edits

app.py

@@ -2,26 +2,98 @@
 # 1️⃣ استيراد المكتبات
 # ===============================
 import os
-import requests
-import threading
-import json
+os.environ["HF_HOME"] = "/tmp"
+os.environ["TRANSFORMERS_CACHE"] = "/tmp"
+os.environ["PEFT_HOME"] = "/tmp"
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
+from peft import LoraConfig, get_peft_model, TaskType, PeftModel
 from flask import Flask, request, jsonify
 from flask_cors import CORS
+import threading, torch, json
 from datasets import Dataset
 import difflib
 
 # ===============================
 # 2️⃣ إعداد المتغيرات
 # ===============================
-HF_API_URL = "https://api-inference.huggingface.co/models/AhmedBenElsayed/Graduation_Project"
-HF_API_TOKEN = "YOUR_HUGGINGFACE_API_TOKEN"  # ضع التوكن هنا
-HEADERS = {"Authorization": f"Bearer {HF_API_TOKEN}"}
-
+MODEL_NAME = "Qwen/Qwen2.5-0.5B-Instruct"
 FLASK_PORT = 7860
+
+SAVE_DIR = "/tmp/lora_model"
 DATA_DIR = "/tmp/data"
-DATASET_FILE = os.path.join(DATA_DIR, "dataset.json")
 os.makedirs(DATA_DIR, exist_ok=True)
+os.makedirs(SAVE_DIR, exist_ok=True)
+
+DATASET_FILE = os.path.join(DATA_DIR, "dataset.json")
+
+# ===============================
+# 3️⃣ تحديد الجهاز
+# ===============================
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print("🔹 Using device:", device)
+
+# ===============================
+# 4️⃣ تحميل الموديل الأساسي
+# ===============================
+print("🔄 Loading base model...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+try:
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_NAME,
+        device_map="auto" if device=="cuda" else {"": "cpu"},
+        torch_dtype=torch.float16 if device=="cuda" else torch.float32,
+        load_in_8bit=(device=="cuda"),
+        trust_remote_code=True
+    )
+    model.to(device)
+    print("✅ Base model loaded!")
+except Exception as e:
+    print("⚠️ Failed to load base model:", e)
+    raise e
+
+# ===============================
+# 5️⃣ إعداد LoRA
+# ===============================
+lora_config = LoraConfig(
+    task_type=TaskType.CAUSAL_LM,
+    r=2,
+    lora_alpha=8,
+    lora_dropout=0.05,
+    target_modules=["q_proj", "v_proj"]
+)
+model = get_peft_model(model, lora_config)
+model.to(device)
+print("✅ LoRA model ready on device:", device)
 
+# ===============================
+# 6️⃣ Auto load LoRA weights
+# ===============================
+if os.path.exists(SAVE_DIR):
+    try:
+        base_model = AutoModelForCausalLM.from_pretrained(
+            MODEL_NAME,
+            device_map="auto" if device=="cuda" else {"": "cpu"},
+            torch_dtype=torch.float16 if device=="cuda" else torch.float32,
+            load_in_8bit=(device=="cuda"),
+            trust_remote_code=True
+        )
+        model = PeftModel.from_pretrained(base_model, SAVE_DIR)
+        model.to(device)
+        print("✅ LoRA weights auto-loaded from disk!")
+    except Exception as e:
+        print("⚠️ Failed to auto-load LoRA weights:", e)
+else:
+    print("ℹ️ No saved LoRA weights found, running with base model.")
+
+# ===============================
+# 7️⃣ إعداد Flask API
+# ===============================
+app = Flask(__name__)
+CORS(app)
+
+trainer = None
 current_dataset = None
 
