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import streamlit as st
# Library for Entailment
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load model
tokenizer = AutoTokenizer.from_pretrained("roberta-large-mnli")
text_classification_model = AutoModelForSequenceClassification.from_pretrained("roberta-large-mnli")
### Streamlit interface ###
st.title("Text Classification")
st.subheader("Entailment, neutral or contradiction?")
with st.form("submission_form", clear_on_submit=False):
threshold = st.slider("Threshold", min_value=0.0, max_value=1.0, step=0.1, value=0.7)
sentence_1 = st.text_input("Sentence 1 input")
sentence_2 = st.text_input("Sentence 2 input")
submit_button_compare = st.form_submit_button("Compare Sentences")
# If submit_button_compare clicked
if submit_button_compare:
print("Comparing sentences...")
### Text classification - entailment, neutral or contradiction ###
raw_inputs = [f"{sentence_1}</s></s>{sentence_2}"]
inputs = tokenizer(raw_inputs, padding=True, truncation=True, return_tensors="pt")
# print(inputs)
outputs = text_classification_model(**inputs)
outputs = torch.nn.functional.softmax(outputs.logits, dim = -1)
# print(outputs)
# argmax_index = torch.argmax(outputs).item()
print(text_classification_model.config.id2label[0], ":", round(outputs[0][0].item()*100,2),"%")
print(text_classification_model.config.id2label[1], ":", round(outputs[0][1].item()*100,2),"%")
print(text_classification_model.config.id2label[2], ":", round(outputs[0][2].item()*100,2),"%")
st.subheader("Text classification for both sentences:")
st.write(text_classification_model.config.id2label[1], ":", round(outputs[0][1].item()*100,2),"%")
st.write(text_classification_model.config.id2label[0], ":", round(outputs[0][0].item()*100,2),"%")
st.write(text_classification_model.config.id2label[2], ":", round(outputs[0][2].item()*100,2),"%")
entailment_score = round(outputs[0][2].item(),2)
if entailment_score >= threshold:
st.subheader("The statements are very similar!")
else:
st.subheader("The statements are not close enough")
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