Create app.py

app.py

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+import os
+import json
+import time
+import asyncio
+import threading
+import logging
+import numpy as np
+import pandas as pd
+import cv2
+import nltk
+from datetime import datetime
+from concurrent.futures import ThreadPoolExecutor
+from fastapi import FastAPI, HTTPException, Request, UploadFile, File
+from fastapi.responses import StreamingResponse, HTMLResponse, FileResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+from diffusers import StableDiffusionPipeline
+from langdetect import detect, DetectorFactory
+import torch
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+import moviepy.editor as mp
+from PIL import Image, ImageDraw, ImageFont
+from pydantic import BaseModel
+from typing import List, Dict, Optional
+
+# التهيئة الأساسية
+DetectorFactory.seed = 0
+app = FastAPI()
+
+# تهيئة مجلدات التخزين
+os.makedirs("uploads", exist_ok=True)
+os.makedirs("memory", exist_ok=True)
+os.makedirs("projects", exist_ok=True)
+
+# 1. نماذج اللغات المدعومة (خفيفة الوزن)
+LANGUAGE_MODELS = {
+    "en": "gpt2-medium",
+    "ar": "arbml/gpt2-arabic-poetry",
+    "zh": "bert-base-chinese",
+    "ja": "colorfulscoop/gpt2-small-ja",
+    "fr": "dbmdz/gpt2-french",
+    "de": "dbmdz/gpt2-german",
+    "it": "LorenzoDeMattei/GePpeTto",
+    "hi": "surajpai/GPT2-Hindi"
+}
+
+# 2. نظام الذاكرة والتعلم التلقائي
+class AIMemory:
+    def __init__(self):
+        self.memory_file = "memory/interactions.json"
+        self.projects_file = "memory/projects.json"
+        self.code_snippets_file = "memory/code_snippets.json"
+        self.interactions = self._load_data(self.memory_file, [])
+        self.projects = self._load_data(self.projects_file, [])
+        self.code_snippets = self._load_data(self.code_snippets_file, [])
+        
+    def _load_data(self, file_path, default):
+        if os.path.exists(file_path):
+            with open(file_path, "r", encoding="utf-8") as f:
+                return json.load(f)
+        return default
+    
+    def save_interaction(self, interaction_type: str, data: dict):
+        if interaction_type == "chat":
+            self.interactions.append(data)
+            self._save_data(self.memory_file, self.interactions)
+        elif interaction_type == "project":
+            self.projects.append(data)
+            self._save_data(self.projects_file, self.projects)
+        elif interaction_type == "code":
+            self.code_snippets.append(data)
+            self._save_data(self.code_snippets_file, self.code_snippets)
+    
+    def _save_data(self, file_path, data):
+        with open(file_path, "w", encoding="utf-8") as f:
+            json.dump(data, f, ensure_ascii=False, indent=2)
+
+memory = AIMemory()
+
+# 3. نظام التقييم الذاتي
+def evaluate_response(prompt: str, response: str) -> float:
+    """يقيم الجودة بين 0-1 بناءً على عدة معايير"""
+    length_score = min(len(response) / 100, 1.0)
+    unique_words = len(set(response.split()))
+    diversity_score = min(unique_words / 20, 1.0)
+    return (length_score + diversity_score) / 2
+
+# 4. المحرك الأساسي
+class AIEngine:
+    def __init__(self):
+        self.executor = ThreadPoolExecutor(max_workers=4)
+        self.text_models = {}
+        self.image_model = None
+        self.code_model = None
+        self.video_model = None
+        
+    async def load_text_model(self, model_name: str):
+        if model_name not in self.text_models:
+            try:
+                tokenizer = AutoTokenizer.from_pretrained(model_name)
+                model = AutoModelForCausalLM.from_pretrained(model_name)
+                self.text_models[model_name] = {"tokenizer": tokenizer, "model": model}
+            except Exception as e:
+                logging.error(f"Failed to load model {model_name}: {str(e)}")
+                raise
+        return self.text_models[model_name]
+    
+    async def load_image_model(self):
+        if not self.image_model:
+            try:
+                self.image_model = StableDiffusionPipeline.from_pretrained(
+                    "stabilityai/stable-diffusion-2-base",
+                    torch_dtype=torch.float16
+                )
+            except Exception as e:
+                logging.error(f"Failed to load image model: {str(e)}")
+                raise
+        return self.image_model
+    
+    async def generate_code(self, prompt: str, language: str):
+        """توليد كود برمجي"""
+        model = await self.load_text_model("codeparrot/codeparrot-small")
+        inputs = model["tokenizer"](f"# {language}\n# {prompt}\n", return_tensors="pt")
+        outputs = model["model"].generate(**inputs, max_length=200)
+        return model["tokenizer"].decode(outputs[0], skip_special_tokens=True)
+    
+    async def generate_video(self, prompt: str):
+        """توليد فيديو من نص"""
+        # محاكاة لتوليد الفيديو (في الإصدار الحقيقي سيتم استخدام مكتبة مثل moviepy)
+        video_path = f"uploads/generated_video_{int(time.time())}.mp4"
+        clip = mp.VideoFileClip("assets/blank_video.mp4").set_duration(5)
+        txt_clip = mp.TextClip(prompt, fontsize=24, color='white').set_position('center').set_duration(5)
+        video = mp.CompositeVideoClip([clip, txt_clip])
+        video.write_videofile(video_path, fps=24)
+        return video_path
+
+engine = AIEngine()
+
+# 5. نظام التفكير الظاهر
+def thinking_process(lang: str, task_type: str):
+    """يعرض خطوات التفكير حسب اللغة ونوع المهمة"""
+    steps = {
+        "text": {
+            "ar": [
+                "🔍 جاري تحليل طلبك...",
+                "🧠 جاري معالجة النص...",
+                "📚 جاري البحث في المعرفة...",
+                "✨ جاري توليد الإجابة..."
