add project

Dockerfile

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+# ================================
+# 1️⃣ استخدم Python كـ base image
+# ================================
+FROM python:3.10-slim
+
+# ================================
+# 2️⃣ ضبط مكان العمل
+# ================================
+WORKDIR /app
+
+# ================================
+# 3️⃣ نسخ الملفات
+# ================================
+COPY requirements.txt requirements.txt
+COPY . .
+
+# ================================
+# 4️⃣ تثبيت المكتبات
+# ================================
+RUN pip install --no-cache-dir -r requirements.txt
+
+# ================================
+# 5️⃣ تحديد المنفذ (HF Spaces يشتغل على 7860 أو 8080)
+# ================================
+EXPOSE 7860
+
+# ================================
+# 6️⃣ تشغيل السيرفر
+# ================================
+CMD ["python", "app.py"]

app.py

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+# ===============================
+# 1️⃣ استيراد المكتبات
+# ===============================
+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
+from datasets import Dataset
+import os, json
+
+# ===============================
+# 2️⃣ إعداد المتغيرات
+# ===============================
+MODEL_NAME = "Qwen/Qwen2.5-0.5B-Instruct"
+SAVE_DIR = "./lora_model"
+DATASET_FILE = "dataset.json"
+
+# ===============================
+# 3️⃣ تحميل الموديل الأساسي
+# ===============================
+print("🔄 Loading base model...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME,
+    device_map="auto",
+    torch_dtype=torch.float16
+)
+print("✅ Base model loaded!")
+
+# ===============================
+# 4️⃣ 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)
+print("✅ LoRA model ready!")
+
+# ===============================
+# 🟢 Auto load LoRA weights on startup
+# ===============================
+if os.path.exists(SAVE_DIR):
+    try:
+        model = AutoModelForCausalLM.from_pretrained(
+            MODEL_NAME,
+            device_map="auto",
+            torch_dtype=torch.float16
+        )
+        model = PeftModel.from_pretrained(model, SAVE_DIR)
+        print("✅ LoRA weights auto-loaded from disk! [Fine-tuned model running]")
+    except Exception as e:
+        print("⚠️ Failed to auto-load LoRA weights:", str(e))
+else:
+    print("ℹ️ No saved LoRA weights found, running with base model.")
+
+# ===============================
+# 5️⃣ إعداد Flask API
+# ===============================
+app = Flask(__name__)
+CORS(app)
+
+trainer = None
+current_dataset = None
+
+# ===============================
+# 🟢 تحميل الداتا من الملف عند البداية
+# ===============================
+def load_dataset():
+    global current_dataset
+    if os.path.exists(DATASET_FILE):
+        with open(DATASET_FILE, "r", encoding="utf-8") as f:
+            try:
+                data = json.load(f)
+            except:
+                data = []
+
+        if data:
+            formatted = []
+            for item in 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)
+            print(f"✅ Loaded {len(formatted)} records from dataset.json")
+        else:
+            current_dataset = None
+    else:
+        current_dataset = None
+
+load_dataset()
+
+# ===============================
+# 5a️⃣ توليد نصوص (generate)
+# ===============================
+@app.route("/generate", methods=["POST"])
+def generate():
+    try:
+        data = request.json
+        prompt = data.get("prompt", "").strip()
+
+        if current_dataset is None:
+            return jsonify({
+                "messages": [
+                    {"role": "assistant", "content": "⚖️ أنا زين، مساعد ذكي للاستشارات القانونية. برجاء طرح سؤال قانوني."}
+                ]
+            })
+
+        # البحث في الداتا
+        for item in current_dataset:
+            lines = item["text"].split("\n")
+            for line in lines:
+                if line.lower().startswith("user:") and prompt in line:
+                    response_messages = []
+                    for l in lines:
+                        if ":" in l:
+                            role, content = l.split(":", 1)
+                            response_messages.append({
+                                "role": role.strip().lower(),
+                                "content": content.strip()
+                            })
+                    return jsonify({"messages": response_messages})
+
+        # 🟡 لو مفيش تطابق
+        return jsonify({
+            "messages": [
+                {"role": "assistant", "content": "⚖️ هذا السؤال ليس من ضمن اختصاصي. أنا زين مساعد قانوني مخصص للإجابة على الاستشارات القانونية فقط."}
+            ]
+        })
+
+    except Exception as e:
+        return jsonify({"messages": [{"role": "assistant", "content": f"خطأ: {str(e)}"}]})
+
+# ===============================
+# 5b️⃣ تدريب LoRA (Async) + حفظ
+# ===============================
+@app.route("/train", methods=["POST"])
+def train():
+    global trainer, current_dataset, model
+    try:
+        data = []
+        epochs = 1
+        batch_size = 1
+
+        # 📌 1) JSON request
+        if request.is_json:
+            body = request.get_json()
+            data = body.get("data", [])
+            epochs = int(body.get("epochs", 1))
+            batch_size = int(body.get("batch_size", 1))
+
+        # 📌 2) Form-data request
+        else:
+            epochs = int(request.form.get("epochs", 1))
+            batch_size = int(request.form.get("batch_size", 1))
+            file = request.files.get("file")
+            if file:
+                data = json.load(file)
+            else:
+                return jsonify({"error": "No file uploaded"}), 400
+
+        # دمج وإزالة التكرارات
+        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 = [], set()
+        for item in combined:
+            key = json.dumps(item, ensure_ascii=False)
+            if key not in seen:
+                seen.add(key)
+                unique_data.append(item)
+
+        # الحفظ
+        with open(DATASET_FILE, "w", encoding="utf-8") as f:
+            json.dump(unique_data, f, ensure_ascii=False, indent=2)
+
+        # تجهيز الداتا
+        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)
+
+        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=True,
+            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({"status": "training started", "epochs": epochs, "batch_size": batch_size, "records": len(unique_data)})
+
+    except Exception as e:
+        return jsonify({"error": str(e)})
+
+# ===============================
+# 6️⃣ تشغيل Flask
+# ===============================
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860, threaded=True)

requirements.txt

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+flask
+flask-cors
+transformers
+torch
+peft
+datasets