Update app.py

app.py

@@ -7,29 +7,29 @@ model = T5ForConditionalGeneration.from_pretrained(best_model_path)
 tokenizer = T5Tokenizer.from_pretrained("swcrazyfan/Kingify-2Way-T5-Large-v1_1")
 
 def tokenize_data(text, dekingify):
-    # Tokenize the review body
-    if dekingify == "Dekingify":
-        input_ = "dekingify: " + str(text) + ' </s>'
-    else:
-        input_ = "kingify: " + str(text) + ' </s>'
-    max_len = 512
-    # tokenize inputs
-    tokenized_inputs = tokenizer(input_, padding='max_length', truncation=True, max_length=max_len, return_attention_mask=True, return_tensors='pt')
+	# Tokenize the review body
+	if dekingify == "Dekingify":
+		input_ = "dekingify: " + str(text) + ' </s>'
+	else:
+		input_ = "kingify: " + str(text) + ' </s>'
+	max_len = 512
+	# tokenize inputs
+	tokenized_inputs = tokenizer(input_, padding='max_length', truncation=True, max_length=max_len, return_attention_mask=True, return_tensors='pt')
 
-    inputs={"input_ids": tokenized_inputs['input_ids'],
-        "attention_mask": tokenized_inputs['attention_mask']}
-    return inputs
+	inputs={"input_ids": tokenized_inputs['input_ids'],
+		"attention_mask": tokenized_inputs['attention_mask']}
+	return inputs
 
 def generate_answers(text, max_length, num_beams, dekingify):
-    inputs = tokenize_data(text, dekingify)
-    results= model.generate(input_ids= inputs['input_ids'], attention_mask=inputs['attention_mask'], do_sample=True,
-                           num_beams=num_beams,
-                            max_length=max_length,
-                            min_length=1,
-                            early_stopping=True,
-                            num_return_sequences=1)
-    answer = tokenizer.decode(results[0], skip_special_tokens=True)
-    return answer
+	inputs = tokenize_data(text, dekingify)
+	results= model.generate(input_ids= inputs['input_ids'], attention_mask=inputs['attention_mask'], do_sample=True,
+						   num_beams=num_beams,
+							max_length=max_length,
+							min_length=1,
+							early_stopping=True,
+							num_return_sequences=1)
+	answer = tokenizer.decode(results[0], skip_special_tokens=True)
+	return answer
 
-iface = gr.Interface(title="DeKingify", description="Write anything below. Then, click submit to 'DeKingify' it.", fn=generate_answers, inputs=[gr.inputs.Textbox(label="Original Text",lines=10), gr.inputs.Slider(label="Maximum Length", minimum=1, maximum=512, default=512, step=1), gr.inputs.Slider(label="Number of Beams", minimum=1, maximum=50, default=5, step=1), gr.inputs.Radio(label=“What do you want to do?, choices=["Kingify", "Dekingify"])], outputs=["text"])
+iface = gr.Interface(title="DeKingify", description="Write anything below. Then, click submit to 'DeKingify' it.", fn=generate_answers, inputs=[gr.inputs.Textbox(label="Original Text",lines=10), gr.inputs.Slider(label="Maximum Length", minimum=1, maximum=512, default=512, step=1), gr.inputs.Slider(label="Number of Beams", minimum=1, maximum=50, default=5, step=1), gr.inputs.Radio(label=“What do you want to do?", choices=["Kingify", "Dekingify"])], outputs=["text"])
 iface.launch(inline=False)