edits

app.py

@@ -2,98 +2,26 @@
 # 1️⃣ استيراد المكتبات
 # ===============================
 import os
-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
+import requests
+import threading
+import json
 from flask import Flask, request, jsonify
 from flask_cors import CORS
-import threading, torch, json
 from datasets import Dataset
 import difflib
 
 # ===============================
 # 2️⃣ إعداد المتغيرات
 # ===============================
-MODEL_NAME = "Qwen/Qwen2.5-0.5B-Instruct"
-FLASK_PORT = 7860
+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}"}
 
-SAVE_DIR = "/tmp/lora_model"
+FLASK_PORT = 7860
 DATA_DIR = "/tmp/data"
-os.makedirs(DATA_DIR, exist_ok=True)
-os.makedirs(SAVE_DIR, exist_ok=True)
-
 DATASET_FILE = os.path.join(DATA_DIR, "dataset.json")
+os.makedirs(DATA_DIR, exist_ok=True)
 
-# ===============================
-# 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
@@ -104,137 +32,7 @@ users_db = {
 }
 
 # ===============================
-# 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
+# 3️⃣ تحميل الداتا
 # ===============================
 def load_dataset():
     global current_dataset
@@ -264,115 +62,110 @@ def load_dataset():
 load_dataset()
 
 # ===============================
-# 7c️⃣ تدريب LoRA (Async)
+# 4️⃣ Flask API
 # ===============================
-@app.route("/train", methods=["POST"])
-def train():
-    global trainer, current_dataset, model
-    try:
-        data = []
-        epochs = 1
-        batch_size = 1
+app = Flask(__name__)
+CORS(app)
 
-        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
+# ----- توليد النصوص -----
+@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))
 
-            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:
-            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
+        user_info = users_db.get(user_id)
+        user_name = user_info["name"] if user_info else "مستخدم"
 
-        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 = []
+        if not prompt:
+            return jsonify({"messages": [{"role": "assistant", "content": "⚠️ الرجاء ارسال سؤال أو استفسار صحيح."}]}), 400
 
-        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()
+        # =====================
+        # 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:"
+            )
+        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:"
+                )
             else:
-                continue
-            if text_key not in seen_texts:
-                seen_texts.add(text_key)
-                unique_data.append(item)
-
-        with open(DATASET_FILE, "w", encoding="utf-8") as f:
-            json.dump(unique_data, f, ensure_ascii=False, indent=2)
-
-        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)
+                final_prompt = f"{instruction}\n\nالسؤال:\n{prompt}\n\nالإجابة:"
 
-        def tokenize_function(examples):
-            return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=256)
+        # ==== استدعاء API HuggingFace
+        payload = {"inputs": final_prompt}
+        response = requests.post(HF_API_URL, headers=HEADERS, json=payload, timeout=60)
 
-        tokenized_dataset = current_dataset.map(tokenize_function, batched=True, remove_columns=["text"])
+        if response.status_code != 200:
+            return jsonify({"messages": [{"role": "assistant", "content": f"❌ خطأ من HuggingFace API: {response.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()
+        result = response.json()
+        if isinstance(result, list) and "generated_text" in result[0]:
+            reply = result[0]["generated_text"]
+        else:
+            reply = str(result)
 
         return jsonify({
-            "status": "training started",
-            "epochs": epochs,
-            "batch_size": batch_size,
-            "records": len(unique_data)
+            "messages": [
+                {"role": "assistant", "content": f"أهلاً {user_name} 👋"},
+                {"role": "assistant", "content": reply}
+            ],
+            "meta": {"mode": "api", "focus_level": focus_level}
         })
 
     except Exception as e:
-        return jsonify({"error": str(e)})
+        return jsonify({"messages": [{"role": "assistant", "content": f"❌ خطأ: {str(e)}"}]})
 
-# ===============================
-# 8️⃣ تشغيل Flask
-# ===============================
+# ----- الصفحة الرئيسية -----
 @app.route("/", methods=["GET"])
 def home():
     return jsonify({
         "status": "ok",
         "message": "Graduation Project API is running 🚀",
-        "endpoints": ["/generate", "/train"]
+        "endpoints": ["/generate"]
     })
 
 if __name__ == "__main__":