Update app.py

app.py

@@ -1,5 +1,5 @@
 import streamlit as st
-from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer, AutoModelForTokenClassification, AutoModelForCausalLM
+from transformers import pipeline, AutoTokenizer, AutoModelForTokenClassification, AutoModelForSequenceClassification, AutoModelForCausalLM
 import pandas as pd
 
 # Uygulama sayfa ayarları
@@ -52,10 +52,10 @@ def load_pipeline(model_name, task_type):
 # Görev ve modele göre pipeline yükleme
 model_dict = {
     "Metin Sınıflandırma": "nlptown/bert-base-multilingual-uncased-sentiment",
-    "Metin Analizi": "dbmdz/bert-base-turkish-cased",  # Türkçe model
+    "Metin Analizi": "dbmdz/bert-base-turkish-cased",
     "Duygu Analizi": "cardiffnlp/twitter-roberta-base-sentiment",
     "Metin Oluşturma": "gpt2",
-    "Varlık Tanıma": "dbmdz/bert-base-turkish-cased"  # Türkçe NER model
+    "Varlık Tanıma": "dbmdz/bert-base-turkish-cased"
 }
 
 pipeline_model = load_pipeline(model_dict[task], task)
@@ -102,8 +102,23 @@ if st.button("Çalıştır") and input_text:
         output = pipeline_model(input_text)
         
         # Process entities
-        processed_entities = process_entities(output, input_text)
-        df = pd.DataFrame(processed_entities)
+        processed_entities = []
+        for entity in output:
+            word = entity['word']
+            label = entity['entity']
+            score = entity['score']
+            start = entity['start']
+            end = entity['end']
+            processed_entities.append({
+                'word': word,
+                'label': label,
+                'score': score,
+                'start': start,
+                'end': end
+            })
+        
+        # Aggregate entities
+        df = pd.DataFrame(process_entities(processed_entities, input_text))
         st.subheader("Tanımlanan Varlıklar")
         st.dataframe(df)
         
@@ -129,7 +144,7 @@ if st.button("Çalıştır") and input_text:
                 formatted_text += original_text[last_end:]
             return formatted_text
 
-        formatted_text = format_text(processed_entities, input_text)
+        formatted_text = format_text(process_entities(processed_entities, input_text), input_text)
         st.subheader("Analiz Edilen Metin")
         st.markdown(f"<p>{formatted_text}</p>", unsafe_allow_html=True)
     elif task == "Metin Oluşturma":