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test / app.py

Update app.py

4c59735 verified 3 months ago

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	import streamlit as st
	from transformers import pipeline
	from transformers import AutoTokenizer, AutoModelForSequenceClassification

	#tokenizer = AutoTokenizer.from_pretrained("mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis")
	#model = AutoModelForSequenceClassification.from_pretrained("Howosn/Sentiment_Model")

	# Load the summarization & translation model pipeline
	tran_sum_pipe = pipeline("translation", model='utrobinmv/t5_summary_en_ru_zh_base_2048',return_all_scores=True)
	sentiment_pipeline = pipeline("text-classification", model='Howosn/Sentiment_Model',return_all_scores=True)
	tokenizer = T5Tokenizer.from_pretrained('utrobinmv/t5_summary_en_ru_zh_base_2048')

	# Streamlit application title
	st.title("Emotion analysis")
	st.write("Turn Your Input Into Sentiment Score")

	# Text input for the user to enter the text to analyze
	text = st.text_area("Enter the text", "")

	# Perform analysis result when the user clicks the "Analyse" button
	if st.button("Analyse"):
	# Perform text classification on the input text
	trans_sum = tran_sum_pipe(text)[0]
	results = sentiment_pipeline(trans_sum)[0]

	# Display the classification result
	max_score = float('-inf')
	max_label = ''

	for result in results:
	if result['score'] > max_score:
	max_score = result['score']
	max_label = result['label']

	st.write("Text:", text)
	st.write("Label:", max_label)
	st.write("Score:", max_score)