import torch
from transformers import XLNetTokenizer, XLNetForSequenceClassification
import gradio as gr


from google.colab import drive

# Mount Google Drive
drive.mount('/content/drive')
# 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()