feat: implement SparkPlug backend API integration for Gemini and Meshy

src/pages/SparkPlug.tsx

@@ -27,6 +27,7 @@ export default function SparkPlug() {
 
   // Step 4: 3D Model
   const [modelProgress, setModelProgress] = useState(0);
+  const [generatedGlbUrl, setGeneratedGlbUrl] = useState<string | null>(null);
 
   const handleFileUpload = async (e: React.ChangeEvent<HTMLInputElement>) => {
     if (e.target.files && e.target.files[0]) {
@@ -49,17 +50,14 @@ export default function SparkPlug() {
         setIsLoading(false);
         return;
       }
-      
-      // We would normally send the PDF to the backend or use Gemini SDK directly here.
-      // Mocking the extraction for now based on the Indica Shampoo PDF context:
-      await new Promise(r => setTimeout(r, 2000));
-      
-      setExtractedPrompt("Product: Indica Easy Herbal Shield Colour Shampoo\nType: Sachet/Packet\nColor Palette: Green, White\nKey Elements: Woman with black hair, Amla & Aloe illustration, 10 Minutes Herbal Care text.\n\nDescription: A glossy green rectangular sachet with vivid branding and product imagery for an herbal hair colour shampoo.");
+      // Call our backend endpoint to parse PDF and query Gemini
+      const prompt = await api.extractPDF(pdfFile);
+      setExtractedPrompt(prompt);
       
       setCurrentStep(2);
-    } catch (err) {
+    } catch (err: any) {
       console.error(err);
-      alert("Failed to process PDF.");
+      alert(`Failed to process PDF: ${err.message || 'Unknown error'}`);
     } finally {
       setIsLoading(false);
     }
@@ -80,22 +78,18 @@ export default function SparkPlug() {
         setIsLoading(false);
         return;
       }
-
-      // Mocking the image generation with Nano Banana
-      await new Promise(r => setTimeout(r, 3000));
+      // Call our backend endpoint which queries Gemini Imagen
+      const images = await api.generateImages(extractedPrompt);
       
-      // Placeholder images for the 4 views (front, back, left, right)
-      setGeneratedImages([
-        "https://images.unsplash.com/photo-1629198688000-71f23e745b6e?w=400&q=80", // front
-        "https://images.unsplash.com/photo-1629198725848-18e55e975cc3?w=400&q=80", // back
-        "https://images.unsplash.com/photo-1629198725838-511bb7405234?w=400&q=80", // left
-        "https://images.unsplash.com/photo-1629198688000-71f23e745b6e?w=400&q=80", // right
-      ]);
-      
-      setCurrentStep(3);
-    } catch (err) {
+      if (images && images.length === 4) {
+        setGeneratedImages(images);
+        setCurrentStep(3);
+      } else {
+        throw new Error("Failed to generate exactly 4 images.");
+      }
+    } catch (err: any) {
       console.error(err);
-      alert("Failed to generate image.");
+      alert(`Failed to generate images: ${err.message || 'Unknown error'}`);
     } finally {
       setIsLoading(false);
     }
@@ -116,17 +110,39 @@ export default function SparkPlug() {
         setIsLoading(false);
         return;
       }
-
-      // Simulate Meshy generation
-      for (let i = 1; i <= 10; i++) {
-        await new Promise(r => setTimeout(r, 500));
-        setModelProgress(i * 10);
+      // Call backend to start Meshy task
+      const taskId = await api.generate3D(generatedImages);
+      
+      // Poll Meshy status
+      let isDone = false;
+      let attempts = 0;
+      while (!isDone && attempts < 60) { // Max 5 mins (60 * 5s)
+        attempts++;
+        await new Promise(resolve => setTimeout(resolve, 5000));
+        
+        const statusData = await api.get3DStatus(taskId);
+        const { status, progress, model_urls, task_error } = statusData;
+        
+        if (progress) {
+          setModelProgress(progress);
+        }
+        
+        if (status === 'SUCCEEDED') {
+          setGeneratedGlbUrl(model_urls.glb);
+          isDone = true;
+          setCurrentStep(4);
+        } else if (status === 'FAILED') {
+          throw new Error(`Meshy generation failed: ${task_error?.message || 'Unknown error'}`);
+        }
       }
       
-      setCurrentStep(4);
-    } catch (err) {
+      if (!isDone) {
+        throw new Error("Meshy task timed out after 5 minutes.");
+      }
+      
+    } catch (err: any) {
       console.error(err);
-      alert("Failed to generate 3D model.");
+      alert(`Failed to generate 3D model: ${err.message || 'Unknown error'}`);
     } finally {
       setIsLoading(false);
     }
@@ -136,8 +152,9 @@ export default function SparkPlug() {
     setLoadingMessage('Saving project to your workspace...');
     setIsLoading(true);
     try {
-      // Mock generated GLB URL from Meshy
-      const mockGlbUrl = "https://raw.githubusercontent.com/KhronosGroup/glTF-Sample-Models/master/2.0/BoxTextured/glTF-Binary/BoxTextured.glb";
+      if (!generatedGlbUrl) {
+        throw new Error("No GLB URL available to save.");
+      }
       
       // Create project and save with model injected into scene_data
       const res = await api.saveProject({
@@ -152,7 +169,7 @@ export default function SparkPlug() {
               position: [0, 0, 0],
               rotation: [0, 0, 0],
               scale: [1, 1, 1],
-              url: mockGlbUrl
+              url: generatedGlbUrl
             }
           ]
         }