Update app.py

app.py

@@ -1,15 +1,15 @@
 import gradio as gr
-from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
 import torch
 import re
 
-# Model ve tokenizer'ı yükleyin
-model_name = "your_model_directory"  # Model dosyalarının bulunduğu dizini buraya yazın
-tokenizer = DistilBertTokenizer.from_pretrained(model_name)  # Doğru tokenizer sınıfını kullanın
-model = DistilBertForSequenceClassification.from_pretrained(model_name)  # Doğru model sınıfını kullanın
+# Load model and tokenizer
+model_name = "alperugurcan/nlp-disaster"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
 def clean_text(text):
-    # URL'leri ve özel karakterleri kaldır
+    # Remove URLs and special characters
     text = re.sub(r'http\S+|[^\w\s]', '', text)
     return text.strip()
 
@@ -18,10 +18,10 @@ def predict(text):
         return "Please enter some text"
     
     try:
-        # Metni ön işleme tabi tut
+        # Preprocess text
         text = clean_text(text)
         
-        # Giriş metnini tokenize et
+        # Tokenize the input text
         inputs = tokenizer(
             text, 
             return_tensors="pt", 
@@ -30,21 +30,21 @@ def predict(text):
             max_length=128
         )
         
-        # Model tahminlerini al
+        # Get model predictions
         with torch.no_grad():
             outputs = model(**inputs)
             probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
             prediction = torch.argmax(outputs.logits, dim=-1)
             confidence = probabilities[0][prediction.item()].item()
             
-        # Sonucu ve güven oranını döndür
+        # Return result with confidence
         result = "Disaster" if prediction.item() == 1 else "Not Disaster"
         return f"{result} (Confidence: {confidence:.2%})"
     
     except Exception as e:
         return f"Error in prediction: {str(e)}"
 
-# Gradio arayüzü oluştur
+# Create a Gradio interface with improved styling
 iface = gr.Interface(
     fn=predict,
     inputs=gr.Textbox(
@@ -67,5 +67,5 @@ iface = gr.Interface(
     )
 )
 
-# Uygulamayı başlat
-iface.launch(share=True)
+# Launch with share=True for public URL
+iface.launch(share=True)