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Update app.py
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app.py
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# - https://huggingface.co/EnglishVoice/t5-base-keywords-to-headline?text=diabetic+diet+plan
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# - Apache 2.0
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# In[2]:
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import torch
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from transformers import T5ForConditionalGeneration,T5Tokenizer
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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tokenizer = T5Tokenizer.from_pretrained("EnglishVoice/t5-base-keywords-to-headline", clean_up_tokenization_spaces=True, legacy=False)
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model = model.to(device)
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def title_gen(keywords):
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text = "headline: " + keywords
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return titles
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# In[1]:
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import gradio as gr
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# In[ ]:
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iface = gr.Interface(fn=paraphrase,
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inputs=[gr.Textbox(label="Paste 2 or more keywords searated by a comma.", lines=1), "checkbox", gr.Slider(0.1, 2, 0.8)],
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iface.launch()
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'''
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#Create a four button panel for changing parameters with one click
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def fn(text):
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return ("Hello gradio!")
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with gr.Blocks () as demo:
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with gr.Row(variant='compact') as PanelRow1: #first row: top
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with gr.Column(scale=0, min_width=180) as PanelCol5:
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gr.HTML("")
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with gr.Column(scale=0) as PanelCol4:
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submit = gr.Button("Temp++", scale=0)
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with gr.Column(scale=1) as PanelCol5:
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gr.HTML("")
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with gr.Row(variant='compact') as PanelRow2: #2nd row: left, right, middle
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with gr.Column(min_width=100) as PanelCol1:
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submit = gr.Button("Contrastive")
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with gr.Column(min_width=100) as PanelCol2:
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submit = gr.Button("Re-generate")
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with gr.Column(min_width=100) as PanelCol3:
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submit = gr.Button("Diversity Beam")
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with gr.Column(min_width=100) as PanelCol5:
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gr.HTML("")
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with gr.Column(min_width=100) as PanelCol5:
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gr.HTML("")
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with gr.Column(scale=0) as PanelCol5:
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gr.HTML("")
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with gr.Row(variant='compact') as PanelRow3: #last row: down
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with gr.Column(scale=0, min_width=180) as PanelCol7:
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gr.HTML("")
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with gr.Column(scale=1) as PanelCol6:
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submit = gr.Button("Temp--", scale=0)
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with gr.Column(scale=0) as PanelCol5:
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gr.HTML("")
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demo.launch()
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'''
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# In[164]:
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import gc
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gc.collect()
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# In[166]:
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gr.close_all()
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# In[ ]:
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# - https://huggingface.co/EnglishVoice/t5-base-keywords-to-headline?text=diabetic+diet+plan
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# - Apache 2.0
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import torch
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from transformers import T5ForConditionalGeneration,T5Tokenizer
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import gradio as gr
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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tokenizer = T5Tokenizer.from_pretrained("EnglishVoice/t5-base-keywords-to-headline", clean_up_tokenization_spaces=True, legacy=False)
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model = model.to(device)
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def title_gen(keywords):
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text = "headline: " + keywords
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return titles
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iface = gr.Interface(fn=paraphrase,
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inputs=[gr.Textbox(label="Paste 2 or more keywords searated by a comma.", lines=1), "checkbox", gr.Slider(0.1, 2, 0.8)],
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iface.launch()
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