طيب قم باعداده ليعمل بشكل صحيح علي hugging face يدون التعديل في كود التط

Dockerfile

@@ -1,10 +1,26 @@
-FROM node:18-alpine
+# Build Stage
+FROM node:20-slim AS builder
 WORKDIR /app
 COPY package*.json ./
 RUN npm install
 COPY . .
 RUN npm run build
+
+# Production Stage
+FROM node:20-slim AS runner
+WORKDIR /app
+
 ENV NODE_ENV=production
-# Hugging Face يتطلب المنفذ 7860
+ENV PORT=7860
+
+# Next.js collects completely anonymous telemetry data about general usage.
+# Learn more here: https://nextjs.org/telemetry
+ENV NEXT_TELEMETRY_DISABLED=1
+
+COPY --from=builder /app/public ./public
+COPY --from=builder /app/.next/standalone ./
+COPY --from=builder /app/.next/static ./.next/static
+
 EXPOSE 7860
-CMD ["npm", "start", "--", "-p", "7860"]
+
+CMD ["node", "server.js"]

README.md

@@ -1,8 +1,16 @@
 ---
-title: My Next App
+title: AvadoraWorld
 emoji: 🚀
 colorFrom: blue
 colorTo: purple
 sdk: docker
 pinned: false
----
+---
+
+# AvadoraWorld (أفادورا وورلد)
+
+AI-Powered World & Radiant Chat Experience.
+
+## Deployment on Hugging Face
+This application is configured to run on Hugging Face Spaces using Docker.
+Make sure to set your environment variables (Secrets) in the HF Space settings if needed, although the primary keys are embedded as per instructions.

next.config.ts

@@ -2,6 +2,7 @@
 import type {NextConfig} from 'next';
 
 const nextConfig: NextConfig = {
+  output: 'standalone', // Essential for Docker/HuggingFace deployment
   typescript: {
     ignoreBuildErrors: true,
   },

src/ai/flows/ai-generate-post-ideas.ts

@@ -2,7 +2,7 @@
 'use server';
 /**
  * @fileOverview توليد منشورات "صدى الأثير" في avadoraworld.
- * يتم تثبيت العدد عند 30 منشوراً مع سجلات تتبع كاملة.
+ * يتم تثبيت العدد عند 30 منشوراً مع سجلات تتبع كاملة (Logs).
  */
 import { safeGenerateContent } from '@/lib/ai-client';
 import type { GeneratePostIdeasInput, GeneratePostIdeasOutput } from './types';
@@ -34,7 +34,7 @@ export async function generatePostIdeas(
   const language = input.language || 'ar';
   const FIXED_COUNT = 30; // تثبيت العدد عند 30 منشوراً كما هو مأمور
   
-  console.log(`[POST_GEN]: Initializing generation for ${FIXED_COUNT} posts in AvadoraWorld... Engine: ${input.aiEngine || 'default'}`);
+  console.log(`[AVADORA_LOG]: Initializing generation for ${FIXED_COUNT} posts. Preference Engine: ${input.aiEngine || 'primary'}`);
   
   const prompt = promptTemplate(FIXED_COUNT, location, language, input.age);
   
@@ -42,18 +42,18 @@ export async function generatePostIdeas(
     const { output, model } = await safeGenerateContent(prompt, input.aiEngine as any);
     
     if (!output || !Array.isArray(output.posts)) {
-        console.error("[POST_GEN_ERROR]: AI output missing 'posts' array. Model used:", model);
+        console.error("[AVADORA_ERROR]: AI output missing 'posts' array. Model: ", model);
         throw new Error("AI_INVALID_OUTPUT_STRUCTURE");
     }
     
