Update app.py

app.py

@@ -35,7 +35,7 @@ class BERT_Arch(nn.Module):
 
 # Load the model and set it to evaluation mode
 model = BERT_Arch(bert)
-fake_news_model_path = "Fake_model.pt"
+fake_news_model_path = "Hate_Speech_model.pt"
 fake_news_model = torch.load(fake_news_model_path, map_location=torch.device('cpu'))
 fake_news_model.eval()
 
@@ -45,26 +45,26 @@ def detect_fake_news(text):
     with torch.no_grad():
         outputs = fake_news_model(inputs['input_ids'], inputs['attention_mask'])
         label = torch.argmax(outputs, dim=1).item()
-    fake_news_result = "Fake" if label == 1 else "Not Fake"
+    fake_news_result = "Hate" if label == 1 else "Real"
     return fake_news_result
 
 # Function to handle post logic
 def post_text(text, fake_news_result):
-    if fake_news_result == "Fake":
-        return "Your message contains Fake News and cannot be posted.", ""
+    if fake_news_result == "Hate":
+        return "Your message contains Hate Speech and cannot be posted.", ""
     else:
         return "The text is safe to post.", text
 
 # Gradio Interface
 interface = gr.Blocks()
 with interface:
-    gr.Markdown("## Fake News Detection")
+    gr.Markdown("## Hate Speech Detection")
     with gr.Row():
         text_input = gr.Textbox(label="Enter Text", lines=5)
     with gr.Row():
-        detect_fake_button = gr.Button("Detect Fake News")
+        detect_fake_button = gr.Button("Detect Hate Speech")
     with gr.Row():
-        fake_news_result_box = gr.Textbox(label="Fake News Detection Result", interactive=False)
+        fake_news_result_box = gr.Textbox(label="Hate speech Detection Result", interactive=False)
     with gr.Row():
         post_button = gr.Button("Post Text")
     with gr.Row():