MarieAngeA13
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8d97474
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Parent(s):
06bd1d2
Update app.py
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app.py
CHANGED
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import streamlit as st
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from transformers import pipeline
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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pip install googletrans==4.0.0rc1
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from googletrans import Translator
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#
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#
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st.title('Sentiment Analysis with BERT')
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st.write('Enter some text and we will predict its sentiment!')
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#
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translator = Translator()
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text_input = st.text_input('Enter text here')
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detected_language = translator.detect(text_input).lang
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if detected_language == 'fr':
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translation = translator.translate(text_input, src='fr', dest='en')
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translated_text = translation.text
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else:
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translated_text = text_input
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# When the user submits text, run the sentiment analysis model on it
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if st.button('Submit'):
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#
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output = classifier(translated_text)
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best_prediction = output[0]
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sentiment = best_prediction['label']
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confidence = best_prediction['score']
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st.write(f'Sentiment: {sentiment}')
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st.write(f'Confidence: {round(confidence, 2)}')
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import streamlit as st
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from transformers import pipeline
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from transformers import AutoModelForSequenceClassification, AutoTokenizer
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from googletrans import Translator
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# Installer le package googletrans
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!pip install googletrans==4.0.0rc1
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# Charger le modèle de classification des sentiments BERT
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classifier = pipeline("text-classification", model="MarieAngeA13/Sentiment-Analysis-BERT")
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# Créer une application Streamlit
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st.title('Sentiment Analysis with BERT')
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st.write('Enter some text and we will predict its sentiment!')
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# Ajouter un champ de saisie de texte pour l'utilisateur
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translator = Translator()
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text_input = st.text_input('Enter text here')
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# Détecter la langue du texte saisi
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detected_language = translator.detect(text_input).lang
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# Traduire le texte s'il est en français
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if detected_language == 'fr':
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translation = translator.translate(text_input, src='fr', dest='en')
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translated_text = translation.text
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else:
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translated_text = text_input
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st.write(translated_text)
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# Lorsque l'utilisateur clique sur "Submit"
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if st.button('Submit'):
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# Prédire le sentiment du texte en utilisant notre modèle BERT
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output = classifier(translated_text)
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best_prediction = output[0]
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sentiment = best_prediction['label']
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confidence = best_prediction['score']
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# Afficher la prédiction de sentiment à l'utilisateur
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st.write(f'Sentiment: {sentiment}')
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st.write(f'Confidence: {round(confidence, 2)}')
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