Update app.py

app.py

@@ -9,7 +9,6 @@ tokenizer = RobertaTokenizer.from_pretrained(model_dir)
 model = RobertaModel.from_pretrained(model_dir)
 
 
-
 def process_text(input_text, input_file):
     if input_text:
         text = input_text
@@ -56,15 +55,11 @@ def process_text(input_text, input_file):
 
     return processed_result, prob, final_label, text_plot,similar_articles
 
-    text_input = gr.inputs.Textbox(label="Enter text")
-    file_input = gr.inputs.File(label="Upload a text file")
-    outputs = [gr.Textbox(label="Processed text"), gr.Textbox(label="Probability"), gr.Textbox(label="Label"), gr.HTML(label="SHAP Plot"),gr.Table(label="Similar Articles", columns=["Title", "Link"])]
+text_input = gr.inputs.Textbox(label="Enter text")
+file_input = gr.inputs.File(label="Upload a text file")
+outputs = [gr.Textbox(label="Processed text"), gr.Textbox(label="Probability"), gr.Textbox(label="Label"), gr.HTML(label="SHAP Plot"),gr.Table(label="Similar Articles", columns=["Title", "Link"])]
 title = "Group 2- ChatGPT text detection module"
 description = '''Please upload text files and text input responsibly and await the explainable results. The approach in place includes finetuning a Roberta model for text classification.Once the classifications are done the decision is exaplined thorugh the SHAP text plot.
 The probability is particularly explained by the attention plots through SHAP'''
-    gr.Interface(fn=process_text, inputs=[text_input, file_input], outputs=outputs).launch()
-
-    return ''
+gr.Interface(fn=process_text,title=title,descriptioon=description, inputs=[text_input, file_input], outputs=outputs).launch()
 
-if __name__ == '__main__':
-    app.run(debug=True)