Update app.py

app.py

@@ -3,8 +3,8 @@ import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
 
-tokenizer  = AutoTokenizer.from_pretrained('humarin/chatgpt_paraphraser_on_T5_base', cache_dir='./Models')
-model = AutoModelForSeq2SeqLM.from_pretrained('humarin/chatgpt_paraphraser_on_T5_base', cache_dir='./Models')
+tokenizer  = AutoTokenizer.from_pretrained('humarin/chatgpt_paraphraser_on_T5_base')
+model = AutoModelForSeq2SeqLM.from_pretrained('humarin/chatgpt_paraphraser_on_T5_base')
 
 def paraphrase(
     text,
@@ -33,7 +33,11 @@ def paraphrase(
 
     res = tokenizer.batch_decode(outputs, skip_special_tokens=True)
 
-    return res
+    result = ''
+    for i, item in enumerate(res):
+        result += f'{i+1}. {item}\n'
+
+    return result
 
 def fn(
     text,
@@ -52,14 +56,14 @@ demo = gr.Interface(
     fn=fn,
     inputs=[
         gr.Textbox(lines=3, placeholder='Enter Text To Paraphrase'),
-        gr.Slider(minimum=1, maximum=25, step=1, value=5),
-        gr.Slider(minimum=1, maximum=25, step=1, value=5),
-        gr.Slider(minimum=1, maximum=20, step=1, value=5),
-        gr.Slider(minimum=0.6, maximum=20.1, step=0.5, value=10.1),
-        gr.Slider(minimum=0.6, maximum=20.1, step=0.5, value=3.1),
-        gr.Slider(minimum=1, maximum=10, step=1, value=2),
-        gr.Slider(minimum=0.0, maximum=1000, step=0.1, value=0.7),
-        gr.Slider(minimum=32, maximum=512, step=1, value=128),
+        gr.Slider(minimum=1, maximum=10, step=1, value=5, label='Num Beams', info='This parameter controls the number of possible next tokens that are considered at each step in the beam search algorithm. A higher value will result in more diverse paraphrases, but may also take longer to generate.'),
+        gr.Slider(minimum=1, maximum=10, step=1, value=5, label='Num Beam Groups', info='This parameter controls the number of beams that are run in parallel. A higher value will result in faster generation, but may also result in less diversity.'),
+        gr.Slider(minimum=1, maximum=10, step=1, value=5, label='Num Return Sequences', info='This parameter controls the number of sequences that are generated at each step in the beam search algorithm. A higher value will produce more results, but may also take longer to generate.'),
+        gr.Slider(minimum=0.6, maximum=20.1, step=0.5, value=10.1, label='Repetition Penalty', info='This parameter controls how much the model penalizes itself for generating repeated words or phrases. A higher value will result in more unique paraphrases, but may also result in less accurate paraphrases.'),
+        gr.Slider(minimum=0.6, maximum=20.1, step=0.5, value=3.1, label='Diversity Penalty', info='This parameter controls how much the model penalizes itself for generating paraphrases that are similar to each other. A higher value will result in more diverse paraphrases, but may also result in less accurate paraphrases.'),
+        gr.Slider(minimum=1, maximum=10, step=1, value=2, label='No Repeat Ngram Size', info='This parameter controls the size of the n-grams that the model is not allowed to repeat. A higher value will result in more unique paraphrases, but may also result in less accurate paraphrases.'),
+        gr.Slider(minimum=0.0, maximum=1, step=0.1, value=0.7, label='Temperature', info='This parameter controls how much the model is allowed to deviate from the original text. A higher value will result in more creative paraphrases, but may also result in less accurate paraphrases.'),
+        gr.Slider(minimum=32, maximum=512, step=1, value=128, label='Max Length', info='This parameter controls the maximum length of the generated paraphrase. A higher value will result in more detailed paraphrases, but may also take longer to generate.'),
     ],
     outputs=['text'],
 )