Update app.py

app.py

@@ -10,11 +10,9 @@ tokenizer = RobertaTokenizer.from_pretrained(model_dir)
 model = RobertaForSequenceClassification.from_pretrained(model_dir)
 #pipe = pipeline("text-classification", model="thugCodeNinja/robertatemp")
 pipe = pipeline("text-classification",model=model,tokenizer=tokenizer)
-def process_text(input_text, input_file):
+def process_text(input_text):
     if input_text:
         text = input_text
-    elif input_file is not None:
-        text = input_file
     inputs = tokenizer(text, return_tensors="pt")
     with torch.no_grad():
         logits = model(**inputs).logits
@@ -59,9 +57,8 @@ def process_text(input_text, input_file):
     return processed_result, prob, final_label, shap_plot_html,similar_articles
 
 text_input = gr.Textbox(label="Enter text")
-file_input = gr.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.Dataframe(label="Similar Articles", headers=["Title", "Link"],row_count=5)]
 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,title=title,description=description, inputs=[text_input, file_input], outputs=outputs).launch()
+gr.Interface(fn=process_text,title=title,description=description, inputs=[text_input], outputs=outputs).launch()