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eefc793
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Parent(s):
1d3aabd
Update app.py
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app.py
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import gradio as gr
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import os
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import torch
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# Load model directly
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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import torch
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from transformers import RobertaTokenizer, RobertaForSequenceClassification
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from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
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#
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# Define a function to analyze text for potential adult content
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def analyze_adult_content(text):
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# Tokenize input text
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inputs = tokenizer(text, return_tensors='pt')
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else:
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sentiment_label = 'Neutral'
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return sentiment_label
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# Example text
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text = "I really enjoy watching this movie, it's so entertaining!"
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# Analyze adult content
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adult_content_label = analyze_adult_content(text)
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print("Adult Content Label:", adult_content_label)
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def text_analysis(text):
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# Analyze sentiment
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sentiment_label
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html = '''<!doctype html>
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<html>
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<h2>Adult Content</h2>
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<p>{}</p>
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</div>
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<div style=background-color:#ffc0c7>
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<h2>Hate Speech</h2>
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<p>{}</p>
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</div>
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<div style=background-color:#cfb0b1>
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<h2>Text Summary</h2>
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<p>{}</p>
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</div>
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</body>
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</html>
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'''.format(sentiment_label,
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return html
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demo = gr.Interface(
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demo.launch()
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import gradio as gr
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import os
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import torch
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import torch
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from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
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# Load model directly
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModelForSeq2SeqLM, RobertaTokenizer, RobertaForSequenceClassification
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# Define a function for text summarization using GPT
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def summarize_text(text, max_length=1024):
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tokenizer = AutoTokenizer.from_pretrained("kabita-choudhary/finetuned-bart-for-conversation-summary")
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model = AutoModelForSeq2SeqLM.from_pretrained("kabita-choudhary/finetuned-bart-for-conversation-summary")
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# Tokenize input text
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input_ids = tokenizer.encode(text, return_tensors='pt', max_length=1024, truncation=True)
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# Generate summary
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summary_ids = model.generate(input_ids, max_length=max_length, num_return_sequences=1, early_stopping=True)
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# Decode and return summary
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summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
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return summary
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# Define a function to analyze text for potential adult content
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def analyze_adult_content(text):
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# Load pre-trained RoBERTa model and tokenizer
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tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
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model = RobertaForSequenceClassification.from_pretrained('roberta-base')
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# Tokenize input text
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inputs = tokenizer(text, return_tensors='pt')
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else:
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sentiment_label = 'Neutral'
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return sentiment_label
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def text_analysis(text):
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# Analyze sentiment
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sentiment_label = analyze_adult_content(text)
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sentiment = analyze_sentiment(text)
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summary = summarize_text(text)
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html = '''<!doctype html>
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<html>
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<h2>Adult Content</h2>
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<p>{}</p>
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</div>
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<div style=background-color:#cfb0b1>
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<h2>Text Summary</h2>
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<p>{}</p>
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</div>
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</body>
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</html>
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'''.format(sentiment_label, sentiment, summary)
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return html
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demo = gr.Interface(
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],
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)
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demo.launch()
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