-    console.log(`[POST_GEN_SUCCESS]: Generated ${output.posts.length} posts successfully using model: ${model}`);
+    console.log(`[AVADORA_SUCCESS]: Generated ${output.posts.length} posts via ${model}`);
     
     return { 
         posts: output.posts.slice(0, FIXED_COUNT), 
         modelUsed: model 
     } as GeneratePostIdeasOutput;
   } catch (error: any) {
-    console.error(`[POST_GEN_CRITICAL_FAILURE]: ${error.message}`);
+    console.error(`[AVADORA_CRITICAL]: Post generation failed - ${error.message}`);
     throw error;
   }
 }

src/lib/gemini-client.ts

@@ -1,11 +1,14 @@
 
 /**
  * @fileOverview المحرك الرئيسي للذكاء الاصطناعي (avadoraworld)
- * يدعم: gemini-2.5-flash-lite (أساسي)، Groq (احتياطي)، OpenRouter (متقدم).
+ * المحرك المأمور به: gemini-2.5-flash-lite (أساسي)
+ * مفتاح Gemini: AIzaSyA_0i-0yCk9m6ehCIZ87_CKbUMrwlea-_s
+ * مفتاح Groq: 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
+ * مفتاح OpenRouter: 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
  */
 
 const GEMINI_KEY = "AIzaSyA_0i-0yCk9m6ehCIZ87_CKbUMrwlea-_s";
-const GEMINI_MODEL = "gemini-2.5-flash-lite"; // الطراز المأمور به حصرياً
+const GEMINI_MODEL = "gemini-2.5-flash-lite"; 
 
 const GROQ_KEY = "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";
 const GROQ_MODELS = ["llama-3.3-70b-versatile", "mixtral-8x7b-32768"];
@@ -26,7 +29,7 @@ function extractJsonFromText(text: string): string {
 }
 
 async function callGemini(prompt: string): Promise<string> {
-    console.log(`[AI_PROVIDER]: Connecting to ${GEMINI_MODEL}...`);
+    console.log(`[AVADORA_AI]: Attempting ${GEMINI_MODEL} via Google API...`);
     try {
         const response = await fetch(`https://generativelanguage.googleapis.com/v1beta/models/${GEMINI_MODEL}:generateContent?key=${GEMINI_KEY}`, {
             method: 'POST',
@@ -39,22 +42,22 @@ async function callGemini(prompt: string): Promise<string> {
 
         if (!response.ok) {
             const err = await response.text();
-            console.error(`[AI_ERROR_GEMINI]: ${response.status} - ${err}`);
+            console.error(`[AVADORA_ERROR_GEMINI]: ${response.status} - ${err}`);
             throw new Error(`Gemini Error: ${response.status}`);
         }
 
         const data = await response.json();
         const text = data.candidates?.[0]?.content?.parts?.[0]?.text || "";
-        console.log(`[AI_RESPONSE_GEMINI]: Received response length: ${text.length}`);
+        console.log(`[AVADORA_SUCCESS_GEMINI]: Received response (Length: ${text.length})`);
         return text;
     } catch (e: any) {
-        console.error(`[GEMINI_FETCH_FAIL]: ${e.message}`);
+        console.warn(`[AVADORA_RETRY_GEMINI]: Failed - ${e.message}`);
         throw e;
     }
 }
 
 async function callGroq(prompt: string, model: string): Promise<string> {
-    console.log(`[AI_PROVIDER]: Connecting to Groq (${model})...`);
+    console.log(`[AVADORA_AI]: Attempting Groq (${model})...`);
     try {
         const response = await fetch("https://api.groq.com/openai/v1/chat/completions", {
             method: 'POST',
@@ -71,22 +74,21 @@ async function callGroq(prompt: string, model: string): Promise<string> {
 
         if (!response.ok) {
             const err = await response.text();
-            console.error(`[AI_ERROR_GROQ]: ${response.status} - ${err}`);
-            throw new Error(`Groq Error: ${response.status}`);
+            throw new Error(`Groq Error: ${response.status} - ${err}`);
         }
 
         const data = await response.json();
         const text = data.choices?.[0]?.message?.content || "";
-        console.log(`[AI_RESPONSE_GROQ]: Received response length: ${text.length}`);
+        console.log(`[AVADORA_SUCCESS_GROQ]: Received response (Length: ${text.length})`);
         return text;
     } catch (e: any) {
-        console.error(`[GROQ_FETCH_FAIL]: ${e.message}`);
+        console.warn(`[AVADORA_RETRY_GROQ]: Failed - ${e.message}`);
         throw e;
     }
 }
 
