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import gradio as gr |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoConfig |
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import numpy as np |
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from scipy.special import softmax |
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def preprocess(text): |
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new_text = [] |
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for t in text.split(" "): |
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t = '@user' if t.startswith('@') and len(t) > 1 else t |
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t = 'http' if t.startswith('http') else t |
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new_text.append(t) |
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return " ".join(new_text) |
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MODEL = "cardiffnlp/twitter-roberta-base-sentiment-latest" |
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tokenizer = AutoTokenizer.from_pretrained(MODEL) |
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config = AutoConfig.from_pretrained(MODEL) |
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model = AutoModelForSequenceClassification.from_pretrained(MODEL, output_attentions=False, output_hidden_states=False) |
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def predict_sentiment(text): |
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text = preprocess(text) |
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encoded_input = tokenizer(text, return_tensors='pt') |
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output = model(**encoded_input) |
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scores = output.logits[0].detach().numpy() |
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scores = softmax(scores) |
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ranking = np.argsort(scores)[::-1] |
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results = [] |
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for i in range(scores.shape[0]): |
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label = config.id2label[ranking[i]] |
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score = np.round(float(scores[ranking[i]]), 4) |
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results.append(f"{label}: {score}") |
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return "\n".join(results) |
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examples = [ |
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["I feel happy!"], |
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["Had a lovely day at the park π³"], |
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["Feeling down after today's news π"], |
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["Just landed a new job, super excited!!"] |
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] |
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footer_text = """ |
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<b>About the Model</b><br> |
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This sentiment analysis model is based on the roberta-base architecture and has been fine-tuned for sentiment analysis on tweets. For more information, check out the model's repository on Hugging Face: |
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<a href="https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest" target="_blank">cardiffnlp/twitter-roberta-base-sentiment-latest</a>. |
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""" |
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iface = gr.Interface(fn=predict_sentiment, |
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inputs=gr.components.Textbox(lines=2, placeholder="Enter Text Here..."), |
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outputs="text", |
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title="Sentiment Analysis", |
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description="This model predicts the sentiment of a given text. Enter text to see its sentiment.", |
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examples=examples, |
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article=footer_text) |
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if __name__ == "__main__": |
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iface.launch() |
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