import { fail } from '@sveltejs/kit';
import type { RequestHandler } from './$types';
import { RecursiveCharacterTextSplitter } from 'langchain/text_splitter';
import { OpenAI } from 'langchain/llms';
import { loadSummarizationChain } from 'langchain/chains';
import { Readability } from '@mozilla/readability';

const parseSite = async (url: string) => {
	const jsdom = await import('jsdom');
	const { JSDOM } = jsdom;
	const response = await fetch(url, {
		method: 'GET',
		mode: 'cors'
	});
	const html = await response.text();
	const dom = new JSDOM(html);
	const article = new Readability(dom.window.document).parse();
	return article?.textContent;
};

export const POST = (async ({ request, url }) => {
	try {
		const form = await request.formData();

		const url = form.get('url');
		const file = form.get('file');
		if (!file && !url) throw new Error('No prompt found in the request.');
		let text: string;
		if (url) {
			const article = await parseSite(url as string);
			if (!article) throw new Error('Could not parse site');
			text = article;
		} else if (file) {
			text = await (file as Blob).text();
		} else {
			throw new Error('Unknown error has occurred.');
		}

		const model = new OpenAI({ temperature: 0 });
		const textSplitter = new RecursiveCharacterTextSplitter({ chunkSize: 1000 });
		const docs = await textSplitter.createDocuments([text]);

		// This convenience function creates a document chain prompted to summarize a set of documents.
		const chain = loadSummarizationChain(model);
		const response = await chain.call({
			input_documents: docs
		});

		return new Response(JSON.stringify({ response: response.text }), {
			headers: {
				'Content-Type': 'application/json'
			}
		});
	} catch (err) {
		console.log(err);

		const appError = err as App.Error;
		console.log(appError.message);
		throw fail(500, { err: err });
	}
}) satisfies RequestHandler;