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import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification
# Load pre-trained tokenizer and model
tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')
model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=2)
def predict_sentiment(input_text):
# Tokenization
inputs = tokenizer(input_text, return_tensors='pt')
# Prediction
outputs = model(**inputs)
probabilities = outputs[0][0].detach().numpy()
labels = ['Negative', 'Positive']
predicted_label = labels[probabilities.argmax()]
return {"Text": input_text, "Sentiment": predicted_label}
iface = gr.Interface(predict_sentiment, input_type="text", output_types=["text"],
input_label="Enter Text", output_label="Predicted Sentiment")
iface.launch() |