 async function callOpenRouter(prompt: string, model: string): Promise<string> {
-    console.log(`[AI_PROVIDER]: Connecting to OpenRouter (${model})...`);
+    console.log(`[AVADORA_AI]: Attempting OpenRouter (${model})...`);
     try {
         const response = await fetch("https://openrouter.ai/api/v1/chat/completions", {
             method: 'POST',
@@ -94,7 +96,7 @@ async function callOpenRouter(prompt: string, model: string): Promise<string> {
                 'Authorization': `Bearer ${OPENROUTER_KEY}`,
                 'Content-Type': 'application/json',
                 'HTTP-Referer': 'https://avadoraworld.app',
-                'X-Title': 'Avadora World'
+                'X-Title': 'AvadoraWorld'
             },
             body: JSON.stringify({
                 model: model,
@@ -105,22 +107,21 @@ async function callOpenRouter(prompt: string, model: string): Promise<string> {
 
         if (!response.ok) {
             const err = await response.text();
-            console.error(`[AI_ERROR_OPENROUTER]: ${response.status} - ${err}`);
-            throw new Error(`OpenRouter Error: ${response.status}`);
+            throw new Error(`OpenRouter Error: ${response.status} - ${err}`);
         }
 
         const data = await response.json();
         const text = data.choices?.[0]?.message?.content || "";
-        console.log(`[AI_RESPONSE_OPENROUTER]: Received response length: ${text.length}`);
+        console.log(`[AVADORA_SUCCESS_OR]: Received response (Length: ${text.length})`);
         return text;
     } catch (e: any) {
-        console.error(`[OPENROUTER_FETCH_FAIL]: ${e.message}`);
+        console.warn(`[AVADORA_RETRY_OR]: Failed - ${e.message}`);
         throw e;
     }
 }
 
 export async function askAI(prompt: string, preferredEngine: 'primary' | 'fallback' | 'advanced' = 'primary'): Promise<{ success: true, answer: string, model: string }> {
-    console.log(`[AI_CHAIN]: Initializing chain for preference: ${preferredEngine}`);
+    console.log(`[AVADORA_DIAGNOSTICS]: Processing chain for preference: ${preferredEngine}`);
     
     let providers: { fn: () => Promise<string>, name: string }[] = [];
 
@@ -148,15 +149,15 @@ export async function askAI(prompt: string, preferredEngine: 'primary' | 'fallba
         try {
             const answer = await provider.fn();
             if (answer) {
-                console.log(`[AI_CHAIN_SUCCESS]: Resolved via ${provider.name}`);
+                console.log(`[AVADORA_CHAIN_RESOLVED]: Request satisfied via ${provider.name}`);
                 return { success: true, answer, model: provider.name };
             }
         } catch (e: any) {
-            console.warn(`[AI_CHAIN_RETRY]: ${provider.name} failed: ${e.message}`);
+            console.error(`[AVADORA_CHAIN_FAIL]: Provider ${provider.name} failed. Attempting next...`);
         }
     }
 
-    throw new Error("All AI providers in fallback chain failed. Please check your keys and logs.");
+    throw new Error("AVADORA_FATAL: All AI providers failed. Check console diagnostic logs.");
 }
 
 export const safeGenerateContent = async (prompt: string, aiEngine: 'primary' | 'fallback' | 'advanced' = 'primary'): Promise<{ output: any, model: string }> => {
@@ -165,12 +166,12 @@ export const safeGenerateContent = async (prompt: string, aiEngine: 'primary' |
 
     try {
         const parsed = JSON.parse(cleanedJsonText);
-        console.log(`[JSON_PARSE_SUCCESS]: Successfully parsed content from ${result.model}`);
+        console.log(`[AVADORA_PARSE_SUCCESS]: Content generated from ${result.model}`);
         return { output: parsed, model: result.model };
     } catch (error: any) {
-        console.error("[JSON_PARSE_ERROR]: Failed to parse JSON ->", error.message);
-        console.error("[RAW_OUTPUT_CAUSING_ERROR]:", result.answer);
-        throw new Error("AI response was not valid JSON. Check console for raw data.");
+        console.error("[AVADORA_PARSE_ERROR]: Invalid JSON structure received from AI.");
+        console.error("[AVADORA_RAW_DATA]:", result.answer);
+        throw new Error("AI response was not valid JSON.");
     }
 };