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from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
import torch
import gradio as gr
tokenizer_sentence_analysis = AutoTokenizer.from_pretrained("finiteautomata/bertweet-base-sentiment-analysis")
model_sentence_analysis = AutoModelForSequenceClassification.from_pretrained("finiteautomata/bertweet-base-sentiment-analysis")
tokenizer_review_feedback_sentiment = AutoTokenizer.from_pretrained('nlptown/bert-base-multilingual-uncased-sentiment')
model_review_feedback_sentiment = AutoModelForSequenceClassification.from_pretrained('nlptown/bert-base-multilingual-uncased-sentiment')
classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
def sentence_sentiment_model(text, tokenizer, model):
inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")
with torch.no_grad():
result = model(inputs['input_ids'], attention_mask=inputs['attention_mask'])
logits = result.logits.detach()
probs = torch.softmax(logits, dim=1)
pos_prob = probs[0][2].item()
neu_prob = probs[0][1].item()
neg_prob = probs[0][0].item()
return {'Positive': [round(float(pos_prob), 2)],"Neutural":[round(float(neu_prob), 2)], 'Negative': [round(float(neg_prob), 2)]}
def review_feedback_sentiment(text, tokenizer, model):
inputs = tokenizer.encode_plus(text, padding='max_length', max_length=512, return_tensors="pt")
with torch.no_grad():
result = model(inputs['input_ids'], attention_mask=inputs['attention_mask'])
logits = result.logits.detach()
probs = torch.softmax(logits, dim=1).detach().numpy()[0]
categories = ['Terrible', 'Poor', 'Average', 'Good', 'Excellent']
output_dict = {}
for i in range(len(categories)):
output_dict[categories[i]] = [round(float(probs[i]), 2)]
return output_dict
def emotion_sentiment(text):
results = classifier(text, padding='max_length', max_length=512)
return {label['label']: [label['score']] for label in results[0]}
def sentence_analysis(text):
result = sentence_sentiment_model(text,tokenizer_sentence_analysis,model_sentence_analysis)
return result
def emotion(text):
result = emotion_sentiment(text)
return result
def review_feed_back(text):
result = review_feedback_sentiment(text,tokenizer_review_feedback_sentiment,model_review_feedback_sentiment)
return result
def selection_model(model,text):
if text == "":
return "No Text Input"
if model=="Emotion Analysis":
return emotion(text)
if model == "Review Feedback Analysis":
return review_feed_back(text)
if model == "Sentence Analysis":
return sentence_analysis(text)
return "Please select model"
paragraph = """
I woke up this morning feeling refreshed and excited for the day ahead.
"""
with gr.Blocks(title="Sentiment",css="footer {visibility: hidden}") as demo:
with gr.Row():
with gr.Column():
gr.Markdown("## Emotion, ReviewFeedback, Sentence Analysis")
with gr.Row():
with gr.Column():
drop_down_name = gr.Dropdown(choices=["Emotion Analysis", "Review Feedback Analysis", "Sentence Analysis"],label="Model")
inputs = gr.TextArea(label="sentence",value=paragraph,interactive=True)
btn = gr.Button(value="RUN")
with gr.Column():
output = gr.Label(label="output")
btn.click(fn=selection_model,inputs=[drop_down_name,inputs],outputs=[output])
demo.launch()
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