Update aiEngine.js

aiEngine.js

@@ -95,26 +95,18 @@ export const AIEngine = {
   },
 
   /**
-   * 3. ONBOARDING ANALYST (Question Generation + Gatekeeping)
-   * Returns STRICT JSON for the Frontend.
-   * Can return { status: "REJECTED", ... } or { status: "ACCEPTED", questions: ... }
+   * 3. ONBOARDING ANALYST (Question Generation)
+   * Returns STRICT JSON for the Frontend
    */
   generateEntryQuestions: async (description) => {
-    const modelId = 'gemini-flash-latest';
-    // Updated prompt to enforce Gatekeeping (TOS/Nonsense check)
+    const modelId = 'gemini-2.5-flash';
+    // Using the updated prompt which handles REJECTED/ACCEPTED logic
     const input = `[MODE 1: QUESTIONS]\nAnalyze this game idea: "${description}". Check for TOS violations or nonsense. If good, ask 3 questions. Output ONLY raw JSON.`;
     
+    try {
       const response = await genAI.models.generateContent({
         model: modelId,
-        
-        
-        
-        config: {
-          
-          /*thinkingConfig: {
-           thinkingBudget: -1,
-         },
-         */
+        config: { 
             responseMimeType: "application/json",
             systemInstruction: { parts: [{ text: prompts.analyst_system_prompt }] }
         },
@@ -125,32 +117,23 @@ export const AIEngine = {
       return JSON.parse(text);
     } catch (e) {
       console.error("Analyst Error:", e);
-      // Fallback to prevent frontend crash
       return { status: "ACCEPTED", questions: [{ id: "fallback", label: "Please describe the core gameplay loop in detail.", type: "textarea" }] };
     }
   },
 
   /**
-   * 4. PROJECT GRADER (Feasibility Check + Title)
+   * 4. PROJECT GRADER (Feasibility Check)
    * Returns STRICT JSON
    */
   gradeProject: async (description, answers) => {
-    const modelId = 'gemini-flash-latest';
-    // Updated prompt to ask for Title and Rating
+    const modelId = 'gemini-2.5-flash';
+    // Using the updated prompt to respect Title and relaxed Grading
     const input = `[MODE 2: GRADING]\nIdea: "${description}"\nUser Answers: ${JSON.stringify(answers)}\n\nAssess feasibility. Output JSON with title and rating.`;
 
     try {
       const response = await genAI.models.generateContent({
         model: modelId,
-        
-        
-        
         config: { 
-        
-      /*  thinkingConfig: {
-          thinkingBudget: -1,
-         },
-         */
             responseMimeType: "application/json",
             systemInstruction: { parts: [{ text: prompts.analyst_system_prompt }] }
         },
@@ -165,70 +148,42 @@ export const AIEngine = {
 
   /**
    * 5. IMAGE GENERATOR (Visual Assets)
-   * Uses Imagen 3
+   * Uses Gemini 2.5 Flash Image with Stream (Correct Implementation)
    */
   generateImage: async (prompt) => {
-    const modelId = 'gemini-2.5-flash-image';
-    
-    try {
-      
-      const config = {
-    responseModalities: [
-        'IMAGE',
-        'TEXT',
-    ],
-  };
-  const model = 'gemini-2.5-flash-image';
-  const contents = [
-    {
-      role: 'user',
-      parts: [
-        {
-          text: prompt,
-        },
-      ],
-    },
-  ];
-
-   const response = await genAI.models.generateContentStream({
- //  const response = await genAI.models.generateContent({
-   
-      model,
-    config,
-    contents,
-  });
-  let fileIndex = 0;
-  for await (const chunk of response) {
-    if (!chunk.candidates || !chunk.candidates[0].content || !chunk.candidates[0].content.parts) {
-      continue;
-    }
-    if (chunk.candidates?.[0]?.content?.parts?.[0]?.inlineData) {
-      
-      // inlineData is the object from the model: { mimeType: 'image/png', data: '...base64...' }
-const inlineData = chunk.candidates[0].content.parts[0].inlineData;
-const rawB64 = (inlineData.data || "").replace(/\s+/g, ""); // remove whitespace/newlines just in case
-const mimeType = inlineData.mimeType || "image/png";
-
-// ensure it's a Buffer (optional, for validation)
-const buffer = Buffer.from(rawB64, "base64");
+    // Inject the prompt template from JSON to ensure adherence to instructions
+    const finalPrompt = prompts.image_gen_prompt.replace('{{DESCRIPTION}}', prompt);
 
-// produce a canonical base64 string (this also validates)
-const base64 = buffer.toString("base64");
-
-// build a browser-friendly data URL and return it
-const dataUrl = `data:${mimeType};base64,${base64}`;
-return dataUrl;
+    const config = {
+      responseModalities: ['IMAGE', 'TEXT'],
+    };
+    const model = 'gemini-2.5-flash-image';
+    const contents = [
+      {
+        role: 'user',
+        parts: [{ text: finalPrompt }],
+      },
+    ];
 
-      
-// Here
+    try {
+      const response = await genAI.models.generateContentStream({
+        model,
+        config,
+        contents,
+      });
 
-    }
-    else {
-      console.log(chunk.text);
-    }
-  }
-      
-     // return image.image.toString('base64');
+      for await (const chunk of response) {
+        if (!chunk.candidates || !chunk.candidates[0].content || !chunk.candidates[0].content.parts) {
+          continue;
+        }
+        if (chunk.candidates?.[0]?.content?.parts?.[0]?.inlineData) {
+          const inlineData = chunk.candidates[0].content.parts[0].inlineData;
+          const rawB64 = (inlineData.data || "").replace(/\s+/g, ""); 
+          // Return raw Base64 
+          return rawB64; 
+        } 
+      }
+      return null;
     } catch (error) {
       console.error("Image Gen Error:", error);
       return null;