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| import gradio as gr | |
| import os | |
| import torch | |
| import torch | |
| from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer | |
| # Load model directly | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModelForSeq2SeqLM, RobertaTokenizer, RobertaForSequenceClassification | |
| # Define a function for text summarization using GPT | |
| def summarize_text(text, max_length=1024): | |
| tokenizer = AutoTokenizer.from_pretrained("kabita-choudhary/finetuned-bart-for-conversation-summary") | |
| model = AutoModelForSeq2SeqLM.from_pretrained("kabita-choudhary/finetuned-bart-for-conversation-summary") | |
| # Tokenize input text | |
| input_ids = tokenizer.encode(text, return_tensors='pt', max_length=1024, truncation=True) | |
| # Generate summary | |
| summary_ids = model.generate(input_ids, max_length=max_length, num_return_sequences=1, early_stopping=True) | |
| # Decode and return summary | |
| summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) | |
| return summary | |
| # Define a function to analyze text for potential adult content | |
| def analyze_adult_content(text): | |
| # Load pre-trained RoBERTa model and tokenizer | |
| tokenizer = RobertaTokenizer.from_pretrained('roberta-base') | |
| model = RobertaForSequenceClassification.from_pretrained('roberta-base') | |
| # 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 | |
| def text_analysis(text): | |
| # Analyze sentiment | |
| sentiment_label = analyze_adult_content(text) | |
| sentiment = analyze_sentiment(text) | |
| summary = summarize_text(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:#cfb0b1> | |
| <h2>Text Summary</h2> | |
| <p>{}</p> | |
| </div> | |
| </body> | |
| </html> | |
| '''.format(sentiment_label, sentiment, summary) | |
| 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() |