Merge branch 'Ai' into main

app.py

@@ -1,18 +1,25 @@
 # ===============================
 # 1️⃣ استيراد المكتبات
 # ===============================
-from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
-from peft import LoraConfig, get_peft_model, TaskType, PeftModel
+import os
+import threading, torch, json
 from flask import Flask, request, jsonify
 from flask_cors import CORS
-import threading, torch
 from datasets import Dataset
-import os, json
+from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
+from peft import LoraConfig, get_peft_model, TaskType, PeftModel
+
+# ===============================
+# 🟢 إصلاح مشكلة الكاش في Hugging Face Spaces
+# ===============================
+os.environ["HF_HOME"] = "/app/cache"
+os.environ["TRANSFORMERS_CACHE"] = "/app/cache"
 
 # ===============================
 # 2️⃣ إعداد المتغيرات
 # ===============================
 MODEL_NAME = "Qwen/Qwen2.5-0.5B-Instruct"
+FLASK_PORT = 7860   # Spaces بيشتغل على 7860
 SAVE_DIR = "./lora_model"
 DATASET_FILE = "dataset.json"
 
@@ -46,12 +53,12 @@ print("✅ LoRA model ready!")
 # ===============================
 if os.path.exists(SAVE_DIR):
     try:
-        model = AutoModelForCausalLM.from_pretrained(
+        base_model = AutoModelForCausalLM.from_pretrained(
             MODEL_NAME,
             device_map="auto",
             torch_dtype=torch.float16
         )
-        model = PeftModel.from_pretrained(model, SAVE_DIR)
+        model = PeftModel.from_pretrained(base_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))
@@ -67,9 +74,7 @@ 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):
@@ -99,140 +104,7 @@ def load_dataset():
 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
+# 6️⃣ تشغيل Flask (بدون ngrok في Spaces)
 # ===============================
 if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=7860, threaded=True)
+    app.run(host="0.0.0.0", port=FLASK_PORT, threaded=True)