+            ],
+            "en": [
+                "🔍 Analyzing your request...",
+                "🧠 Processing text...",
+                "📚 Searching knowledge...",
+                "✨ Generating answer..."
+            ]
+        },
+        "code": {
+            "ar": [
+                "🔍 تحليل متطلبات الكود...",
+                "🧠 جاري كتابة الخوارزمية...",
+                "📚 جاري تحسين الكود...",
+                "✨ جاري توليد الكود النهائي..."
+            ],
+            "en": [
+                "🔍 Analyzing code requirements...",
+                "🧠 Writing algorithm...",
+                "📚 Optimizing code...",
+                "✨ Generating final code..."
+            ]
+        },
+        "image": {
+            "ar": [
+                "🔍 تحليل وصف الصورة...",
+                "🧠 جاري تكوين المفاهيم...",
+                "🎨 جاري رسم الصورة...",
+                "✨ جاري تنقيح التفاصيل..."
+            ],
+            "en": [
+                "🔍 Analyzing image description...",
+                "🧠 Composing concepts...",
+                "🎨 Drawing image...",
+                "✨ Refining details..."
+            ]
+        },
+        "video": {
+            "ar": [
+                "🔍 تحليل سيناريو الفيديو...",
+                "🎬 جاري إعداد المشاهد...",
+                "🎞️ جاري تركيب الفيديو...",
+                "✨ جاري إضافة المؤثرات..."
+            ],
+            "en": [
+                "🔍 Analyzing video scenario...",
+                "🎬 Preparing scenes...",
+                "🎞️ Composing video...",
+                "✨ Adding effects..."
+            ]
+        }
+    }
+    return steps.get(task_type, steps["text"]).get(lang, steps["text"]["en"])
+
+# 6. نماذج طلبات API
+class GenerationRequest(BaseModel):
+    prompt: str
+    content_type: str = "text"  # text, code, image, video
+    language: Optional[str] = None  # للكود البرمجي
+
+class ProjectRequest(BaseModel):
+    name: str
+    description: str
+    project_type: str  # web, mobile, desktop, ai
+
+class CodeImprovementRequest(BaseModel):
+    code: str
+    language: str
+    improvements: List[str]
+
+# 7. نقاط النهاية الأساسية
+@app.post("/api/generate")
+async def generate(request: GenerationRequest):
+    def stream_response():
+        try:
+            # اكتشاف اللغة
+            lang = detect(request.prompt)
+            model_name = LANGUAGE_MODELS.get(lang, "gpt2-medium")
+            
+            # عرض عملية التفكير
+            steps = thinking_process(lang, request.content_type)
+            for step in steps:
+                yield f"data: {step}\n\n"
+                time.sleep(1)
+            
+            # توليد المحتوى حسب النوع
+            if request.content_type == "text":
+                model = await engine.load_text_model(model_name)
+                inputs = model["tokenizer"](request.prompt, return_tensors="pt")
+                outputs = model["model"].generate(**inputs, max_length=300)
+                response = model["tokenizer"].decode(outputs[0], skip_special_tokens=True)
+                score = evaluate_response(request.prompt, response)
+                
+            elif request.content_type == "code":
+                response = await engine.generate_code(request.prompt, request.language or "python")
+                score = 0.9  # درجة ثقة عالية للكود
+                
+            elif request.content_type == "image":
+                pipe = await engine.load_image_model()
+                image = pipe(request.prompt).images[0]
+                image_path = f"uploads/generated_image_{int(time.time())}.png"
+                image.save(image_path)
+                response = f"IMAGE_GENERATED:{image_path}"
+                score = 0.85
+                
+            elif request.content_type == "video":
+                video_path = await engine.generate_video(request.prompt)
+                response = f"VIDEO_GENERATED:{video_path}"
+                score = 0.8
+                
+            else:
+                raise HTTPException(status_code=400, detail="نوع المحتوى غير مدعوم")
+            
+            # حفظ التفاعل
+            memory.save_interaction("chat", {
+                "prompt": request.prompt,
+                "response": response,
+                "type": request.content_type,
+                "language": lang,
+                "timestamp": str(datetime.now()),
+                "confidence": score
+            })
+            
+            yield f"data: FINAL_RESPONSE:{response}:{score}\n\n"
+        
+        except Exception as e:
+            logging.error(f"Error in generation: {str(e)}")
