app.py

import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaTokenizer
from datasets import load_dataset
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
import os
import sys

# التحقق من تثبيت sentencepiece
try:
    import sentencepiece
    print("✓ sentencepiece is installed")
except ImportError:
    print("✗ sentencepiece is NOT installed - attempting to install...")
    import subprocess
    subprocess.check_call([sys.executable, "-m", "pip", "install", "sentencepiece", "protobuf"])
    import sentencepiece
    print("✓ sentencepiece installed successfully")

# تحميل النموذج والـ tokenizer
MODEL_NAME = "aab20abdullah/akin-yurt-finely"
DATASET_NAME = "aab20abdullah/turkmen-martyrs-dataset"

print("="*60)
print("Loading model and tokenizer...")
print("="*60)

# محاولة تحميل tokenizer بعدة طرق
tokenizer = None
tokenizer_loaded = False

# الطريقة 1: تجربة التحميل العادي
try:
    print("Attempt 1: Loading with default settings...")
    tokenizer = AutoTokenizer.from_pretrained(
        MODEL_NAME,
        trust_remote_code=True
    )
    tokenizer_loaded = True
    print("✓ Tokenizer loaded successfully with default settings")
except Exception as e:
    print(f"✗ Attempt 1 failed: {str(e)[:100]}")

# الطريقة 2: محاولة استخدام slow tokenizer
if not tokenizer_loaded:
    try:
        print("Attempt 2: Loading with use_fast=False...")
        tokenizer = AutoTokenizer.from_pretrained(
            MODEL_NAME,
            trust_remote_code=True,
            use_fast=False
        )
        tokenizer_loaded = True
        print("✓ Tokenizer loaded successfully with slow tokenizer")
    except Exception as e:
        print(f"✗ Attempt 2 failed: {str(e)[:100]}")

# الطريقة 3: محاولة استخدام LlamaTokenizer مباشرة
if not tokenizer_loaded:
    try:
        print("Attempt 3: Trying LlamaTokenizer directly...")
        tokenizer = LlamaTokenizer.from_pretrained(
            MODEL_NAME,
            trust_remote_code=True
        )
        tokenizer_loaded = True
        print("✓ Tokenizer loaded successfully with LlamaTokenizer")
    except Exception as e:
        print(f"✗ Attempt 3 failed: {str(e)[:100]}")

# الطريقة 4: استخدام tokenizer من نموذج متوافق كـ fallback
if not tokenizer_loaded:
    try:
        print("Attempt 4: Using fallback tokenizer from compatible model...")
        # استخدام tokenizer من نموذج Llama2 العربي
        fallback_models = [
            "mistralai/Mistral-7B-v0.1",
            "meta-llama/Llama-2-7b-hf",
            "facebook/opt-1.3b"
        ]
        for fallback_model in fallback_models:
            try:
                tokenizer = AutoTokenizer.from_pretrained(fallback_model)
                tokenizer_loaded = True
                print(f"✓ Using fallback tokenizer from {fallback_model}")
                break
            except:
                continue
    except Exception as e:
        print(f"✗ Attempt 4 failed: {str(e)[:100]}")

if not tokenizer_loaded:
    raise RuntimeError(
        "Failed to load tokenizer! Please check:\n"
        "1. Model name is correct: aab20abdullah/akin-yurt-finely\n"
        "2. You have access to the model (if private)\n"
        "3. sentencepiece is properly installed\n"
        "4. Check the model card for special requirements"
    )

# تعيين pad_token إذا لم يكن موجوداً
if tokenizer.pad_token is None:
    tokenizer.pad_token = tokenizer.eos_token
    print("✓ Set pad_token to eos_token")

print("\nLoading model...")
try:
    model = AutoModelForCausalLM.from_pretrained(
        MODEL_NAME,
        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
        device_map="auto" if torch.cuda.is_available() else None,
        trust_remote_code=True,
        low_cpu_mem_usage=True
    )
    model.eval()
    print("✓ Model loaded successfully")
except Exception as e:
    print(f"✗ Model loading failed: {e}")
    raise

# تحميل نموذج الـ embeddings للـ RAG
print("\nLoading embedding model...")
try:
    embedding_model = SentenceTransformer('sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2')
    print("✓ Embedding model loaded successfully")
except Exception as e:
    print(f"✗ Embedding model loading failed: {e}")
    raise

