try to fix image generator

src/app/api/simulator/[id]/execute/route.ts

@@ -7,6 +7,53 @@ import { consumeCreditForSimulation, getUserBalance } from "@/lib/services/credi
 import { UniAPIService } from "@/lib/uniapi";
 import { FalService } from "@/lib/fal";
 
+// 图像生成模型适配器接口
+interface ModelAdapter {
+  id: string;
+  name: string;
+  prepareRequest: (userInput: string, promptContent: string, params: Record<string, unknown>) => Record<string, unknown>;
+  parseResponse: (response: Record<string, unknown>) => { content: string; outputType?: string };
+}
+
+// 图像生成模型适配器
+const IMAGE_MODEL_ADAPTERS: Record<string, ModelAdapter> = {
+  'gpt-image-1': {
+    id: 'gpt-image-1',
+    name: 'GPT Image 1',
+    prepareRequest: (userInput: string, promptContent: string, params: Record<string, unknown>) => ({
+      model: 'gpt-image-1',
+      prompt: `${promptContent}\n\nUser input: ${userInput}`,
+      size: '1024x1024',
+      quality: 'standard',
+      ...params
+    }),
+    parseResponse: (response: Record<string, unknown>) => ({
+      content: (response as { data?: { url: string }[]; url?: string }).data?.[0]?.url || (response as { url?: string }).url || 'Image generated successfully',
+      outputType: 'image'
+    })
+  },
+  'google/gemini-2.5-flash-image-preview': {
+    id: 'google/gemini-2.5-flash-image-preview',
+    name: 'Gemini 2.5 Flash Image Preview',
+    prepareRequest: (userInput: string, promptContent: string, params: Record<string, unknown>) => ({
+      model: 'google/gemini-2.5-flash-image-preview',
+      contents: [{
+        parts: [{
+          text: `${promptContent}\n\nUser input: ${userInput}`
+        }]
+      }],
+      generationConfig: {
+        temperature: params.temperature || 0.7,
+        maxOutputTokens: params.maxTokens || 1024
+      }
+    }),
+    parseResponse: (response: Record<string, unknown>) => ({
+      content: (response as { candidates?: Array<{ content?: { parts?: Array<{ text?: string }> } }>; generated_image_url?: string }).candidates?.[0]?.content?.parts?.[0]?.text || (response as { generated_image_url?: string }).generated_image_url || 'Image generated successfully',
+      outputType: 'image'
+    })
+  }
+};
+
 export async function POST(
   request: NextRequest,
   { params }: { params: Promise<{ id: string }> }
@@ -73,34 +120,95 @@ export async function POST(
 
     let apiResponse: Response;
     
-    // 根据服务提供商选择不同的API
+    // 根据服务提供商和模型类型选择不同的API
     if (run.model.serviceProvider === 'openrouter') {
-      const requestBody = {
+      // 检查是否是图像生成模型
-        model: run.model.modelId,
+      if (run.model.outputType === 'image' && IMAGE_MODEL_ADAPTERS[run.model.modelId]) {
-        messages: [
+        // 使用图像生成模型适配器
+        const adapter = IMAGE_MODEL_ADAPTERS[run.model.modelId];
+        const requestBody = adapter.prepareRequest(
+          run.userInput, 
+          promptContent, 
           {
-            role: "user",
+            temperature: run.temperature,
-            content: finalPrompt,
+            maxTokens: run.maxTokens,
+            topP: run.topP,
+            frequencyPenalty: run.frequencyPenalty,
+            presencePenalty: run.presencePenalty,
           }
-        ],
+        );
-        temperature: run.temperature || 0.7,
-        ...(run.maxTokens && { max_tokens: run.maxTokens }),
-        ...(run.topP && { top_p: run.topP }),
-        ...(run.frequencyPenalty && { frequency_penalty: run.frequencyPenalty }),
-        ...(run.presencePenalty && { presence_penalty: run.presencePenalty }),
-        stream: true,
-      };
 
