Upload 2 files

app.py

@@ -1,25 +1,32 @@
 import gradio as gr
-from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqGeneration
+from transformers import pipeline, AutoTokenizer, T5ForConditionalGeneration
 
 # Model ve tokenizer'ı yükle
 model_name = "ozcangundes/mt5-small-turkish-summarization"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSeq2SeqGeneration.from_pretrained(model_name)
-summarizer = pipeline("summarization", model=model, tokenizer=tokenizer)
 
 def summarize_text(text):
     if len(text.split()) < 100:
         return "Metin çok kısa. En az 100 kelime olmalıdır."
     
-    # Metni özetle
-    summary = summarizer(text, 
-                        max_length=150, 
-                        min_length=50, 
-                        length_penalty=2.0,
-                        num_beams=4,
-                        early_stopping=True)
-    
-    return summary[0]['summary_text']
+    try:
+        # Pipeline oluştur
+        summarizer = pipeline(
+            "summarization",
+            model=model_name,
+            tokenizer=model_name
+        )
+        
+        # Metni özetle
+        summary = summarizer(text, 
+                           max_length=150, 
+                           min_length=50, 
+                           length_penalty=2.0,
+                           num_beams=4,
+                           early_stopping=True)
+        
+        return summary[0]['summary_text']
+    except Exception as e:
+        return f"Hata oluştu: {str(e)}"
 
 # Gradio arayüzünü oluştur
 iface = gr.Interface(

requirements.txt

@@ -1,4 +1,5 @@
 gradio>=3.50.2
 transformers>=4.30.0
 torch>=2.0.0
-sentencepiece>=0.1.99
+sentencepiece>=0.1.99
+protobuf>=3.20.0