// This is a Node.js backend file for secure API handling
// Place this in your backend server, not in the frontend

const express = require('express');
const { Configuration, OpenAIApi } = require('openai');
const multer = require('multer');
const fs = require('fs');
const path = require('path');

const router = express.Router();

// Configure OpenAI - IMPORTANT: Store this in environment variables
const configuration = new Configuration({
  apiKey: process.env.OPENAI_API_KEY, // Never expose API keys in frontend
});

const openai = new OpenAIApi(configuration);

// Configure multer for file uploads
const upload = multer({
  dest: 'uploads/',
  limits: {
    fileSize: 10 * 1024 * 1024, // 10MB limit
  },
  fileFilter: (req, file, cb) => {
    if (file.mimetype.startsWith('image/')) {
      cb(null, true);
    } else {
      cb(new Error('Only image files are allowed'));
    }
  }
});

// Generate realistic T-shirt mockup endpoint
router.post('/generate-mockup', upload.single('image'), async (req, res) => {
  try {
    const { caption, shirtColor = 'white' } = req.body;
    const imagePath = req.file ? req.file.path : null;

    if (!imagePath && !req.body.image) {
      return res.status(400).json({ 
        success: false, 
        error: 'No image provided' 
      });
    }

    let base64Image = req.body.image;
    
    // If file was uploaded, convert to base64
    if (imagePath) {
      const imageBuffer = fs.readFileSync(imagePath);
      base64Image = imageBuffer.toString('base64');
      // Clean up uploaded file
      fs.unlinkSync(imagePath);
    }

    // Construct the detailed prompt for DALL-E
    const prompt = `
      Generate a high-quality, realistic photograph of a plain unisex T-shirt on a clean, neutral background.
      The T-shirt must:
      - Be ${shirtColor} colored
      - Have soft, natural lighting with visible fabric texture
      - Show natural folds and wrinkles in the fabric
      - Have the following design seamlessly printed on it: "${caption}"
      - The printed design should follow the shirt's wrinkles, lighting, and shadows naturally
      - Blend the design so it appears part of the fabric, not pasted on
      - Preserve realistic shadows, highlights, and depth
      - Style: photorealistic, commercial product photography, high detail
    `;

    // Call DALL-E API
    const response = await openai.createImage({
      model: "dall-e-3",
      prompt: prompt,
      n: 1,
      size: "1024x1024",
      quality: "hd",
      response_format: "url",
    });

    const imageUrl = response.data.data[0].url;

    res.json({
      success: true,
      imageUrl: imageUrl,
      message: 'Mockup generated successfully'
    });

  } catch (error) {
    console.error('Mockup generation error:', error);
    res.status(500).json({
      success: false,
      error: 'Failed to generate mockup: ' + error.message
    });
  }
});

// Alternative endpoint for when image is sent as base64
router.post('/generate-mockup-base64', async (req, res) => {
  try {
    const { image, caption, shirtColor = 'white' } = req.body;

    if (!image) {
      return res.status(400).json({ 
        success: false, 
        error: 'No image provided' 
      });
    }

    // Same DALL-E prompt as above
    const prompt = `
      Generate a high-quality, realistic photograph of a plain unisex T-shirt on a clean, neutral background.
      The T-shirt must:
      - Be ${shirtColor} colored
      - Have soft, natural lighting with visible fabric texture
      - Show natural folds and wrinkles in the fabric
      - Have the user's uploaded image seamlessly printed on the chest area
      - The printed design should follow the shirt's wrinkles, lighting, and shadows naturally
      - Blend the design so it appears part of the fabric, not pasted on
      - Preserve realistic shadows, highlights, and depth
      - Include the text "${caption}" as part of the design if appropriate
      - Style: photorealistic, commercial product photography, high detail
    `;

    // Call DALL-E API with image editing capability
    const response = await openai.createImageEdit(
      // Convert base64 to file buffer for API
      Buffer.from(image.split(',')[1], 'base64'),
      prompt,
      null, // No mask needed for full image
      1,
      "1024x1024"
    );

    const imageUrl = response.data.data[0].url;

    res.json({
      success: true,
      imageUrl: imageUrl,
      message: 'Mockup generated successfully'
    });

  } catch (error) {
    console.error('Mockup generation error:', error);
    res.status(500).json({
      success: false,
      error: 'Failed to generate mockup: ' + error.message
    });
  }
});

module.exports = router;