Update app.py

app.py

@@ -1,46 +1,53 @@
 import gradio as gr
-from transformers import LlamaTokenizer, LlamaForCausalLM
+from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 
-# Genel bir LLaMA tokenizer'ı kullanın
-tokenizer = LlamaTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
-model = LlamaForCausalLM.from_pretrained("bkaplan/MRL1", device_map="auto", torch_dtype=torch.float16)
+# Modeli yükleyin
+model_name = "bkaplan/MRL1"
 
-def respond(message, history, system_message, max_tokens, temperature, top_p):
-    try:
-        # Girdiyi hazırlama
-        input_text = f"System: {system_message}\nUser: {message}\nAssistant:"
-        
-        # Tokenize etme
-        inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
-        
-        # Üretim parametreleri
-        outputs = model.generate(
-            **inputs, 
-            max_length=max_tokens, 
-            temperature=temperature, 
-            top_p=top_p,
-            num_return_sequences=1,
-            do_sample=True
-        )
+try:
+    # Tokenizer ve modeli yükleme
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.float16)
+
+    def respond(message, history, system_message, max_tokens, temperature, top_p):
+        try:
+            # Girdiyi hazırlama
+            input_text = f"System: {system_message}\nUser: {message}\nAssistant:"
+            
+            # Tokenize etme
+            inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+            
+            # Üretim parametreleri
+            outputs = model.generate(
+                **inputs, 
+                max_length=max_tokens, 
+                temperature=temperature, 
+                top_p=top_p,
+                num_return_sequences=1,
+                do_sample=True
+            )
+            
+            # Yanıtı çözme
+            response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+            yield response
         
-        # Yanıtı çözme
-        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-        yield response
-    
-    except Exception as e:
-        yield f"Hata oluştu: {str(e)}"
+        except Exception as e:
+            yield f"Hata oluştu: {str(e)}"
+
+    # Gradio arayüzü
+    demo = gr.ChatInterface(
+        respond, 
+        additional_inputs=[
+            gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+            gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+            gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
+            gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
+        ]
+    )
 
-# Gradio arayüzü
-demo = gr.ChatInterface(
-    respond, 
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
-    ]
-)
+    if __name__ == "__main__":
+        demo.launch(share=True)
 
-if __name__ == "__main__":
-    demo.launch(share=True)
+except Exception as e:
+    print(f"Model yüklenirken hata oluştu: {e}")