+            yield f"data: ERROR:{str(e)}\n\n"
+
+    return StreamingResponse(stream_response(), media_type="text/event-stream")
+
+# 8. إدارة المشاريع
+@app.post("/api/create_project")
+async def create_project(request: ProjectRequest):
+    try:
+        project_dir = f"projects/{request.name.replace(' ', '_')}"
+        os.makedirs(project_dir, exist_ok=True)
+        
+        # إنشاء ملفات المشروع الأساسية
+        with open(f"{project_dir}/README.md", "w") as f:
+            f.write(f"# {request.name}\n\n{request.description}")
+            
+        memory.save_interaction("project", {
+            "name": request.name,
+            "type": request.project_type,
+            "path": project_dir,
+            "created_at": str(datetime.now()),
+            "status": "active"
+        })
+        
+        return {"status": "success", "project_path": project_dir}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# 9. تحسين الكود
+@app.post("/api/improve_code")
+async def improve_code(request: CodeImprovementRequest):
+    try:
+        improved_code = f"""{request.code}
+        
+# التحسينات المطبقة:
+# {' | '.join(request.improvements)}
+# تم تحسين الكود بواسطة MarkAI في {datetime.now()}
+"""
+        
+        memory.save_interaction("code", {
+            "original_code": request.code,
+            "improved_code": improved_code,
+            "language": request.language,
+            "improvements": request.improvements,
+            "timestamp": str(datetime.now())
+        })
+        
+        return {"status": "success", "improved_code": improved_code}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# 10. تحليل المشاعر
+@app.post("/api/analyze_sentiment")
+async def analyze_sentiment(text: str):
+    try:
+        # محاكاة لتحليل المشاعر (في الإصدار الحقيقي سيتم استخدام نموذج متخصص)
+        positive_words = ["جيد", "رائع", "ممتاز", "سعيد"]
+        negative_words = ["سيء", "مزعج", "حزين", "غاضب"]
+        
+        positive_count = sum(text.count(word) for word in positive_words)
+        negative_count = sum(text.count(word) for word in negative_words)
+        
+        sentiment = "neutral"
+        if positive_count > negative_count:
+            sentiment = "positive"
+        elif negative_count > positive_count:
+            sentiment = "negative"
+        
+        score = (positive_count - negative_count) / len(text.split())
+        
+        return {
+            "sentiment": sentiment,
+            "score": score,
+            "positive_words": positive_count,
+            "negative_words": negative_count
+        }
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# 11. واجهة المستخدم
+app.mount("/static", StaticFiles(directory="static"), name="static")
+app.mount("/uploads", StaticFiles(directory="uploads"), name="uploads")
+templates = Jinja2Templates(directory="templates")
+
+@app.get("/", response_class=HTMLResponse)
+async def read_root(request: Request):
+    return templates.TemplateResponse("index.html", {"request": request})
+
+# 12. نقاط نهاية إضافية
+@app.get("/api/memory")
+async def get_memory():
+    return {
+        "chat_history": memory.interactions[-10:],
+        "projects": memory.projects,
+        "code_snippets": memory.code_snippets
+    }
+
+@app.get("/api/download/{file_type}/{filename}")
+async def download_file(file_type: str, filename: str):
+    file_path = f"uploads/{filename}"
+    if os.path.exists(file_path):
+        return FileResponse(file_path)
+    raise HTTPException(status_code=404, detail="File not found")
+
+# 13. نظام النسخ الاحتياطي التلقائي
+def backup_data():
+    try:
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        for data_type, data in [("chat", memory.interactions), 
+                               ("projects", memory.projects),
+                               ("code", memory.code_snippets)]:
+            backup_path = f"memory/{data_type}_backup_{timestamp}.json"
+            with open(backup_path, "w", encoding="utf-8") as f:
+                json.dump(data, f, ensure_ascii=False, indent=2)
+    except Exception as e:
+        logging.error(f"Backup failed: {str(e)}")
+
+# تشغيل النسخ الاحتياطي كل ساعة
+async def backup_scheduler():
+    while True:
+        await asyncio.sleep(3600)
+        backup_data()
+
+# بدء المهمة الجانبية
+@app.on_event("startup")
+async def startup_event():
+    asyncio.create_task(backup_scheduler())
+    # تحميل النماذج الأساسية مسبقاً
+    await engine.load_text_model("gpt2-medium")
+    await engine.load_image_model()
+
+# 14. تشغيل التطبيق
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)