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| from transformers import AutoModelForSequenceClassification | |
| from transformers import TFAutoModelForSequenceClassification | |
| from transformers import AutoTokenizer, AutoConfig | |
| import numpy as np | |
| from scipy.special import softmax | |
| import gradio as gr | |
| # Requirements | |
| model_path = f"FKBaffour/fine-tuned-roberta-base-model-for-sentiment-analysis" | |
| tokenizer = AutoTokenizer.from_pretrained('roberta-base') | |
| config = AutoConfig.from_pretrained(model_path) | |
| model = AutoModelForSequenceClassification.from_pretrained(model_path) | |
| # Preprocess text (username and link placeholders) | |
| def preprocess(text): | |
| new_text = [] | |
| for t in text.split(" "): | |
| t = '@user' if t.startswith('@') and len(t) > 1 else t | |
| t = 'http' if t.startswith('http') else t | |
| new_text.append(t) | |
| return " ".join(new_text) | |
| def sentiment_analysis(text): | |
| text = preprocess(text) | |
| # PyTorch-based models | |
| encoded_input = tokenizer(text, return_tensors='pt') | |
| output = model(**encoded_input) | |
| scores_ = output[0][0].detach().numpy() | |
| scores_ = softmax(scores_) | |
| # Format output dict of scores | |
| labels = ['Negative', 'Neutral', 'Positive'] | |
| scores = {l:float(s) for (l,s) in zip(labels, scores_) } | |
| return scores | |
| demo = gr.Interface( | |
| fn=sentiment_analysis, | |
| inputs=gr.Textbox(placeholder="Copy and paste/Write a tweet here..."), | |
| outputs="label", | |
| interpretation="default", | |
| examples=["What's up with the vaccine"], | |
| title="Sentiment Analysis on Vaccinations", | |
| description="This Application assesses if a social media post relating to vaccination is positive, neutral, or negative.", ) | |
| demo.launch() | |