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import gradio as gr
import os
import torch
# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
from transformers import RobertaTokenizer, RobertaForSequenceClassification
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
# Load pre-trained RoBERTa model and tokenizer
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = RobertaForSequenceClassification.from_pretrained('roberta-base')
# Define a function to analyze text for potential adult content
def analyze_adult_content(text):
# Tokenize input text
inputs = tokenizer(text, return_tensors='pt')
# Perform inference
outputs = model(**inputs)
# Get predicted label (0: Not Adult Content, 1: Adult Content)
predicted_label_idx = torch.argmax(outputs.logits).item()
predicted_label = model.config.id2label[predicted_label_idx]
return predicted_label
# Define a function to analyze the sentiment of the text using VADER
def analyze_sentiment(text):
analyzer = SentimentIntensityAnalyzer()
sentiment_scores = analyzer.polarity_scores(text)
# Determine sentiment label based on compound score
if sentiment_scores['compound'] >= 0.05:
sentiment_label = 'Positive'
elif sentiment_scores['compound'] <= -0.05:
sentiment_label = 'Negative'
else:
sentiment_label = 'Neutral'
return sentiment_label, sentiment_scores
# Example text
text = "I really enjoy watching this movie, it's so entertaining!"
# Analyze adult content
adult_content_label = analyze_adult_content(text)
print("Adult Content Label:", adult_content_label)
def text_analysis(text):
# Analyze sentiment
sentiment_label, sentiment_scores = analyze_sentiment(text)
print("Sentiment Label:", sentiment_label)
print("Sentiment Scores:", sentiment_scores)
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(sentiment_label, sentiment_scores, "Gamma", "Theta")
return html
demo = gr.Interface(
text_analysis,
gr.Textbox(placeholder="Enter sentence here..."),
["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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