Update app.py

app.py

@@ -33,20 +33,20 @@ elif input_method == "Metin Yaz veya Yapıştır":
 @st.cache_resource
 def load_pipeline(model_name, task_type):
     if task_type == "Metin Sınıflandırma":
-        model = AutoModelForSequenceClassification.from_pretrained(model_name)
-        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
+        tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
         return pipeline('text-classification', model=model, tokenizer=tokenizer)
     elif task_type == "Metin Analizi":
-        model = AutoModelForTokenClassification.from_pretrained(model_name)
-        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForTokenClassification.from_pretrained("dbmdz/bert-base-turkish-cased")
+        tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-turkish-cased")
         return pipeline('ner', model=model, tokenizer=tokenizer)
     elif task_type == "Duygu Analizi":
-        model = AutoModelForSequenceClassification.from_pretrained(model_name)
-        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
+        tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
         return pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
     elif task_type == "Metin Oluşturma":
-        model = AutoModelForCausalLM.from_pretrained(model_name)
-        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForCausalLM.from_pretrained("gpt2")
+        tokenizer = AutoTokenizer.from_pretrained("gpt2")
         return pipeline('text-generation', model=model, tokenizer=tokenizer)
 
 # Görev ve modele göre pipeline yükleme