Merge pull request #1 from Maria-Aidarus/netlify

Netlify

netlify.toml

@@ -0,0 +1,2 @@
+[build]
+  functions = "netlify/functions/"

netlify/functions/handleMetadata.js

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+// Placeholder function to fetch content from URL using a web scraping service
+async function fetchContentFromURL(url) {
+    // Implement logic to fetch content from the URL using a web scraping service
+    // Return the extracted content
+    // Placeholder code
+    const content = "<p>This is a sample content fetched from the URL</p>";
+    return content;
+}
+
+// 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
+    // 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 = {
+        mediaType: 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' }),
+        };
+    }
+}