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import torch | |
import streamlit as st | |
from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
import plotly.graph_objects as go | |
input_text = st.text_input( | |
label='Estimate item desirability:', | |
value='I love a good fight.', | |
placeholder='Enter item' | |
) | |
#model_path = '/nlp/nlp/models/finetuned/twitter-xlm-roberta-base-regressive-desirability-ft-4' | |
model_path = 'magnolia-psychometrics/item-desirability' | |
tokenizer = AutoTokenizer.from_pretrained( | |
pretrained_model_name_or_path=model_path, | |
use_fast=True | |
) | |
model = AutoModelForSequenceClassification.from_pretrained( | |
pretrained_model_name_or_path=model_path, | |
num_labels=1, | |
ignore_mismatched_sizes=True, | |
) | |
def z_score(y, mean=.04853076, sd=.9409466): | |
return (y - mean) / sd | |
if input_text: | |
inputs = tokenizer(input_text, padding=True, return_tensors='pt') | |
with torch.no_grad(): | |
score = model(**inputs).logits.squeeze().tolist() | |
z = z_score(score) | |
fig = go.Figure(go.Indicator( | |
mode = "gauge+delta", | |
value = z, | |
domain = {'x': [0, 1], 'y': [0, 1]}, | |
title = f"Item Desirability <br><sup>\"{input_text}\"</sup>", | |
delta = { | |
'reference': 0, | |
'decreasing': {'color': "#ec4899"}, | |
'increasing': {'color': "#36def1"} | |
}, | |
gauge = { | |
'axis': {'range': [-4, 4], 'tickwidth': 1, 'tickcolor': "black"}, | |
'bar': {'color': "#4361ee"}, | |
'bgcolor': "white", | |
'borderwidth': 2, | |
'bordercolor': "#efefef", | |
'steps': [ | |
{'range': [-4, 0], 'color': '#efefef'}, | |
{'range': [0, 4], 'color': '#efefef'}], | |
'threshold': { | |
'line': {'color': "#4361ee", 'width': 8}, | |
'thickness': 0.75, | |
'value': z} | |
})) | |
fig.update_layout( | |
paper_bgcolor = "white", | |
font = {'color': "black", 'family': "Arial"}) | |
st.plotly_chart(fig, theme=None, use_container_width=True) | |