 # Fake DB
@@ -32,7 +104,137 @@ users_db = {
 }
 
 # ===============================
-# 3️⃣ تحميل الداتا
+# 7a️⃣ توليد نصوص
+# ===============================
+@app.route("/generate", methods=["POST"])
+def generate():
+    try:
+        data = request.json or {}
+        prompt = str(data.get("prompt", "")).strip()
+        user_id = str(data.get("userId", "")).strip()
+        focus_level = float(data.get("focus_level", 0.5))
+        n_examples = int(data.get("n_examples", 2))
+
+        user_info = users_db.get(user_id)
+        user_name = user_info["name"] if user_info else "مستخدم"
+
+        if not prompt:
+            return jsonify({"messages": [{"role": "assistant", "content": "⚠️ الرجاء ارسال سؤال أو استفسار صحيح."}]}), 400
+
+        # =====================
+        # حالة: لا يوجد داتا تدريبية
+        # =====================
+        if current_dataset is None:
+            base_prompt = (
+                f"أنت مساعد قانوني ذكي اسمه زين 👨‍⚖️.\n"
+                f"مهمة زين: الإجابة على الأسئلة القانونية بأسلوب واضح ومبسط "
+                f"مع الحفاظ على الدقة القانونية.\n\n"
+                f"السؤال:\n{prompt}\n\nالإجابة:"
+            )
+
+            inputs = tokenizer(base_prompt, return_tensors="pt", truncation=True, max_length=1024)
+            inputs = {k: v.to(model.device) for k, v in inputs.items()}
+            input_len = inputs["input_ids"].shape[-1]
+
+            outputs = model.generate(
+                **inputs,
+                max_new_tokens=200,
+                temperature=0.7,
+                top_p=0.9,
+                do_sample=True,
+                pad_token_id=tokenizer.eos_token_id,
+                repetition_penalty=1.1
+            )
+
+            reply = tokenizer.decode(outputs[0][input_len:], skip_special_tokens=True).strip()
+
+            return jsonify({
+                "messages": [
+                    {"role": "assistant", "content": f"أهلاً {user_name} 👋"},
+                    {"role": "assistant", "content": reply}
+                ],
+                "meta": {"mode": "base"}
+            })
+
+        # =====================
+        # حالة: يوجد داتا تدريبية
+        # =====================
+        candidates = []
+        for item in current_dataset:
+            text = item.get("text", "")
+            ratio = difflib.SequenceMatcher(None, prompt.lower(), text.lower()).ratio()
+            candidates.append((ratio, text))
+
+        candidates.sort(reverse=True, key=lambda x: x[0])
+        top_texts = [t for r, t in candidates[:n_examples] if r > 0.05]
+
+        context_parts = []
+        for t in top_texts:
+            lines = t.split("\n")
+            u_lines, a_lines = [], []
+            for l in lines:
+                if ":" in l:
+                    role, content = l.split(":", 1)
+                    role = role.strip().lower()
+                    if role.startswith("user"):
+                        u_lines.append(content.strip())
+                    elif role.startswith("assistant") or role.startswith("ai") or role.startswith("system"):
+                        a_lines.append(content.strip())
+            if u_lines or a_lines:
+                context_parts.append("User: " + " ".join(u_lines) + "\nAssistant: " + " ".join(a_lines))
+
+        context_text = "\n\n---\n\n".join(context_parts)
+
+        instruction = (
+            "التعليمات: استعن بالأمثلة التدريبية كمرجع أساسي، "
+            "لكن أجب بأسلوبك الخاص وبكلمات مختلفة. "
+            "حافظ على صحة المعلومة القانونية ولا تنسخ النصوص كما هي."
+        )
+
+        if context_text and focus_level > 0.0:
+            emphasis = "ركز بشكل كبير على الأمثلة" if focus_level > 0.7 else "خذ الأمثلة كمرجع فقط"
+            final_prompt = (
+                f"{instruction}\n\n{emphasis}\n\n"
+                f"أمثلة تدريبية (مستوى التركيز={focus_level}):\n{context_text}\n\n"
+                f"السؤال:\n{prompt}\n\nالإجابة:"
+            )
+        else:
+            final_prompt = f"{instruction}\n\nالسؤال:\n{prompt}\n\nالإجابة:"
+
+        # ==== توليد
+        inputs = tokenizer(final_prompt, return_tensors="pt", truncation=True, max_length=1024)
+        inputs = {k: v.to(model.device) for k, v in inputs.items()}
+        input_len = inputs["input_ids"].shape[-1]
+
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=200,
+            temperature=0.6,
+            top_p=0.9,
+            do_sample=True,
+            pad_token_id=tokenizer.eos_token_id,
+            repetition_penalty=1.1
+        )
+
+        reply = tokenizer.decode(outputs[0][input_len:], skip_special_tokens=True).strip()
+
+        return jsonify({
+            "messages": [
+                {"role": "assistant", "content": f"أهلاً {user_name} 👋"},
+                {"role": "assistant", "content": reply}
+            ],
+            "meta": {
+                "mode": "trained",
+                "used_examples": len(context_parts),
+                "focus_level": focus_level
+            }
+        })
+
+    except Exception as e:
+        return jsonify({"messages": [{"role": "assistant", "content": f"❌ خطأ: {str(e)}"}]})
+
+# ===============================
+# 7b️⃣ تحميل الداتا
 # ===============================
 def load_dataset():
     global current_dataset
@@ -62,110 +264,115 @@ def load_dataset():
 load_dataset()
 
