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import gradio as gr
import os
import torch
import spacy
from spacy import displacy
nlp = spacy.load("en_core_web_sm")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
def get_hatespeech_score(text):
tokenizer = AutoTokenizer.from_pretrained("unhcr/hatespeech-detection")
model = AutoModelForSequenceClassification.from_pretrained("unhcr/hatespeech-detection")
# Tokenize input text
inputs = tokenizer(text, return_tensors='pt')
# Perform inference
outputs = model(**inputs)
# Get predicted label
predicted_label_idx = torch.argmax(outputs.logits).item()
predicted_label = model.config.id2label[predicted_label_idx]
return predicted_label
def text_analysis(text):
label_1 = get_hatespeech_score(text)
html = '''<!doctype html>
<html>
<body>
<h1>Text Sentiment Analysis</h1>
<div style=background-color:#d9eee1>
<h2>Overall Sentiment</h2>
<p>{}</p>
</div>
<div style=background-color:#fff4a3>
<h2>Adult Content</h2>
<p>{}</p>
</div>
<div style=background-color:#ffc0c7>
<h2>Hate Speech</h2>
<p>{}</p>
</div>
<div style=background-color:#cfb0b1>
<h2>Text Summary</h2>
<p>{}</p>
</div>
</body>
</html>
'''.format("Alpha", label_1, "Gamma", "Theta")
doc = nlp(text)
pos_tokens = []
for token in doc:
pos_tokens.extend([(token.text, token.pos_), (" ", None)])
return pos_tokens, html
demo = gr.Interface(
text_analysis,
gr.Textbox(placeholder="Enter sentence here..."),
["highlight", "html"],
examples=[
["What a beautiful morning for a walk!"],
["It was the best of times, it was the worst of times."],
],
)
demo.launch()
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