learndb/netlify/functions/handleMetadata.js

const https = require('https'); // Import for webscraping (fetchContentFromURL(url) function
// const fetch = require('node-fetch');
const { OpenAIApi, Configuration } = require('openai');

// Placeholder function to fetch content from URL using a web scraping service
async function fetchContentFromURL(url) {
    return new Promise((resolve, reject) => {
        https.get(url, (response) => {
            let data = '';
            // A chunk of data has been received.
            response.on('data', (chunk) => {
                data += chunk;
            });
            // The whole response has been received.
            response.on('end', () => {
                resolve(data);
            });
        }).on("error", (error) => {
            reject(`Error fetching URL: ${error.message}`);
        });
    });
}

function simplifyContent(content) {
    // Remove HTML tags
    let simplifiedContent = content.replace(/<[^>]*>/g, '');
    // Remove CSS within style tags
    simplifiedContent = simplifiedContent.replace(/<style[^>]*>.*<\/style>/gms, '');
    // Remove inline CSS and JavaScript within script tags
    simplifiedContent = simplifiedContent.replace(/<script[^>]*>.*<\/script>/gms, '');
    // Remove special characters and HTML entities
    simplifiedContent = simplifiedContent.replace(/[^\w\s]/gi, '').replace(/&[a-z]+;/gi, '');
    // Remove URLs
    simplifiedContent = simplifiedContent.replace(/https?:\/\/[^\s]+/gi, '');
    // Normalize whitespace
    simplifiedContent = simplifiedContent.replace(/\s+/g, ' ').trim();
    // Basic language simplification
    simplifiedContent = simplifiedContent.toLowerCase();
    // // Simple summarization: taking the first few sentences
    // const sentences = simplifiedContent.split('. ');
    // const summarizedContent = sentences.slice(0, Math.min(5, sentences.length)).join('. ');
    return simplifiedContent;
}

// Placeholder function to perform GPT analysis for media type and topics using Mistral-7b via OpenRouter
async function performGPTAnalysis(content) {
    // Implement logic to send content to Mistral-7b via OpenRouter for GPT analysis
    // Send content and receive GPT analysis response
    // Placeholder code
    const inferredMediaType = "article";
    const extractedTopics = ["topic1", "topic2"];
    return { inferredMediaType, extractedTopics };
}

// Placeholder function to map inferred values to predefined formats and topics
function mapInferredValues(mediaType, topics) {
    // Implement logic to map inferred media type and topics to predefined formats and topics
    // Match inferred values with predefined taxonomy
    // Placeholder code
    const predefinedMediaType = "Article";
    const predefinedTopics = ["Topic 1", "Topic 2"];
    return { predefinedMediaType, predefinedTopics };
}

// Placeholder function to format the response
function formatResponse(predefinedMediaType, predefinedTopics) {
    // Implement logic to format the extracted metadata into the desired response structure
    // Construct the response object
    // Placeholder code
    const response = {
        format: predefinedMediaType,
        topics: predefinedTopics,
    };
    return response;
}

export async function handler(event) {
    try {
        // Extract URL and API Key from the request body
        const { url, apiKey } = JSON.parse(event.body);

        // Validate if URL and API Key are present
        if (!url || !apiKey) {
            return {
                statusCode: 400,
                body: JSON.stringify({ error: 'URL and API Key are required' }),
            };
        }

        // Step 1: Fetch content from the URL using a web scraping service
        const fetchedContent = await fetchContentFromURL(url);

        // Step 2: Simplify the fetched content for GPT analysis
        const simplifiedContent = simplifyContent(fetchedContent);

        // Step 3: Perform GPT analysis for media type and topics
        const { inferredMediaType, extractedTopics } = await performGPTAnalysis(simplifiedContent);

        // Step 4: Map inferred values to predefined formats and topics
        const { predefinedMediaType, predefinedTopics } = mapInferredValues(inferredMediaType, extractedTopics);

        // Step 5: Format the response
        const formattedResponse = formatResponse(predefinedMediaType, predefinedTopics);

        // Return the formatted response
        return {
            statusCode: 200,
            body: JSON.stringify(simplifiedContent),
        };
    } catch (error) {
        return {
            statusCode: 500,
            body: JSON.stringify({ error: 'Something went wrong' }),
        };
    }
}