Update app.py

app.py

@@ -1,61 +1,64 @@
 import streamlit as st
-from transformers import pipeline, TFAutoModelForSequenceClassification, AutoTokenizer
+from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
 from datasets import load_dataset
 import pandas as pd
 
 st.set_page_config(layout="wide")
 
-# Load dataset
+# Veriyi yükleme ve ön işleme
 @st.cache_resource
 def load_data():
     dataset = load_dataset("WhiteAngelss/Turkce-Duygu-Analizi-Dataset")
     return dataset
 
 dataset = load_data()
-st.title("Sentiment Analysis with Turkish Dataset")
+st.title("Türkçe Sentiment Analizi Uygulaması")
 
-# Show dataset examples
-st.subheader("Sample Data")
+# Örnek veri gösterme
+st.subheader("Örnek Veri")
 sample_df = pd.DataFrame(dataset['train'])
 st.write(sample_df.head())
 
-# Set up model
+# Model seçim kısmı
 model_list = ['WhiteAngelss/entity-word-sentiment-analysis']
-st.sidebar.header("Select Sentiment Analysis Model")
+st.sidebar.header("Sentiment Analizi Modeli Seçimi")
 model_checkpoint = st.sidebar.radio("", model_list)
 
-st.sidebar.write("For details of models: 'https://huggingface.co/WhiteAngelss/entity-word-sentiment-analysis'")
+st.sidebar.write("Model detayları için: 'https://huggingface.co/WhiteAngelss/entity-word-sentiment-analysis'")
 st.sidebar.write("")
 
-st.subheader("Select Text Input Method")
-input_method = st.radio("", ('Select from Examples', 'Write or Paste New Text'))
+# Metin girdi yöntemi seçimi
+st.subheader("Metin Giriş Yöntemi Seçin")
+input_method = st.radio("", ('Örneklerden Seç', 'Yeni Metin Yaz veya Yapıştır'))
 
-if input_method == 'Select from Examples':
-    example_texts = dataset['train']['text'][:5]  # Sample examples from the dataset
-    selected_text = st.selectbox('Select Text from List', example_texts)
-    st.subheader("Text to Analyze")
-    input_text = st.text_area("Selected Text", selected_text, height=128, max_chars=None)
-elif input_method == "Write or Paste New Text":
-    st.subheader("Text to Analyze")
-    input_text = st.text_area('Write or Paste Text Below', value="", height=128, max_chars=None)
+if input_method == 'Örneklerden Seç':
+    example_texts = dataset['train']['text'][:5]  # Veri kümesinden örnek metinler
+    selected_text = st.selectbox('Listeden Metin Seçin', example_texts)
+    st.subheader("Analiz Edilecek Metin")
+    input_text = st.text_area("Seçilen Metin", selected_text, height=128, max_chars=None)
+elif input_method == "Yeni Metin Yaz veya Yapıştır":
+    st.subheader("Analiz Edilecek Metin")
+    input_text = st.text_area('Aşağıya Metin Yazın veya Yapıştırın', value="", height=128, max_chars=None)
 
+# Model ve tokenizer'ı yükleme
 @st.cache_resource
-def setModel(model_checkpoint):
-    model = TFAutoModelForSequenceClassification.from_pretrained(model_checkpoint)
+def set_model(model_checkpoint):
+    model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint)  # TensorFlow yerine PyTorch modelini yükleyin
     tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
     return pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
 
-Run_Button = st.button("Run")
+# Analiz butonu
+run_button = st.button("Analiz Et")
 
-if Run_Button and input_text:
-    sentiment_pipeline = setModel(model_checkpoint)
+if run_button and input_text:
+    sentiment_pipeline = set_model(model_checkpoint)
     output = sentiment_pipeline(input_text)
     
-    st.subheader("Sentiment Analysis Results")
+    st.subheader("Sentiment Analizi Sonuçları")
     df = pd.DataFrame(output)
     st.dataframe(df)
 
-    # Display the sentiment in a more user-friendly format
+    # Sonuçları kullanıcı dostu bir formatta gösterme
     sentiment = output[0]['label']
     score = output[0]['score']
-    st.write(f"Sentiment: {sentiment} (Score: {score:.2f})")  # Corrected line with closed parenthesis
+    st.write(f"Sentiment: {sentiment} (Skor: {score:.2f})")