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()