Update app.py

app.py

@@ -2,21 +2,30 @@ import gradio as gr
 import torch
 from transformers import BertTokenizerFast, BertForSequenceClassification
 
-def predict_news_category(text, model, tokenizer, device):
-    inputs = tokenizer(text, truncation=True, padding=True, max_length=512, return_tensors='pt').to(device)
-    model.to(device)
+def predict_news_category(text, model, tokenizer):
+    # Tokenize input text
+    inputs = tokenizer(text, truncation=True, padding=True, max_length=512, return_tensors='pt')
+    
+    # Predict
     outputs = model(**inputs)
     probs = outputs[0].softmax(1)
+    
+    # Get the predicted category
     _, predicted_category = torch.max(probs, dim=1)
+    
     return predicted_category.item()
 
+# Load your model
 model = BertForSequenceClassification.from_pretrained('akhil2808/EPICS-PROJECT')
-model.eval()
+model.eval()  # Set the model to evaluation mode
+
+# Load tokenizer
 tokenizer = BertTokenizerFast.from_pretrained('akhil2808/EPICS-PROJECT')
 
+# Function to predict news category
 def detect_news_category(text):
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    category = predict_news_category(text, model, tokenizer, device)
+    category = predict_news_category(text, model, tokenizer)
+    # Map the prediction to fake or real news
     prediction_dict = {1: 'Real News', 0: 'Fake News'}
     return prediction_dict[category]
 
@@ -28,3 +37,4 @@ iface = gr.Interface(fn=detect_news_category,
                      theme='huggingface')
 
 iface.launch()
+