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| import streamlit as st | |
| import transformers as t | |
| import plotly.express as px | |
| import pandas as pd | |
| st.title("Phrase Emotion Analysis") | |
| with st.spinner(text="Loading model..."): | |
| classifier = t.pipeline("zero-shot-classification", | |
| model="facebook/bart-large-mnli", | |
| multi_class=True) | |
| sentiment_task = t.pipeline("sentiment-analysis", | |
| model="cardiffnlp/twitter-xlm-roberta-base-sentiment", | |
| tokenizer="cardiffnlp/twitter-xlm-roberta-base-sentiment") | |
| x = st.text_input("Enter your title here:") | |
| candidate_labels = ['anger', 'sadness', 'fear', 'joy', 'interest', | |
| 'surprise', 'disgust', 'shame', 'compassion', 'other'] | |
| if x != "": | |
| with st.spinner(text="Evaluating your input..."): | |
| output = classifier(x, candidate_labels) | |
| sentiment = sentiment_task(x) | |
| st.write(str(sentiment)) | |
| ordered_results = [] | |
| for lbl in candidate_labels: | |
| ind = output['labels'].index(lbl) | |
| ordered_results.append(output['scores'][ind]) | |
| df = pd.DataFrame(dict(r=ordered_results, theta=candidate_labels)) | |
| fig = px.line_polar(df, r='r', theta='theta', line_close=True) | |
| fig.update_traces(fill='toself') | |
| st.plotly_chart(fig) | |