Update app.py

app.py

@@ -3,48 +3,33 @@ from transformers import AutoModelForSequenceClassification, AutoTokenizer
 import torch
 import re
 
-# 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):
-    # Remove URLs and special characters
-    text = re.sub(r'http\S+|[^\w\s]', '', text)
-    return text.strip()
+    return re.sub(r'http\S+|[^\w\s]', '', text).strip()
 
 def predict(text):
     if not text or len(text.strip()) == 0:
         return "Please enter some text"
     
     try:
-        # Preprocess text
         text = clean_text(text)
+        inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
         
-        # Tokenize the input text
-        inputs = tokenizer(
-            text, 
-            return_tensors="pt", 
-            truncation=True, 
-            padding=True,
-            max_length=128
-        )
-        
-        # 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()
-            
-        # 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)}"
 
-# Create a Gradio interface with improved styling
 iface = gr.Interface(
     fn=predict,
     inputs=gr.Textbox(
@@ -67,5 +52,4 @@ iface = gr.Interface(
     )
 )
 
-# Launch with share=True for public URL
 iface.launch(share=True)