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Created the netlify.toml file and the file to handle the metadata
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[build] | ||
functions = "netlify/functions/" |
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// 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; | ||
} | ||
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// 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; | ||
} | ||
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// 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 }; | ||
} | ||
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// 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 }; | ||
} | ||
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// 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; | ||
} | ||
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export async function handler(event) { | ||
try { | ||
const { url, apiKey } = JSON.parse(event.body); | ||
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// Step 1: Fetch content from the URL using web scraping service | ||
const fetchedContent = await fetchContentFromURL(url); | ||
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// Step 2: Simplify the fetched content for GPT analysis | ||
const simplifiedContent = simplifyContent(fetchedContent); | ||
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// Step 3: Perform GPT analysis for media type and topics | ||
const { inferredMediaType, extractedTopics } = await performGPTAnalysis(simplifiedContent); | ||
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// Step 4: Map inferred values to predefined formats and topics | ||
const { predefinedMediaType, predefinedTopics } = mapInferredValues(inferredMediaType, extractedTopics); | ||
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// Step 5: Format the response | ||
const formattedResponse = formatResponse(predefinedMediaType, predefinedTopics); | ||
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// Return the formatted response | ||
return { | ||
statusCode: 200, | ||
body: JSON.stringify(formattedResponse), | ||
}; | ||
} catch (error) { | ||
return { | ||
statusCode: 500, | ||
body: JSON.stringify({ error: 'Something went wrong' }), | ||
}; | ||
} | ||
} |