Update app.py

app.py

@@ -1,5 +1,5 @@
 import torch
-from transformers import (T5ForConditionalGeneration,T5Tokenizer)
+from transformers import T5ForConditionalGeneration, T5Tokenizer
 import gradio as gr
 
 best_model_path = "swcrazyfan/Kingify-2Way-T5-Large-v1_1"
@@ -7,32 +7,48 @@ 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')
+    if dekingify == "Dekingify":
+        input_ = "dekingify: " + str(text) + " </s>"
+    else:
+        input_ = "kingify: " + str(text) + " </s>"
+    max_len = 512
+    tokenized_inputs = tokenizer(
+        input_,
+        padding="max_length",
+        truncation=True,
+        max_length=max_len,
+        return_attention_mask=True,
+        return_tensors="pt"
+    )
+    return {"input_ids": tokenized_inputs["input_ids"],
+            "attention_mask": tokenized_inputs["attention_mask"]}
 
-	inputs={"input_ids": tokenized_inputs['input_ids'],
-		"attention_mask": tokenized_inputs['attention_mask']}
-	return inputs
-
-#def generate_answers(text, max_length, num_beams, dekingify):
 def generate_answers(text, dekingify):
-	inputs = tokenize_data(text, dekingify)
-	results= model.generate(input_ids= inputs['input_ids'], attention_mask=inputs['attention_mask'], do_sample=True,
-						   num_beams=5,
-							max_length=512,
-							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=5,
+        max_length=512,
+        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="Kingify 2Way", 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=10, default=5, step=1), gr.inputs.Radio(label="What do you want to do?", choices=["Kingify", "Dekingify"])], outputs=["text"])
+iface = gr.Interface(
+    title="Kingify 2Way",
+    description=("This is a custom AI model that translates modern English into 17th-century English or "
+                 "'King James' English (and vice versa). Write anything below, select 'Kingify' or 'Dekingify', "
+                 "and click submit."),
+    fn=generate_answers,
+    inputs=[
+        gr.Textbox(label="Original Text", lines=10),
+        gr.Radio(label="What do you want to do?", choices=["Kingify", "Dekingify"])
+    ],
+    outputs="text"
+)
 
-iface = gr.Interface(title="Kingify 2Way", description="This is a custom AI model that translates modern English into 17th-century English or 'King James' English (and visa). Write anything below. Then, selevt 'Kingify' or 'DeKingify', and click submit.", fn=generate_answers, inputs=[gr.inputs.Textbox(label="Original Text",lines=10), gr.inputs.Radio(label="What do you want to do?", choices=["Kingify", "Dekingify"])], outputs=["text"])
-iface.launch(inline=False)
+iface.launch()