# تحميل الـ dataset
print("\nLoading dataset...")
dataset = None
try:
    dataset = load_dataset(DATASET_NAME, split='train')
    print(f"✓ Loaded dataset with {len(dataset)} examples")
except Exception as e:
    print(f"⚠ Error loading with split='train': {e}")
    try:
        dataset = load_dataset(DATASET_NAME)
        if isinstance(dataset, dict):
            split_name = list(dataset.keys())[0]
            dataset = dataset[split_name]
            print(f"✓ Loaded dataset split '{split_name}' with {len(dataset)} examples")
    except Exception as e2:
        print(f"⚠ Error loading dataset: {e2}")
        print("Creating demo dataset for testing...")
        from datasets import Dataset
        dataset = Dataset.from_dict({
            "text": [
                "شهيد تركماني من العراق، استشهد في الدفاع عن أرضه.",
                "من شهداء تركمان تلعفر الذين ضحوا بأرواحهم.",
                "معلومات عن تاريخ وبطولات شهداء تركمان العراق.",
                "سيرة شهيد من أبناء الشعب التركماني في العراق.",
                "تضحيات شهداء تركمان في مواجهة الإرهاب والظلم."
            ]
        })
        print(f"✓ Created demo dataset with {len(dataset)} examples")

# طباعة معلومات عن الـ dataset
if len(dataset) > 0:
    print(f"\nDataset info:")
    print(f"  - Columns: {list(dataset[0].keys())}")
    print(f"  - First item sample: {str(dataset[0])[:150]}...")

# إعداد الـ RAG system
print("\n" + "="*60)
print("Building RAG index...")
print("="*60)

# استخراج النصوص من الـ dataset
def extract_texts_from_dataset(dataset):
    """استخراج نصوص من dataset مع دعم بنى متعددة"""
    texts = []
    
    for idx, item in enumerate(dataset):
        try:
            text_parts = []
            
            # محاولة استخراج النصوص بطرق مختلفة
            for key, value in item.items():
                if value is None:
                    continue
                    
                # نصوص
                if isinstance(value, str) and len(value) > 5:
                    text_parts.append(f"{key}: {value}")
                
                # قوائم
                elif isinstance(value, list):
                    list_str = ", ".join([str(v) for v in value if v])
                    if list_str:
                        text_parts.append(f"{key}: {list_str}")
                
                # أرقام وقيم أخرى
                elif isinstance(value, (int, float, bool)):
                    text_parts.append(f"{key}: {value}")
            
            if text_parts:
                text = " | ".join(text_parts)
                texts.append(text)
            elif 'text' in item and item['text']:
                texts.append(str(item['text']))
                
        except Exception as e:
            if idx < 5:  # فقط للعناصر الأولى
                print(f"⚠ Warning: Could not process item {idx}: {e}")
            continue
    
    # Fallback إذا لم نستخرج أي نصوص
    if not texts:
        print("⚠ Warning: No texts extracted, using raw dataset items")
        texts = [str(item) for item in dataset[:100]]
    
    return texts

texts = extract_texts_from_dataset(dataset)
print(f"✓ Extracted {len(texts)} text chunks from dataset")
if texts:
    print(f"  Sample text: {texts[0][:150]}...")

# التحقق من وجود نصوص
if len(texts) == 0:
    print("⚠ Error: No texts found! Creating demo texts...")
    texts = [
        "معلومات افتراضية عن شهداء تركمان العراق",
        "بيانات تجريبية لاختبار نظام RAG",
        "نص تجريبي للتأكد من عمل النظام"
    ]

# إنشاء embeddings
print(f"\nCreating embeddings for {len(texts)} texts...")
try:
    embeddings = embedding_model.encode(texts, show_progress_bar=True, batch_size=32)
    embeddings = np.array(embeddings).astype('float32')
    print(f"✓ Created embeddings with shape {embeddings.shape}")
except Exception as e:
    print(f"✗ Error creating embeddings: {e}")
    raise

# إنشاء FAISS index
print("\nBuilding FAISS index...")
try:
    dimension = embeddings.shape[1]
    index = faiss.IndexFlatL2(dimension)
    index.add(embeddings)
    print(f"✓ FAISS index built with {index.ntotal} vectors")
except Exception as e:
    print(f"✗ Error building FAISS index: {e}")
    raise

print("\n" + "="*60)
print("✓ RAG system ready!")
print("="*60 + "\n")

def retrieve_relevant_context(query, k=3):
    """استرجاع السياق الأكثر صلة بالاستعلام"""
    try:
        query_embedding = embedding_model.encode([query])
        query_embedding = np.array(query_embedding).astype('float32')
        
        distances, indices = index.search(query_embedding, k)
        
        relevant_texts = [texts[idx] for idx in indices[0] if idx < len(texts)]
        return "\n\n".join(relevant_texts)
    except Exception as e:
        print(f"Error in retrieve_relevant_context: {e}")
        return "خطأ في استرجاع المعلومات"

def generate_response(message, history, temperature=0.7, max_tokens=512, use_rag=True):
    """توليد الرد باستخدام النموذج مع أو بدون RAG"""
    
    try:
        conversation = []
        
        # RAG context
        if use_rag:
            try:
                context = retrieve_relevant_context(message)
                system_message = f"""أنت مساعد ذكي. استخدم المعلومات التالية للإجابة على السؤال:

المعلومات المرجعية:
{context}

أجب بناءً على هذه المعلومات. إذا لم تكن المعلومات كافية، قل ذلك."""
                
                conversation.append({"role": "system", "content": system_message})
            except Exception as e:
                print(f"⚠ RAG retrieval failed: {e}")
        
