message = openai.Message{
                    .role = "assistant",
                    .content = "Hello! This is a placeholder response from DeepZig V3.",
                },
                .finish_reason = "stop",
            },
        },
        .usage = openai.Usage{
            .prompt_tokens = 10,
            .completion_tokens = 15,
            .total_tokens = 25,
        },
    };
    
    return response;
}