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/* eslint-disable @typescript-eslint/no-explicit-any */
import type { NextRequest } from "next/server";
import { NextResponse } from "next/server";
import OpenAI from "openai";
import { MODELS } from "@/lib/providers";
import {
DIVIDER,
FOLLOW_UP_SYSTEM_PROMPT,
INITIAL_SYSTEM_PROMPT,
REPLACE_END,
SEARCH_START,
} from "@/lib/prompts";
export async function POST(request: NextRequest) {
const body = await request.json();
const { prompt, model, redesignMarkdown, html, apiKey, customModel, baseUrl } = body;
const openai = new OpenAI({
apiKey: apiKey || process.env.OPENAI_API_KEY || "",
baseURL: baseUrl || process.env.OPENAI_BASE_URL,
});
if (!model || (!prompt && !redesignMarkdown)) {
return NextResponse.json(
{ ok: false, error: "Missing required fields" },
{ status: 400 }
);
}
const selectedModel = MODELS.find(
(m) => m.value === model || m.label === model
);
if (!selectedModel) {
return NextResponse.json(
{ ok: false, error: "Invalid model selected" },
{ status: 400 }
);
}
try {
// Create a stream response
const encoder = new TextEncoder();
const stream = new TransformStream();
const writer = stream.writable.getWriter();
// Start the response
const response = new NextResponse(stream.readable, {
headers: {
"Content-Type": "text/plain; charset=utf-8",
"Cache-Control": "no-cache",
Connection: "keep-alive",
},
});
(async () => {
let completeResponse = "";
try {
const chatCompletion = await openai.chat.completions.create({
model: customModel || selectedModel.value,
messages: [
{
role: "system",
content: INITIAL_SYSTEM_PROMPT,
},
{
role: "user",
content: redesignMarkdown
? `Here is my current design as a markdown:\n\n${redesignMarkdown}\n\nNow, please create a new design based on this markdown.`
: html
? `Here is my current HTML code:\n\n\`\`\`html\n${html}\n\`\`\`\n\nNow, please create a new design based on this HTML.`
: prompt,
},
],
stream: true,
});
for await (const chunk of chatCompletion) {
const content = chunk.choices[0]?.delta?.content || "";
await writer.write(encoder.encode(content));
completeResponse += content;
if (completeResponse.includes("</html>")) {
break;
}
}
} catch (error: any) {
await writer.write(
encoder.encode(
JSON.stringify({
ok: false,
message:
error.message ||
"An error occurred while processing your request.",
})
)
);
} finally {
await writer?.close();
}
})();
return response;
} catch (error: any) {
return NextResponse.json(
{
ok: false,
message:
error?.message || "An error occurred while processing your request.",
},
{ status: 500 }
);
}
}
export async function PUT(request: NextRequest) {
const body = await request.json();
const { prompt, html, previousPrompt, selectedElementHtml, apiKey, model, baseUrl, customModel } = body;
const openai = new OpenAI({
apiKey: apiKey || process.env.OPENAI_API_KEY,
baseURL: baseUrl || process.env.OPENAI_BASE_URL,
});
if (!prompt || !html) {
return NextResponse.json(
{ ok: false, error: "Missing required fields" },
{ status: 400 }
);
}
const selectedModel = MODELS.find(
(m) => m.value === model || m.label === model
);
if (!selectedModel) {
return NextResponse.json(
{ ok: false, error: "Invalid model selected" },
{ status: 400 }
);
}
try {
const response = await openai.chat.completions.create({
model: customModel || selectedModel.value,
messages: [
{
role: "system",
content: FOLLOW_UP_SYSTEM_PROMPT,
},
{
role: "user",
content: previousPrompt
? previousPrompt
: "You are modifying the HTML file based on the user's request.",
},
{
role: "assistant",
content: `The current code is: \n\`\`\`html\n${html}\n\`\`\` ${selectedElementHtml
? `\n\nYou have to update ONLY the following element, NOTHING ELSE: \n\n\`\`\`html\n${selectedElementHtml}\n\`\`\``
: ""}
`,
},
{
role: "user",
content: prompt,
},
],
});
const chunk = response.choices[0]?.message?.content;
if (!chunk) {
return NextResponse.json(
{ ok: false, message: "No content returned from the model" },
{ status: 400 }
);
}
if (chunk) {
const updatedLines: number[][] = [];
let newHtml = html;
let position = 0;
let moreBlocks = true;
while (moreBlocks) {
const searchStartIndex = chunk.indexOf(SEARCH_START, position);
if (searchStartIndex === -1) {
moreBlocks = false;
continue;
}
const dividerIndex = chunk.indexOf(DIVIDER, searchStartIndex);
if (dividerIndex === -1) {
moreBlocks = false;
continue;
}
const replaceEndIndex = chunk.indexOf(REPLACE_END, dividerIndex);
if (replaceEndIndex === -1) {
moreBlocks = false;
continue;
}
const searchBlock = chunk.substring(
searchStartIndex + SEARCH_START.length,
dividerIndex
);
const replaceBlock = chunk.substring(
dividerIndex + DIVIDER.length,
replaceEndIndex
);
if (searchBlock.trim() === "") {
newHtml = `${replaceBlock}\n${newHtml}`;
updatedLines.push([1, replaceBlock.split("\n").length]);
} else {
const blockPosition = newHtml.indexOf(searchBlock);
if (blockPosition !== -1) {
const beforeText = newHtml.substring(0, blockPosition);
const startLineNumber = beforeText.split("\n").length;
const replaceLines = replaceBlock.split("\n").length;
const endLineNumber = startLineNumber + replaceLines - 1;
updatedLines.push([startLineNumber, endLineNumber]);
newHtml = newHtml.replace(searchBlock, replaceBlock);
}
}
position = replaceEndIndex + REPLACE_END.length;
}
return NextResponse.json({
ok: true,
html: newHtml,
updatedLines,
});
} else {
return NextResponse.json(
{ ok: false, message: "No content returned from the model" },
{ status: 400 }
);
}
} catch (error: any) {
return NextResponse.json(
{
ok: false,
message:
error.message || "An error occurred while processing your request.",
},
{ status: 500 }
);
}
} |