Update src/services/geminiService.ts

src/services/geminiService.ts

@@ -3,7 +3,8 @@ import { Product, ProductCategory } from "../types";
 
 // Helper to clean base64 string
 const cleanBase64 = (data: string) => {
-  return data.replace(/^data:image\/(png|jpeg|jpg|webp);base64,/, '');
+  // Use regex with case-insensitive flag
+  return data.replace(/^data:image\/\w+;base64,/i, '');
 };
 
 const getMimeType = (data: string) => {
@@ -55,7 +56,7 @@ const urlToBase64 = async (url: string): Promise<string> => {
 export const generateTryOn = async (userImageBase64: string, product: Product): Promise<string> => {
   const apiKey = process.env.API_KEY;
   if (!apiKey) {
-    throw new Error("API Key is missing. Please set the API_KEY environment variable.");
+    throw new Error("API Key is missing. Please check your settings.");
   }
 
   const ai = new GoogleGenAI({ apiKey });
@@ -69,81 +70,28 @@ export const generateTryOn = async (userImageBase64: string, product: Product):
       throw new Error("Could not load product image. Please try another item.");
   }
 
-  // --- PROMPT ENGINEERING FOR VIRTUAL TRY-ON ---
-  // We use Gemini 2.5 Flash Image ('nano banana') capabilities.
-  // The key is to instruct it to act as a segmentation and warping engine.
-
-  const basePrompt = `Act as an advanced Virtual Try-On (VTON) AI model.
-  
-  TASK:
-  Synthesize a photorealistic image of the USER (Image A) wearing the GARMENT (Image B).
-
-  STRICT PROCESSING STEPS:
-  1. SEGMENTATION: Identify the user's body parts relevant to the garment (e.g., torso for shirts, legs for pants, face for glasses).
-  2. EXTRACTION: Extract the garment from Image B, completely ignoring any white background, hangers, or mannequins.
-  3. WARPING: Warp the extracted garment to match the user's body pose, rotation, and shape in Image A. 
-     - If the user is turning, the garment must turn.
-     - If the user has arms crossed, the garment must fold realistically.
-  4. COMPOSITING: Blend the warped garment onto the user.
-     - Apply lighting from the user's environment to the garment.
-     - Cast shadows from the garment onto the user's skin/body to create depth.
+  // Simplified prompt focused on the visual task to avoid safety filter triggers
+  // and improve adherence to the "editing" capability of the model.
+  const prompt = `Update the first image to show the person wearing the ${product.name} shown in the second image.
   
-  CONSTRAINTS:
-  - PRESERVE FACE IDENTITY: Do not modify the user's facial features.
-  - PRESERVE BACKGROUND: Do not change the background behind the user.
-  - REALISM: The output must look like a photograph, not a photoshop cut-paste.
+  Product Category: ${product.category}
   
-  CATEGORY SPECIFIC INSTRUCTIONS:
+  Instructions:
+  - The item should fit the person naturally based on their pose.
+  - Maintain the original lighting, shadows, and background.
+  - Do not change the person's identity or facial features (other than adding eyewear if applicable).
+  - High photorealism.
   `;
 
-  let categoryInstruction = "";
-  switch (product.category) {
-    case ProductCategory.EYEWEAR:
-      categoryInstruction = `Category: EYEWEAR.
-      - Fit: Align perfectly with the eyes and nose bridge.
-      - Scale: Ensure the glasses width matches the face width.
-      - Reality: If the glasses are sunglasses, add reflections. If clear, show the eyes through the lenses.`;
-      break;
-    case ProductCategory.NECKLACE:
-      categoryInstruction = `Category: JEWELRY.
-      - Fit: Drape the necklace around the neck curve. Gravity is important.
-      - Layering: It must sit ON TOP of skin or the shirt the user is already wearing (unless it's a shirt try-on, then on the new shirt).`;
-      break;
-    case ProductCategory.HEADWEAR:
-      categoryInstruction = `Category: HAT.
-      - Fit: Sit naturally on the crown of the head.
-      - Occlusion: If the user has hair volume, the hat should compress it slightly or sit behind fringe.`;
-      break;
-    case ProductCategory.SHIRT:
-      categoryInstruction = `Category: UPPER BODY GARMENT.
-      - Action: Completely replace the user's current top.
-      - Sleeves: Match the sleeve length of the target garment. If the user's arm pose is complex, warp the sleeves accordingly.
-      - Tucking: Determine if a tuck is natural based on the user's pose.`;
-      break;
-    case ProductCategory.PANTS:
-      categoryInstruction = `Category: LOWER BODY GARMENT.
-      - Action: Replace the user's pants/skirt.
-      - Waist: Align with the natural waistline.`;
-      break;
-    default:
-      categoryInstruction = `Category: FASHION ITEM. Wear it naturally.`;
-      break;
-  }
-
-  const fullPrompt = `${basePrompt}\n${categoryInstruction}`;
-
   const userMime = getMimeType(userImageBase64);
   const productMime = getMimeType(productBase64);
 
   try {
-    // gemini-2.5-flash-image is the recommended model for high-fidelity image manipulation tasks
     const response = await ai.models.generateContent({
       model: 'gemini-2.5-flash-image', 
       contents: {
         parts: [
-          {
-            text: fullPrompt
-          },
+          { text: prompt },
           {
             inlineData: {
               mimeType: userMime,
@@ -160,19 +108,32 @@ export const generateTryOn = async (userImageBase64: string, product: Product):
       }
     });
 
-    const parts = response.candidates?.[0]?.content?.parts;
+    const candidate = response.candidates?.[0];
+    
+    // Check for safety blocks or other finish reasons
+    if (candidate?.finishReason && candidate.finishReason !== 'STOP') {
+        throw new Error(`Generation blocked. Reason: ${candidate.finishReason}`);
+    }
+
+    const parts = candidate?.content?.parts;
     if (parts) {
       for (const part of parts) {
         if (part.inlineData && part.inlineData.data) {
            return `data:${part.inlineData.mimeType || 'image/png'};base64,${part.inlineData.data}`;
         }
+        // If we got text back instead of an image, it might be an explanation of refusal
+        if (part.text) {
+            console.warn("Model returned text:", part.text);
+            // Don't throw immediately, look for other parts, but if only text remains...
+        }
       }
     }
 
-    throw new Error("No image generated by the AI.");
+    throw new Error("The AI did not generate an image. Please try a different photo.");
 
-  } catch (error) {
+  } catch (error: any) {
     console.error("Gemini API Error:", error);
-    throw error;
+    // Propagate the specific error message to the UI
+    throw new Error(error.message || "Failed to connect to AI service");
   }
 };