Update app.py

app.py

@@ -3,107 +3,82 @@ from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments,
 from peft import LoraConfig, get_peft_model
 from datasets import load_dataset
 import gradio as gr
+import time
 import spaces
 
-#----------------------------
-import time
-import gradio as gr
+# === 1️⃣ MODEL VE TOKENIZER YÜKLEME ===
+MODEL_NAME = "mistralai/Mistral-7B-v0.1"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
 
-#--------------------
+torch_dtype = torch.float32
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch_dtype)
+
+# === 2️⃣ LoRA AYARLARI ===
+lora_config = LoraConfig(
+    r=8,              
+    lora_alpha=32,    
+    lora_dropout=0.1, 
+    bias="none",
+    target_modules=["q_proj", "v_proj"],
+)
+model = get_peft_model(model, lora_config)
 
+# === 3️⃣ VERİ SETİ ===
+dataset = load_dataset("oscar", "unshuffled_deduplicated_tr", trust_remote_code=True)
+subset = dataset["train"].shuffle(seed=42).select(range(10000))
 
 @spaces.GPU
-def ana():
-    # === 1️⃣ MODEL VE TOKENIZER YÜKLEME ===
-    MODEL_NAME = "mistralai/Mistral-7B-v0.1"  # Hugging Face model adı
-    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-    
-    # === 2️⃣ CPU OPTİMİZASYONU ===
-    torch_dtype = torch.float32  # CPU için uygun dtype
-    model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch_dtype)
-    
-    # === 3️⃣ LoRA AYARLARI ===
-    lora_config = LoraConfig(
-        r=8,              
-        lora_alpha=32,    
-        lora_dropout=0.1, 
-        bias="none",
-        target_modules=["q_proj", "v_proj"], 
-    )
-    model = get_peft_model(model, lora_config)
-    
-    # === 4️⃣ VERİ SETİ ===
-    dataset = load_dataset("oscar", "unshuffled_deduplicated_tr", trust_remote_code=True)  # trust_remote_code=True
-    subset = dataset["train"].shuffle(seed=42).select(range(10000))  # Küçük subset seçiyoruz (10.000 örnek)
-    
-    # === 5️⃣ TOKENLEŞTİRME FONKSİYONU ===
-    @spaces.GPU
-    def tokenize_function(examples):
-        return tokenizer(examples["text"], truncation=True, max_length=512)
-    
-    tokenized_datasets = subset.map(tokenize_function, batched=True)
-    
-    # === 6️⃣ EĞİTİM AYARLARI ===
-    # Eğitimde kaç adım olduğunu hesaplayalım
-    train_size = len(tokenized_datasets)  # 10,000 örnek
-    batch_size = 1  # Batch size 1
-    num_epochs = 1  # 1 epoch eğitimi
-    max_steps = (train_size // batch_size) * num_epochs  # max_steps hesapla
-    
-    training_args = TrainingArguments(
-        output_dir="./mistral_lora",
-        per_device_train_batch_size=1,  
-        gradient_accumulation_steps=16, 
-        learning_rate=5e-4,  
-        num_train_epochs=1,  
-        max_steps=max_steps,  # Buraya max_steps parametresini ekliyoruz
-        save_steps=500,
-        save_total_limit=2,
-        logging_dir="./logs",
-        logging_steps=10,
-        optim="adamw_torch",
-        train_dataset=split_dataset["train"],
-        eval_dataset=split_dataset["test"],
-        no_cuda=True,  # GPU kullanılmıyor
-    )
-    train_model()
+def tokenize_function(examples):
+    return tokenizer(examples["text"], truncation=True, max_length=512)
+
+tokenized_datasets = subset.map(tokenize_function, batched=True)
+
+# === 4️⃣ EĞİTİM AYARLARI ===
+batch_size = 1  
+num_epochs = 1  
+max_steps = (len(tokenized_datasets) // batch_size) * num_epochs
+
+training_args = TrainingArguments(
+    output_dir="./mistral_lora",
+    per_device_train_batch_size=1,  
+    gradient_accumulation_steps=16, 
+    learning_rate=5e-4,  
+    num_train_epochs=1,  
+    max_steps=max_steps,  
+    save_steps=500,
+    save_total_limit=2,
+    logging_dir="./logs",
+    logging_steps=10,
+    optim="adamw_torch",
+    no_cuda=True,  
+)
 
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_datasets,
+)
 
-# === 7️⃣ MODEL EĞİTİMİ ===
 @spaces.GPU
 def train_model():
-    trainer = Trainer(
-        model=model,
-        args=training_args,
-        train_dataset=tokenized_datasets,
-    )
     trainer.train()
 
-  # Eğitimi başlat
-demo = gr.ChatInterface(
-    slow_echo,
-    type="messages",
-    flagging_mode="manual",
-    flagging_options=["Like", "Spam", "Inappropriate", "Other"],
-    save_history=True,
-)
-
+# === 5️⃣ CHAT ARAYÜZÜ ===
 @spaces.GPU
 def slow_echo(message, history):
-    for i in range(len(message)):
-        ana()
-        time.sleep(0.05)
-        
-        yield "You typed: " + message[: i + 1]
+    response = "Model henüz eğitilmedi. Lütfen eğitimi başlatın."
+    if model:
+        response = f"You typed: {message}"
+    return response
 
 demo = gr.ChatInterface(
     slow_echo,
-    type="messages",
+    type="text",
     flagging_mode="manual",
     flagging_options=["Like", "Spam", "Inappropriate", "Other"],
     save_history=True,
 )
 
 if __name__ == "__main__":
-    demo.launch(share=True)
-
+    train_model()
+    demo.launch(share=True)