The Future of Multimodal AI Applications

// Client: Send multimodal update via WebSocket
        async function sendUpdate(text, imageBlob, sensorData) {
          let imageBase64 = null;
          if (imageBlob) {
            imageBase64 = await blobToBase64(imageBlob); // Convert Blob to Base64
          }
          const messagePayload = {
            text: text,
            sensorData: sensorData,
            image: imageBase64 // Embed Base64 image data (or null)
          };
          // Send a single JSON message containing all data
          socket.send(JSON.stringify({ type: 'chem_update_request', payload: messagePayload }));
        }
        
        // Server: Handle WebSocket message
        socket.on('message', async (message) => {
          try {
            // Parse the incoming JSON message
            const received = JSON.parse(message.toString());
        
            if (received.type === 'chem_update_request' && received.payload) {
              const { text, sensorData, image } = received.payload; // Extract data
              let imageBuffer = null;
              if (image) {
                imageBuffer = Buffer.from(image, 'base64'); // Decode Base64 image if present
              }
              // --> Call Multimodal AI (Gemini) with combined data
              // Note: Pass imageBuffer (binary) or image (base64) based on API needs
              const aiResponse = await callGeminiApi({ text, sensorData, imageBuffer });
              // Send AI response back to client
              socket.send(JSON.stringify({ type: 'ai_response', payload: { text: aiResponse } }));
            }
          } catch (e) {
            // Handle potential errors (e.g., JSON parsing)
            console.error("Failed to process message:", e);
          }
        });

// Client: Call backend API to analyze image
        async function analyzeMathWork(imageBase64, promptText, taxonomy) {
          try {
            const response = await fetch('/api/analyze', {
              method: 'POST',
              headers: { 'Content-Type': 'application/json' },
              body: JSON.stringify({ imageBase64, prompt: promptText, taxonomy })
            });
            if (!response.ok) throw new Error(`HTTP error! status: ${response.status}`);
            const analysis = await response.json();
            displayFeedback(analysis); // Show results in UI
          } catch (error) {
            console.error("Error analyzing math work:", error);
            // Display error to user
          }
        }
        
        // Server: Process request and call AI (e.g., Express.js)
        app.post('/api/analyze', async (req, res) => {
          try {
            const { imageBase64, prompt, taxonomy } = req.body;
            if (!imageBase64 || !prompt) {
              return res.status(400).json({ error: 'Missing imageBase64 or prompt.' });
            }
        
            const model = getGeminiVisionModel(); 
            // Construct the full prompt including taxonomy if needed by the model API
            const fullPrompt = `${prompt} (Consider taxonomy: ${JSON.stringify(taxonomy)})`;
        
            const aiResponse = await model.generateContent([fullPrompt, { inlineData: { mimeType: 'image/jpeg', data: imageBase64 } }]);
            // Assuming aiResponse structure allows direct sending or needs transformation
            res.json({ analysis: aiResponse.response.candidates[0].content.parts[0].text });
          } catch (error) {
            console.error("Error processing AI analysis:", error);
            res.status(500).json({ error: 'Failed to analyze image.' });
          }
        });

// Core logic when user asks for help or an asset
async function handleUserInput(prompt, context) {
  // --> 1. Call AI Model (e.g., Gemini) with prompt and context
  const aiResponse = await callAIModelAPI(prompt, context);

  // 2. Parse response to determine type (text, code, image)
  const parsed = parseAIResponse(aiResponse);

  // 3. Integrate back into UI
  if (parsed.type === 'image_asset') {
    // --> Optional: Call background removal API/library
    const finalImage = await removeBackground(parsed.imageBase64);
    displayImageAsset(finalImage); // Add to Scratch assets
  } else if (parsed.type === 'code_suggestion') {
    displayCodeSuggestion(parsed.codeBlocks); // Show blocks in UI
  } else {
    displayExplanation(parsed.text); // Show text in chat
  }
}