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| import gradio as gr | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| # Load the models and tokenizers | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| tokenizer1 = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-imdb") | |
| tokenizer2 = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment") | |
| model1 = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-imdb") | |
| model2 = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment") | |
| # Define the sentiment prediction functions | |
| def predict_sentiment(text): | |
| # Predict sentiment using model 1 | |
| inputs1 = tokenizer1.encode_plus(text, padding="longest", truncation=True, return_tensors="pt") | |
| outputs1 = model1(**inputs1) | |
| predicted_label1 = outputs1.logits.argmax().item() | |
| sentiment1 = "Positive" if predicted_label1 == 1 else "Negative" if predicted_label1 == 0 else "Neutral" | |
| # Predict sentiment using model 2 | |
| inputs2 = tokenizer2.encode_plus(text, padding="longest", truncation=True, return_tensors="pt") | |
| outputs2 = model2(**inputs2) | |
| predicted_label2 = outputs2.logits.argmax().item() | |
| sentiment2 = "Positive" if predicted_label2 == 1 else "Negative" if predicted_label2 == 0 else "Neutral" | |
| return sentiment1, sentiment2 | |
| # Create the Gradio interface | |
| iface = gr.Interface( | |
| fn=predict_sentiment, | |
| inputs="text", | |
| outputs=["text", "text"], | |
| title="Sentiment Analysis (Model 1 vs Model 2)", | |
| description="Compare sentiment predictions from two models.", | |
| ) | |
| # Launch the interface | |
| iface.launch() | |