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import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig
import numpy as np
from scipy.special import softmax
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)
MODEL = "cardiffnlp/twitter-roberta-base-sentiment-latest"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
config = AutoConfig.from_pretrained(MODEL)
model = AutoModelForSequenceClassification.from_pretrained(MODEL, output_attentions=False, output_hidden_states=False)
def predict_sentiment(text):
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output.logits[0].detach().numpy()
scores = softmax(scores)
ranking = np.argsort(scores)[::-1]
results = []
for i in range(scores.shape[0]):
label = config.id2label[ranking[i]]
score = np.round(float(scores[ranking[i]]), 4)
results.append(f"{label}: {score}")
return "\n".join(results)
examples = [
["I feel happy!"],
["Had a lovely day at the park 🌳"],
["Feeling down after today's news 😞"],
["Just landed a new job, super excited!!"]
]
footer_text = """
<b>About the Model</b><br>
This sentiment analysis model is based on the roberta-base architecture and has been fine-tuned for sentiment analysis on tweets. For more information, check out the model's repository on Hugging Face:
<a href="https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest" target="_blank">cardiffnlp/twitter-roberta-base-sentiment-latest</a>.
"""
iface = gr.Interface(fn=predict_sentiment,
inputs=gr.components.Textbox(lines=2, placeholder="Enter Text Here..."),
outputs="text",
title="Sentiment Analysis",
description="This model predicts the sentiment of a given text. Enter text to see its sentiment.",
examples=examples,
article=footer_text)
if __name__ == "__main__":
iface.launch()
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