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from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer, AutoConfig
import numpy as np
from scipy.special import softmax
import gradio as gr
# Preprocess text (username and link placeholders)
def preprocess(text):
new_text = []
for t in text.split(" "):
t = '@user' if t.startswith('@') and len(t) > 1 else t
t = 'http' if t.startswith('http') else t
new_text.append(t)
return " ".join(new_text)
# load model
MODEL = f"cardiffnlp/twitter-roberta-base-sentiment-latest"
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
#model.save_pretrained(MODEL)
tokenizer = AutoTokenizer.from_pretrained(MODEL)
config = AutoConfig.from_pretrained(MODEL)
# create classifier function
def classify_sentiments(text):
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = softmax(scores)
# Print labels and scores
probs = {}
ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(len(scores)):
l = config.id2label[ranking[i]]
s = scores[ranking[i]]
probs[l] = np.round(float(s), 4)
return probs
#build the Gradio app
#Instructuction = "Write an imaginary review about a product or service you might be interested in."
title="Text Sentiment Analysis"
description = """Write a Good or Bad review about an imaginary product or service,\
see how the machine learning model is able to predict your sentiments"""
article = """
- Click submit button to test sentiment analysis prediction
- Click clear button to refresh text
"""
gr.Interface(classify_sentiments,
'text',
'label',
title = title,
description = description,
#Instruction = Instructuction,
article = article,
allow_flagging = "never",
live = False,
examples=["This has to be the best Introductory course in machine learning",
"I consider this training an absolute waste of time."]
).launch()
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