Delete GradioApp.py

GradioApp.py

@@ -1,36 +0,0 @@
-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)