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| import requests | |
| import torch | |
| from transformers import XLNetTokenizer, XLNetForSequenceClassification | |
| import gradio as gr | |
| # URL of the saved model on GitHub | |
| model_url = 'https://github.com/AliArshadswl/severity_prediction/raw/main/XLNet_model_project_Core.pt' | |
| # Function to download the model from URL and load it | |
| def download_model(url): | |
| response = requests.get(url) | |
| with open('XLNet_model_project_Core.pt', 'wb') as f: | |
| f.write(response.content) | |
| # Download the model | |
| download_model(model_url) | |
| # Load the saved model | |
| tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased') | |
| model = XLNetForSequenceClassification.from_pretrained('xlnet-base-cased', num_labels=2) | |
| model.load_state_dict(torch.load('XLNet_model_project_Core.pt', map_location=torch.device('cpu'))) | |
| model.eval() | |
| # 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="SevPrecit - A GPT-2 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() | |