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| import time | |
| import nltk | |
| from nltk.sentiment import SentimentIntensityAnalyzer | |
| import gradio as gr | |
| from transformers import AutoTokenizer | |
| from transformers import AutoModelForSequenceClassification | |
| from scipy.special import softmax | |
| import nltk | |
| import matplotlib.pyplot as plt | |
| nltk.download('vader_lexicon') | |
| sia = nltk.sentiment.SentimentIntensityAnalyzer() | |
| def sentiment_analyzer(text, model_name): | |
| positive_score = 0.0 | |
| negative_score = 0.0 | |
| if model_name == "Vader": | |
| sentiment_score = sia.polarity_scores(text) | |
| positive_score = sentiment_score['pos'] | |
| negative_score = sentiment_score['neg'] | |
| elif model_name == "RoBERTa": | |
| MODEL = f"cardiffnlp/twitter-roberta-base-sentiment" | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL) | |
| model = AutoModelForSequenceClassification.from_pretrained(MODEL) | |
| encoded_text = tokenizer(text, return_tensors='pt') | |
| output = model(**encoded_text) | |
| scores = output[0][0].detach().numpy() | |
| scores = softmax(scores) | |
| scores_dict = { | |
| 'roberta_neg' : scores[0], | |
| 'roberta_neu' : scores[1], | |
| 'roberta_pos' : scores[2] | |
| } | |
| positive_score = float(scores[2]) | |
| negative_score = float(scores[0]) | |
| # print(f"Output positive score: {positive_score}") | |
| # print(f"Output negative score: {negative_score}") | |
| return positive_score, negative_score | |
| sentiment_analyzer("I am a goog human being","RoBERTa") | |
| input_text = gr.inputs.Textbox(lines=15, label="Input Text") | |
| sentiment_model = gr.inputs.Radio(["Vader", "RoBERTa"], label="Choose Sentiment Analyzer") | |
| outputs = [gr.Slider(minimum=0, maximum=1, step=0.01, label="Positive Sentiment"), | |
| gr.Slider(minimum=0, maximum=1, step=0.01, label="Negative Sentiment")] | |
| iface = gr.Interface(fn=sentiment_analyzer, inputs=[input_text, sentiment_model], outputs=outputs) | |
| print(time.time()) | |
| iface.launch(inline=False) |