Upload app.py

app.py

@@ -0,0 +1,49 @@
+import streamlit as st
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+print("selam")
+
+st.title("Sentiment Analysis with BERT")
+
+user_input = st.text_area("Enter your text here:")
+
+
+
+if st.button("Analyze"):
+    import streamlit as st
+    from transformers import BertTokenizer, BertForSequenceClassification
+    import torch
+
+    tokenizer = BertTokenizer.from_pretrained("ulascelenk/siena")
+    model = BertForSequenceClassification.from_pretrained("ulascelenk/siena")
+
+    inputs = tokenizer.encode_plus(
+    user_input,
+    return_tensors='pt',        # PyTorch tensörü olarak döndür
+    truncation=True,            # Metni maksimum uzunluğa göre kes
+    max_length=128,             # Maksimum token uzunluğunu belirleyin
+    padding='max_length'        # Token uzunluğunu maksimuma göre doldur
+    )
+
+    with torch.no_grad():
+        input_ids = inputs['input_ids']
+        attention_mask = inputs['attention_mask']
+        outputs = model(input_ids, attention_mask=attention_mask)
+        logits = outputs.logits
+
+    # Tahmin edilen sınıfı alın
+    predicted_class = torch.argmax(logits, dim=1).item()
+
+    sentiment_dict = {
+    0: "Mild Negative",
+    1: "Mild Positive",
+    2: "Neutral",
+    3: "Strong Negative",
+    4: "Strong Positive"
+    }
+
+    # Tahmini kullanıcıya gösterin
+    st.write(f"Predicted class: {sentiment_dict[predicted_class]}")
+
+
+