import torch from transformers import AutoModelForSequenceClassification, AutoTokenizer import gradio as gr # Path to the saved model in Hugging Face Spaces model_name = 'AliArshad/SeverityPrediction' # Replace with your actual username and model name model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) # Function for prediction def xl_net_predict(text): inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=100) with torch.no_grad(): outputs = model(**inputs) logits = outputs.logits probabilities = torch.softmax(logits, dim=1) predicted_class = torch.argmax(probabilities).item() return "Severe" if predicted_class == 1 else "Non-severe" # Customizing the interface iface = gr.Interface( fn=xl_net_predict, inputs=gr.Textbox(lines=2, label="Summary", placeholder="Enter text here..."), outputs=gr.Textbox(label="Predicted Severity"), title="XLNet Based Bug Report Severity Prediction", description="Enter text and predict its severity (Severe or Non-severe).", theme="huggingface", examples=[ ["Can't open multiple bookmarks at once from the bookmarks sidebar using the context menu"], ["Minor enhancements to make-source-package.sh"] ], allow_flagging=False ) iface.launch()