        # إضافة تاريخ المحادثة
        for user_msg, assistant_msg in history:
            conversation.append({"role": "user", "content": user_msg})
            if assistant_msg:
                conversation.append({"role": "assistant", "content": assistant_msg})
        
        # إضافة الرسالة الحالية
        conversation.append({"role": "user", "content": message})
        
        # تحويل إلى prompt
        try:
            prompt = tokenizer.apply_chat_template(
                conversation,
                tokenize=False,
                add_generation_prompt=True
            )
        except Exception as e:
            print(f"⚠ Chat template failed, using simple format: {e}")
            prompt = "\n".join([f"{msg['role']}: {msg['content']}" for msg in conversation])
            prompt += "\nassistant: "
        
        # Tokenize
        inputs = tokenizer(
            prompt,
            return_tensors="pt",
            truncation=True,
            max_length=2048
        )
        
        if torch.cuda.is_available():
            inputs = {k: v.to(model.device) for k, v in inputs.items()}
        
        # Generate
        with torch.no_grad():
            outputs = model.generate(
                **inputs,
                max_new_tokens=max_tokens,
                temperature=temperature,
                do_sample=temperature > 0,
                top_p=0.9,
                pad_token_id=tokenizer.pad_token_id if tokenizer.pad_token_id else tokenizer.eos_token_id,
                eos_token_id=tokenizer.eos_token_id,
                repetition_penalty=1.1
            )
        
        # Decode
        response = tokenizer.decode(
            outputs[0][inputs['input_ids'].shape[1]:],
            skip_special_tokens=True
        )
        
        return response.strip()
        
    except Exception as e:
        error_msg = f"عذراً، حدث خطأ: {str(e)}"
        print(f"✗ Error in generate_response: {e}")
        import traceback
        traceback.print_exc()
        return error_msg

# إنشاء Gradio interface
with gr.Blocks(title="Akin Yurt with RAG", theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # 🤖 Akin Yurt Model with RAG
    
    نموذج **Akin Yurt** مع نظام **Retrieval-Augmented Generation (RAG)** لبيانات شهداء تركمان.
    
    يمكنك تفعيل أو تعطيل RAG لمقارنة النتائج.
    """)
    
    with gr.Row():
        with gr.Column(scale=2):
            chatbot = gr.Chatbot(
                height=500,
                label="المحادثة",
                show_label=True,
                avatar_images=(None, "🤖")
            )
            
            with gr.Row():
                msg = gr.Textbox(
                    label="رسالتك",
                    placeholder="اكتب سؤالك هنا...",
                    show_label=False,
                    scale=4
                )
                submit = gr.Button("إرسال", variant="primary", scale=1)
            
            clear = gr.Button("مسح المحادثة")
                
        with gr.Column(scale=1):
            gr.Markdown("### ⚙️ الإعدادات")
            
            use_rag = gr.Checkbox(
                label="استخدام RAG",
                value=True,
                info="تفعيل استرجاع المعلومات من قاعدة البيانات"
            )
            
            temperature = gr.Slider(
                minimum=0.1,
                maximum=2.0,
                value=0.7,
                step=0.1,
                label="Temperature",
                info="يتحكم في عشوائية الإجابات"
            )
            
            max_tokens = gr.Slider(
                minimum=128,
                maximum=2048,
                value=512,
                step=128,
                label="Max Tokens",
                info="الحد الأقصى لطول الإجابة"
            )
            
            gr.Markdown(f"""
            ### 📊 معلومات النظام
            
            - **النموذج**: {MODEL_NAME}
            - **البيانات**: {DATASET_NAME}
            - **عدد السجلات**: {len(texts)}
            - **Tokenizer**: {'✓ Loaded' if tokenizer_loaded else '✗ Failed'}
            - **Device**: {'GPU' if torch.cuda.is_available() else 'CPU'}
            
            ### 💡 نصائح
            
            - جرّب تشغيل/إيقاف RAG لرؤية الفرق
            - Temperature منخفض = إجابات دقيقة
            - Temperature عالي = إجابات إبداعية
            """)
    
    def user_message(message, history):
        return "", history + [[message, None]]
    
    def bot_response(history, temperature, max_tokens, use_rag):
        message = history[-1][0]
        response = generate_response(
            message,
            history[:-1],
            temperature=temperature,
            max_tokens=max_tokens,
            use_rag=use_rag
        )
        history[-1][1] = response
        return history
    
    # Event handlers
    msg.submit(user_message, [msg, chatbot], [msg, chatbot], queue=False).then(
        bot_response, [chatbot, temperature, max_tokens, use_rag], chatbot
    )
    
    submit.click(user_message, [msg, chatbot], [msg, chatbot], queue=False).then(
        bot_response, [chatbot, temperature, max_tokens, use_rag], chatbot
    )
    
    clear.click(lambda: None, None, chatbot, queue=False)

if __name__ == "__main__":
    demo.launch()