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import torch | |
from transformers import pipeline | |
from transformers import AutoModelForSeq2SeqLM | |
from transformers import AutoTokenizer | |
# Load trained model | |
model = AutoModelForSeq2SeqLM.from_pretrained("/output/reframer") | |
tokenizer = AutoTokenizer.from_pretrained("/output/reframer") | |
reframer = pipeline('summarization', model=model, tokenizer=tokenizer) | |
def reframe(text, strategy): | |
text_with_strategy = text + "Strategy: ['" + strategy + "']" | |
#Input Control | |
#The input text cannot be too short to ensure it has substantial content to be reframed. It also cannot be too long to ensure the text has a focused idea. | |
if len(text) < 15 or len(text) > 150: | |
return "Please try again by inputing text with moderate length." | |
if TextBlob(text).sentiment.polarity > 0.2: | |
return "Please try again by inputing text that is negative." | |
from hatesonar import Sonar | |
sonar = Sonar() | |
if sonar.ping(text)['classes'][1]['confidence'] > 0.8: | |
return "Please try again by not inputing offensive language." | |
#if TextBlob(text).sentiment.polarity > 0: | |
# return "Please try again by inputing text that is negative." | |
return reframer(text_with_strategy)[0]['summary_text'] | |
import gradio as gr | |
with gr.Blocks() as demo: | |
text = gr.Textbox(label="Original Text") | |
radio = gr.Radio( | |
["thankfulness", "neutralizing", "optimism", "growth", "impermanence", "self_affirmation"], label="Strategy to use?" | |
) | |
output = gr.Textbox(label="Reframed Output") | |
radio.change(fn=reframe, inputs=[text, radio], outputs=output) | |
demo.launch() |