 # ===============================
-# 4️⃣ Flask API
+# 7c️⃣ تدريب LoRA (Async)
 # ===============================
-app = Flask(__name__)
-CORS(app)
-
-# ----- توليد النصوص -----
-@app.route("/generate", methods=["POST"])
-def generate():
+@app.route("/train", methods=["POST"])
+def train():
+    global trainer, current_dataset, model
     try:
-        data = request.json or {}
-        prompt = str(data.get("prompt", "")).strip()
-        user_id = str(data.get("userId", "")).strip()
-        focus_level = float(data.get("focus_level", 0.5))
-        n_examples = int(data.get("n_examples", 2))
+        data = []
+        epochs = 1
+        batch_size = 1
 
-        user_info = users_db.get(user_id)
-        user_name = user_info["name"] if user_info else "مستخدم"
-
-        if not prompt:
-            return jsonify({"messages": [{"role": "assistant", "content": "⚠️ الرجاء ارسال سؤال أو استفسار صحيح."}]}), 400
+        if request.is_json:
+            body = request.get_json()
+            if isinstance(body, list):
+                data = body
+            elif isinstance(body, dict) and "data" in body:
+                data = body["data"]
+            else:
+                return jsonify({"error": "JSON format not recognized"}), 400
 
-        # =====================
-        # حالة: لا يوجد داتا تدريبية
-        # =====================
-        if current_dataset is None:
-            final_prompt = (
-                f"أنت مساعد قانوني ذكي اسمه زين 👨‍⚖️.\n"
-                f"مهمة زين: الإجابة على الأسئلة القانونية بأسلوب واضح ومبسط.\n\n"
-                f"السؤال:\n{prompt}\n\nالإجابة:"
-            )
+            epochs = int(body.get("epochs", 1)) if isinstance(body, dict) else 1
+            batch_size = int(body.get("batch_size", 1)) if isinstance(body, dict) else 1
         else:
-            # =====================
-            # حالة: يوجد داتا تدريبية
-            # =====================
-            candidates = []
-            for item in current_dataset:
-                text = item.get("text", "")
-                ratio = difflib.SequenceMatcher(None, prompt.lower(), text.lower()).ratio()
-                candidates.append((ratio, text))
-            candidates.sort(reverse=True, key=lambda x: x[0])
-            top_texts = [t for r, t in candidates[:n_examples] if r > 0.05]
-
-            context_parts = []
-            for t in top_texts:
-                lines = t.split("\n")
-                u_lines, a_lines = [], []
-                for l in lines:
-                    if ":" in l:
-                        role, content = l.split(":", 1)
-                        role = role.strip().lower()
-                        if role.startswith("user"):
-                            u_lines.append(content.strip())
-                        elif role.startswith("assistant") or role.startswith("ai") or role.startswith("system"):
-                            a_lines.append(content.strip())
-                if u_lines or a_lines:
-                    context_parts.append("User: " + " ".join(u_lines) + "\nAssistant: " + " ".join(a_lines))
-
-            context_text = "\n\n---\n\n".join(context_parts)
-            instruction = (
-                "التعليمات: استعن بالأمثلة التدريبية كمرجع، "
-                "لكن أجب بأسلوبك الخاص وبكلمات مختلفة. "
-                "حافظ على صحة المعلومة القانونية ولا تنسخ النصوص كما هي."
-            )
-            if context_text and focus_level > 0.0:
-                emphasis = "ركز بشكل كبير على الأمثلة" if focus_level > 0.7 else "خذ الأمثلة كمرجع فقط"
-                final_prompt = (
-                    f"{instruction}\n\n{emphasis}\n\n"
-                    f"أمثلة تدريبية (مستوى التركيز={focus_level}):\n{context_text}\n\n"
-                    f"السؤال:\n{prompt}\n\nالإجابة:"
-                )
+            file = request.files.get("file")
+            if file:
+                data = json.load(file)
+                epochs = int(request.form.get("epochs", 1))
+                batch_size = int(request.form.get("batch_size", 1))
+            else:
+                return jsonify({"error": "No file uploaded or JSON provided"}), 400
+
+        old_data = []
+        if os.path.exists(DATASET_FILE):
+            with open(DATASET_FILE, "r", encoding="utf-8") as f:
+                try:
+                    old_data = json.load(f)
+                except:
+                    old_data = []
+
+        combined = old_data + data
+        unique_data, seen_texts = [], set()
+        for item in combined:
+            if "messages" in item:
+                text_key = "\n".join([f"{m['role'].strip().lower()}:{m['content'].strip()}" for m in item["messages"]])
+            elif "text" in item:
+                text_key = item["text"].strip()
             else:
-                final_prompt = f"{instruction}\n\nالسؤال:\n{prompt}\n\nالإجابة:"
+                continue
+            if text_key not in seen_texts:
+                seen_texts.add(text_key)
+                unique_data.append(item)
 
