Update app.py

app.py

@@ -10,7 +10,7 @@ import torch
 labels = ["Explicit", "Not_Explicit"]
 
 #Defining the models and tokenuzer
-model_name = 'valurank/finetuned-distilbert-explicit_content_detection'
+model_name = "valurank/finetuned-distilbert-explicit_content_detection"
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
@@ -35,8 +35,8 @@ def clean_text(url):
         " +", " ", text
     ).strip()  # get rid of multiple spaces and replace with a single
 
-  text = re.sub(r'Date\s\d{1,2}\/\d{1,2}\/\d{4}', '', text) #remove date
-  text = re.sub(r'\d{1,2}:\d{2}\s[A-Z]+\s[A-Z]+', '', text) #remove time
+  text = re.sub(r"Date\s\d{1,2}\/\d{1,2}\/\d{4}", "", text) #remove date
+  text = re.sub(r"\d{1,2}:\d{2}\s[A-Z]+\s[A-Z]+", "", text) #remove time
     
   return text
  
@@ -44,7 +44,7 @@ def clean_text(url):
 def get_category(file):
   text = clean_text(file)
 
-  input_tensor = tokenizer.encode(text, return_tensors='pt', truncation=True)
+  input_tensor = tokenizer.encode(text, return_tensors="pt", truncation=True)
   logits = model(input_tensor).logits
 
   softmax = torch.nn.Softmax(dim=1)
@@ -56,10 +56,10 @@ def get_category(file):
   return emotion
   
 #Creating the interface for the radio app
-demo = gr.Interface(get_category, inputs=gr.inputs.Textbox(label='Drop your articles here'),
-                    outputs = 'text',
-                    title='Explicit Content Detection')
+demo = gr.Interface(get_category, inputs=gr.inputs.Textbox(label="Drop your articles here"),
+                    outputs = "text",
+                    title="Explicit Content Detection")
                                       
 #Launching the gradio app
-if __name__ == '__main__':
+if __name__ == "__main__":
   demo.launch(debug=True)