Update app.py

app.py

@@ -1,90 +1,95 @@
-from functools import lru_cache
-import os
-from flask import Flask, request, jsonify, render_template
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-import logging
-import time
-
-app = Flask(__name__)
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# Önbellek dizini ayarı
-os.environ['TRANSFORMERS_CACHE'] = '/app/cache'
-os.makedirs('/app/cache', exist_ok=True)
-
-# Model konfigürasyonu
-MODEL_NAME = "redrussianarmy/gpt2-turkish-cased"
-
-@lru_cache(maxsize=1)
-def load_model():
-    try:
-        logger.info("Model yükleniyor...")
-        tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-        model = AutoModelForCausalLM.from_pretrained(
-            MODEL_NAME,
-            torch_dtype=torch.float16
-        )
-        model = model.to('cpu')
-        torch.set_num_threads(1)
-        logger.info("Model başarıyla yüklendi")
-        return model, tokenizer
-    except Exception as e:
-        logger.error(f"Model yükleme hatası: {str(e)}")
-        raise RuntimeError(f"Model yüklenemedi: {str(e)}")
-
-@app.route('/')
-def home():
-    return render_template('index.html')  # DÜZELTİLDİ: 'templates/index.html' değil
-
-@app.route('/health')
-def health_check():
-    try:
-        load_model()
-        return jsonify({"status": "healthy"}), 200
-    except Exception as e:
-        return jsonify({"status": "unhealthy", "error": str(e)}), 500
-
-@app.route('/generate', methods=['POST'])
-def generate():
-    try:
-        start_time = time.time()
-        data = request.get_json()
-        prompt = data.get('prompt', '')[:300]  # 300 karakter sınır
-        
-        if not prompt:
-            return jsonify({"error": "Prompt gereklidir", "success": False}), 400
-
-        model, tokenizer = load_model()
-        inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True).to('cpu')
-        
-        with torch.no_grad():
-            outputs = model.generate(
-                inputs.input_ids,
-                attention_mask=inputs.attention_mask,
-                max_length=100,
-                do_sample=True,
-                top_k=40,
-                temperature=0.7,
-                pad_token_id=tokenizer.eos_token_id
-            )
-        
-        result = tokenizer.decode(outputs[0], skip_special_tokens=True)
-        processing_time = round(time.time() - start_time, 2)
-        
-        return jsonify({
-            "result": result,
-            "success": True,
-            "processing_time": processing_time
-        })
-        
-    except Exception as e:
-        logger.error(f"Hata: {str(e)}")
-        return jsonify({
-            "error": str(e),
-            "success": False
-        }), 500
-
-if __name__ == '__main__':
+from functools import lru_cache
+import os
+from flask import Flask, request, jsonify, render_template
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+import logging
+import time
+
+app = Flask(__name__)
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Önbellek dizini ayarı
+os.environ['TRANSFORMERS_CACHE'] = '/app/cache'
+os.makedirs('/app/cache', exist_ok=True)
+
+# Model konfigürasyonu
+MODEL_NAME = "redrussianarmy/gpt2-turkish-cased"
+
+@lru_cache(maxsize=1)
+def load_model():
+    try:
+        logger.info("Model yükleniyor...")
+        tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+        
+        # Pad token kontrolü ve ayarlama
+        if tokenizer.pad_token is None:
+            tokenizer.pad_token = tokenizer.eos_token
+        
+        model = AutoModelForCausalLM.from_pretrained(
+            MODEL_NAME,
+            torch_dtype=torch.float16
+        )
+        model = model.to('cpu')
+        torch.set_num_threads(1)
+        logger.info("Model başarıyla yüklendi")
+        return model, tokenizer
+    except Exception as e:
+        logger.error(f"Model yükleme hatası: {str(e)}")
+        raise RuntimeError(f"Model yüklenemedi: {str(e)}")
+
+@app.route('/')
+def home():
+    return render_template('index.html')
+
+@app.route('/health')
+def health_check():
+    try:
+        load_model()
+        return jsonify({"status": "healthy"}), 200
+    except Exception as e:
+        return jsonify({"status": "unhealthy", "error": str(e)}), 500
+
+@app.route('/generate', methods=['POST'])
+def generate():
+    try:
+        start_time = time.time()
+        data = request.get_json()
+        prompt = data.get('prompt', '')[:300]  # 300 karakter sınır
+        
+        if not prompt:
+            return jsonify({"error": "Prompt gereklidir", "success": False}), 400
+
+        model, tokenizer = load_model()
+        inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True).to('cpu')
+        
+        with torch.no_grad():
+            outputs = model.generate(
+                inputs.input_ids,
+                attention_mask=inputs.attention_mask,
+                max_length=100,
+                do_sample=True,
+                top_k=40,
+                temperature=0.7,
+                pad_token_id=tokenizer.pad_token_id  # Düzeltildi: eos_token yerine pad_token
+            )
+        
+        result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        processing_time = round(time.time() - start_time, 2)
+        
+        return jsonify({
+            "result": result,
+            "success": True,
+            "processing_time": processing_time
+        })
+        
+    except Exception as e:
+        logger.error(f"Hata: {str(e)}")
+        return jsonify({
+            "error": str(e),
+            "success": False
+        }), 500
+
+if __name__ == '__main__':
     app.run(host='0.0.0.0', port=7860, threaded=False)