Add application file
Browse files
app.py
ADDED
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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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