Update app.py

app.py

@@ -1,22 +1,35 @@
-import gradio as gr
 import torch
-from transformers import T5Tokenizer, T5ForConditionalGeneration
+from transformers import (T5ForConditionalGeneration,T5Tokenizer)
+import gradio as gr
+
+best_model_path = "swcrazyfan/Dekingify-T5-Large"
+model = T5ForConditionalGeneration.from_pretrained(best_model_path)
+tokenizer = T5Tokenizer.from_pretrained("swcrazyfan/Dekingify-T5-Large")
 
-model = T5ForConditionalGeneration.from_pretrained('swcrazyfan/KingJamesify-T5-large')
-tokenizer = T5Tokenizer.from_pretrained('swcrazyfan/KingJamesify-T5-large')
+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')
 
-def king_jamesify(input_text, num_beams, max_length, temperature):
-    prompt_text = "kingify: " + input_text
-    input_ids = tokenizer.encode(prompt_text, return_tensors='pt').to(torch.int64)
-    generated_ids = model.generate(input_ids=input_ids, max_length=max_length, num_beams=num_beams, num_beam_groups=num_beams, num_return_sequences=1, temperature=temperature)
-    result = tokenizer.decode(generated_ids, skip_special_tokens=True)
-    return result
+    inputs={"input_ids": tokenized_inputs['input_ids'],
+        "attention_mask": tokenized_inputs['attention_mask']}
+    return inputs
 
-iface = gr.Interface(king_jamesify,
-                     [gr.inputs.Textbox(lines=20, label="Enter text to be King Jamesified"),
-                      gr.inputs.Slider(minimum=0.0, maximum=1, default=0.7, label="Temperature"),
-                      gr.inputs.Slider(minimum=1, maximum=512, default=512, label="Max Length"),
-                      gr.inputs.Slider(minimum=1, maximum=10, default=1, label="Number of Beams")],
-                     gr.outputs.Textbox(label="King Jamesified Text"))
+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
 
-iface.launch()
+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="", choices=["Kingify", "Dekingify"])], outputs=["text"])
+iface.launch(inline=False)