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 = '''
Text Sentiment Analysis
'''.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()