Init

GradioApp.py

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+import torch
+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')
+torch.quantization.quantize_dynamic(model, {torch.nn.Linear}, dtype=torch.qint8, inplace=True)
+
+def paraphrase(model, text, max_length=128, num_return_sequences=5, num_beams=25, temperature=0.7):
+    input_ids = tokenizer(
+        f'paraphrase: {text}',
+        return_tensors="pt", padding="longest",
+        max_length=max_length,
+        truncation=True,
+    ).input_ids
+    
+    outputs = model.generate(
+        input_ids, temperature=temperature, repetition_penalty=1.5,
+        num_return_sequences=num_return_sequences, no_repeat_ngram_size=5, num_beams=num_beams, max_length=max_length
+    )
+
+    res = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+    return res
+
+def fn(text, results_num=5, beams_num=25, temperature=0.7):
+    return '\n'.join(paraphrase(model, text, num_return_sequences=results_num, num_beams=beams_num, temperature=temperature))
+
+demo = gr.Interface(
+    fn=fn,
+    inputs=[gr.Textbox(lines=3, placeholder='Enter Text To Paraphrase'), gr.Slider(minimum=1, maximum=10, step=1, value=5), gr.Slider(minimum=1, maximum=50, step=1, value=25), gr.Slider(minimum=0.0, maximum=1.0, step=0.01, value=0.7)],
+    outputs=['text'],
+)
+
+demo.launch(share=True)