Hugging face schnell generator from csv

const fs = require('fs');
const path = require('path');
const readline = require('readline');

const API_URL = "https://api-inference.huggingface.co/models/black-forest-labs/FLUX.1-schnell";
const headers = {
  "Authorization": "Bearer <Hugging face token>",
  "Content-Type": "application/json"
};

// Delay function to wait for a specified number of milliseconds
function delay(ms) {
  return new Promise(resolve => setTimeout(resolve, ms));
}

// Function to parse CSV rows manually
function parseCSVRow(row) {
  const columns = [];
  let current = '';
  let inQuotes = false;

  for (let i = 0; i < row.length; i++) {
    const char = row[i];
    const nextChar = row[i + 1];

    if (char === '"' && inQuotes && nextChar === '"') {
      // Handle escaped quotes within a quoted field
      current += '"';
      i++; // Skip the next character
    } else if (char === '"') {
      // Toggle inQuotes state
      inQuotes = !inQuotes;
    } else if (char === ',' && !inQuotes) {
      // End of field
      columns.push(current.trim());
      current = '';
    } else {
      // Regular character within a field
      current += char;
    }
  }
  // Add the last field
  columns.push(current.trim());

  return columns;
}


// Function to generate a single image based on inputs and save it to the specified folder
async function fetchImageAndSave(folderName, inputText, imageIndex) {
  const payload = { 
	inputs: inputText,
	parameters: {
		width: 720,
		height: 480,
		seed: Math.ceil(Math.random() * 10000000),
	}
  };
  
  try {
	  console.log( {
      method: 'POST',
      headers,
      body: JSON.stringify(payload),
    });
    const response = await fetch(API_URL, {
      method: 'POST',
      headers,
      body: JSON.stringify(payload),
    });

    if (!response.ok) {
      throw new Error(`Error fetching image: ${response.statusText}`);
    }

    const imageBuffer = Buffer.from(await response.arrayBuffer());

    // Ensure the folder exists
    const folderPath = path.join(__dirname, folderName);
    fs.mkdirSync(folderPath, { recursive: true });

    // Save the image with a unique filename in the specified folder
    const outputPath = path.join(folderPath, `output_image_${imageIndex}.png`);
    fs.writeFileSync(outputPath, imageBuffer);

    console.log(`Image ${imageIndex} saved to ${outputPath}`);
  } catch (error) {
    console.error(`Error generating image ${imageIndex}:`, error);
  }
}

// Function to process CSV file manually
async function processCSV() {
  const fileStream = fs.createReadStream('story.csv');
  const rl = readline.createInterface({
    input: fileStream,
    crlfDelay: Infinity,
  });

  let lineIndex = 0;

  for await (const line of rl) {
    lineIndex += 1;

    // Skip the first two rows
    if (lineIndex <= 2) continue;

     // Parse the CSV row
    const columns = parseCSVRow(line);

    // Get folder name from the 1st column and input text from the 6th column
    let folderName = columns[0]?.trim(); // First column
    const inputText = columns[5]?.trim();  // Sixth column
	
	folderName = folderName.replaceAll(':', '-');
	
	// Reset imageIndex for each new row
    let imageIndex = 0;

    if (folderName && inputText) {
		// Loop to generate 70 images for each row
      for (let i = 0; i < 70; i++) {
        await fetchImageAndSave(folderName, inputText, imageIndex);
        imageIndex += 1;

        await delay(20 * 1000);
      }
    } else {
      console.warn(`Skipping row ${lineIndex}: Missing folder name or input text`);
    }
  }
}

processCSV();