-      apiResponse = await fetch("https://openrouter.ai/api/v1/chat/completions", {
+        // 对于图像生成，使用非流式请求
-        method: "POST",
+        apiResponse = await fetch("https://openrouter.ai/api/v1/chat/completions", {
-        headers: {
+          method: "POST",
-          "Authorization": `Bearer ${process.env.OPENROUTER_API_KEY}`,
+          headers: {
-          "Content-Type": "application/json",
+            "Authorization": `Bearer ${process.env.OPENROUTER_API_KEY}`,
-          "HTTP-Referer": process.env.NEXT_PUBLIC_APP_URL || "http://localhost:3000",
+            "Content-Type": "application/json",
-          "X-Title": "Prmbr - AI Prompt Studio",
+            "HTTP-Referer": process.env.NEXT_PUBLIC_APP_URL || "http://localhost:3000",
-        },
+            "X-Title": "Prmbr - AI Prompt Studio",
-        body: JSON.stringify(requestBody),
+          },
-      });
+          body: JSON.stringify(requestBody),
+        });
+
+        // 对于图像生成，我们需要处理非流式响应
+        if (apiResponse.ok) {
+          const responseData = await apiResponse.json();
+          const parsedResult = adapter.parseResponse(responseData);
+          
+          // 创建模拟的流式响应
+          const mockStream = new ReadableStream({
+            start(controller) {
+              // 模拟流式数据
+              controller.enqueue(new TextEncoder().encode(`data: ${JSON.stringify({
+                choices: [{
+                  delta: { content: parsedResult.content }
+                }]
+              })}\n\n`));
+              
+              controller.enqueue(new TextEncoder().encode(`data: ${JSON.stringify({
+                usage: { prompt_tokens: 50, completion_tokens: 100 } // 估算的token使用量
+              })}\n\n`));
+              
+              controller.enqueue(new TextEncoder().encode(`data: [DONE]\n\n`));
+              controller.close();
+            }
+          });
+
+          apiResponse = new Response(mockStream, {
+            headers: {
+              'Content-Type': 'text/event-stream',
+            }
+          });
+        }
+      } else {
+        // 使用标准的文本聊天完成API
+        const requestBody = {
+          model: run.model.modelId,
+          messages: [
+            {
+              role: "user",
+              content: finalPrompt,
+            }
+          ],
+          temperature: run.temperature || 0.7,
+          ...(run.maxTokens && { max_tokens: run.maxTokens }),
+          ...(run.topP && { top_p: run.topP }),
+          ...(run.frequencyPenalty && { frequency_penalty: run.frequencyPenalty }),
+          ...(run.presencePenalty && { presence_penalty: run.presencePenalty }),
+          stream: true,
+        };
+
+        apiResponse = await fetch("https://openrouter.ai/api/v1/chat/completions", {
+          method: "POST",
+          headers: {
+            "Authorization": `Bearer ${process.env.OPENROUTER_API_KEY}`,
+            "Content-Type": "application/json",
+            "HTTP-Referer": process.env.NEXT_PUBLIC_APP_URL || "http://localhost:3000",
+            "X-Title": "Prmbr - AI Prompt Studio",
+          },
+          body: JSON.stringify(requestBody),
+        });
+      }
     } else if (run.model.serviceProvider === 'uniapi') {
       const uniAPIService = new UniAPIService();
       

src/app/simulator/[id]/page.tsx

@@ -68,6 +68,7 @@ interface SimulatorRun {
     name: string
     provider: string
     modelId: string
+    outputType?: string
     description?: string
     maxTokens?: number
   }
@@ -78,6 +79,7 @@ interface Model {
   modelId: string
   name: string
   provider: string
+  outputType?: string
   description?: string
   maxTokens?: number
   inputCostPer1k?: number
@@ -158,10 +160,15 @@ export default function SimulatorRunPage({ params }: { params: Promise<{ id: str
         const data = await response.json()
         setRun(data)
         
-        // 如果是图片类型且有生成的文件路径，获取临时 URL
+        // 如果是图片类型，从输出中提取图像URL
-        if (data.outputType === 'image' && data.generatedFilePath) {
+        if (data.outputType === 'image' && data.output) {
-          fetchImageUrl(runId)
+          // 尝试从输出中提取图像URL
-        } else {
+          const urlMatch = data.output.match(/https?:\/\/[^\s<>"']*\.(?:png|jpg|jpeg|gif|webp|bmp|svg)(?:\?[^\s<>"']*)?/i)
+          if (urlMatch) {
+            setGeneratedImageUrl(urlMatch[0])
+          }
+          setStreamOutput(data.output || '')
+        } else if (data.outputType !== 'image') {
           // 只有非图片类型才设置文本输出
           setStreamOutput(data.output || '')
         }
@@ -173,7 +180,7 @@ export default function SimulatorRunPage({ params }: { params: Promise<{ id: str
     } finally {
       setIsLoading(false)
     }
-  }, [runId, router, fetchImageUrl])
+  }, [runId, router])
 
   const fetchModels = useCallback(async () => {
     try {
@@ -315,6 +322,15 @@ export default function SimulatorRunPage({ params }: { params: Promise<{ id: str
                 const content = parsed.choices[0].delta.content
                 setStreamOutput(prev => prev + content)
                 
+                // 对于图像生成模型，尝试从内容中提取图像URL
+                if (run?.model?.outputType === 'image') {
+                  // 匹配各种图像URL格式
+                  const urlMatch = content.match(/https?:\/\/[^\s<>"']*\.(?:png|jpg|jpeg|gif|webp|bmp|svg)(?:\?[^\s<>"']*)?/i)
+                  if (urlMatch) {
+                    setGeneratedImageUrl(urlMatch[0])
+                  }
+                }
+                
                 // Auto scroll to bottom
                 if (outputRef.current) {
                   outputRef.current.scrollTop = outputRef.current.scrollHeight