learndb/netlify/functions/handleMetadata.js

// import fetch from 'node-fetch'; // Import for webscraping (fetchContentFromURL(url) function
// const OpenAI = require('openai'); // Import for performGPTAnalysis(content) function

// Function to fetch content from URL using a web scraping service
async function fetchContentFromURL(url) {
    // try {
    //     // Make an HTTP GET request to the provided URL
    //     const response = await fetch(url);
    //     // Check if the response status is OK (status code 200)
    //     if (!response.ok) {
    //         throw new Error(`Failed to fetch URL: ${response.statusText}`);
    //     }
    //     // Read the response body as text (HTML content)
    //     const content = await response.text();
    //     // Return the extracted content
    //     return content;
    // } catch (error) {
    //     throw new Error(`Error fetching URL: ${error.message}`);
    // }

    return "Hello maria";

}

// Placeholder function to simplify the content for GPT analysis
function simplifyContent(content) {
    // Implement logic to simplify the content for GPT analysis
    // Remove unnecessary elements, clean HTML tags, format content, etc.
    // Placeholder code
    const simplifiedContent = "Simplified content suitable for GPT analysis";
    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
    
    
    // Default code from OpenRouter documentation for the Mistral-7b model
    // Retrieved from https://openrouter.ai/models/mistralai/mistral-7b-instruct?tab=api
    // Using OpenAI's client API makes this easily replaceable with other models (ex. GPT-4)
    ```
    const openai = new OpenAI({
        baseURL: "https://openrouter.ai/api/v1",
        apiKey: $OPENROUTER_API_KEY,
        defaultHeaders: {
            "HTTP-Referer": $YOUR_SITE_URL, // Optional, for including your app on openrouter.ai rankings.
            "X-Title": $YOUR_SITE_NAME, // Optional. Shows in rankings on openrouter.ai.
        },
        // dangerouslyAllowBrowser: true,
    })
    async function main() {
        const completion = await openai.chat.completions.create({
            model: "mistralai/mistral-7b-instruct",
            messages: [
                { role: "user", content: "Say this is a test" }
            ],
        })
    console.log(completion.choices[0].message)
    }
    main()
    ```
    
    // 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,
        // Other metadata fields if needed
    };
    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(formattedResponse),
        };
    } catch (error) {
        return {
            statusCode: 500,
            body: JSON.stringify({ error: 'Something went wrong' }),
        };
    }
}