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from difflib import Differ | |
import gradio as gr | |
from transformers import pipeline | |
pipe = pipeline("summarization", "dominguesm/positive-reframing-en") | |
def predict(text, operation): | |
try: | |
res = pipe(f"[{operation}]: {text}", max_length=124) | |
except Exception as e: | |
return e | |
d = Differ() | |
return ( | |
res[0]["summary_text"], | |
[ | |
(token[2:], token[0] if token[0] != " " else None) | |
for token in d.compare(text, res[0]["summary_text"]) | |
], | |
) | |
# return res[0]["summary_text"] | |
iface = gr.Interface( | |
title="Positive Reframing EN", | |
description="This model is a T5 adjusted to the sentiment transfer task, where the objective is to reverse the sentiment polarity of a text without contradicting the original meaning. Positive reframing induces a complementary positive viewpoint (e.g. glass-half-full) escaping negative patterns. More info [here](https://huggingface.co/dominguesm/positive-reframing-en).", | |
fn=predict, | |
inputs=[ | |
gr.Textbox( | |
lines=1, | |
placeholder=( | |
f"Pensar no meu futuro me faz querer viver numa ilha sozinha para sempre" | |
), | |
), | |
gr.Radio( | |
[ | |
"growth", | |
"impermanence", | |
"neutralizing", | |
"optimism", | |
"self_affirmation", | |
"thankfulness", | |
] | |
), | |
], | |
outputs=[ | |
gr.Textbox(label="Generated Text"), | |
gr.HighlightedText( | |
label="Diff", | |
combine_adjacent=True, | |
).style(color_map={"+": "green", "-": "red"}), | |
], | |
examples=[ | |
[ | |
"You know I really don't care about the power struggle between the papacy and secular authority in the medieval ages. stupid", | |
"growth", | |
], | |
[ | |
"thinking about my future makes me want to go live on a island alone forever. annoyed", | |
"optimism", | |
], | |
[ | |
"Who would have ever guessed that it would be so freaking hard to get three different grades from two different schools together.", | |
"thankfulness", | |
], | |
], | |
) | |
iface.launch() | |