-        # ==== استدعاء API HuggingFace
-        payload = {"inputs": final_prompt}
-        response = requests.post(HF_API_URL, headers=HEADERS, json=payload, timeout=60)
+        with open(DATASET_FILE, "w", encoding="utf-8") as f:
+            json.dump(unique_data, f, ensure_ascii=False, indent=2)
 
-        if response.status_code != 200:
-            return jsonify({"messages": [{"role": "assistant", "content": f"❌ خطأ من HuggingFace API: {response.text}"}]})
+        formatted = []
+        for item in unique_data:
+            if "messages" in item:
+                conversation = ""
+                for msg in item["messages"]:
+                    conversation += f"{msg['role'].capitalize()}: {msg['content']}\n"
+                formatted.append({"text": conversation.strip()})
+            elif "text" in item:
+                formatted.append({"text": item["text"]})
+        current_dataset = Dataset.from_list(formatted)
 
-        result = response.json()
-        if isinstance(result, list) and "generated_text" in result[0]:
-            reply = result[0]["generated_text"]
-        else:
-            reply = str(result)
+        def tokenize_function(examples):
+            return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=256)
+
+        tokenized_dataset = current_dataset.map(tokenize_function, batched=True, remove_columns=["text"])
+
+        training_args = TrainingArguments(
+            output_dir=SAVE_DIR,
+            per_device_train_batch_size=batch_size,
+            num_train_epochs=epochs,
+            logging_steps=2,
+            save_strategy="epoch",
+            fp16=torch.cuda.is_available(),
+            report_to=None
+        )
+
+        trainer = Trainer(model=model, args=training_args, train_dataset=tokenized_dataset)
+
+        def run_training():
+            print("🔄 Training started...")
+            trainer.train()
+            trainer.save_model(SAVE_DIR)
+            print("✅ Training completed & model saved!")
+
+        threading.Thread(target=run_training, daemon=True).start()
 
         return jsonify({
-            "messages": [
-                {"role": "assistant", "content": f"أهلاً {user_name} 👋"},
-                {"role": "assistant", "content": reply}
-            ],
-            "meta": {"mode": "api", "focus_level": focus_level}
+            "status": "training started",
+            "epochs": epochs,
+            "batch_size": batch_size,
+            "records": len(unique_data)
         })
 
     except Exception as e:
-        return jsonify({"messages": [{"role": "assistant", "content": f"❌ خطأ: {str(e)}"}]})
+        return jsonify({"error": str(e)})
 
-# ----- الصفحة الرئيسية -----
+# ===============================
+# 8️⃣ تشغيل Flask
+# ===============================
 @app.route("/", methods=["GET"])
 def home():
     return jsonify({
         "status": "ok",
         "message": "Graduation Project API is running 🚀",
-        "endpoints": ["/generate"]
+        "endpoints": ["/generate", "/train"]
     })
 
 if __name__ == "__main__":