Create severitypredictor.py

severitypredictor.py

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+import torch
+from transformers import XLNetTokenizer, XLNetForSequenceClassification
+import gradio as gr
+
+# Path to the saved model
+model_path = '/content/drive/My Drive/XLNet_model_project_Core.pt'  # Update with your model path
+
+# 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(model_path))
+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="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()