from transformers import pipeline
import gradio as gr

# Load the model using the pipeline
pipe = pipeline("text-classification", model="AliArshad/Severity_Predictor")
from transformers import pipeline
import gradio as gr

# Load the model using the pipeline
pipe = pipeline("text-classification", model="AliArshad/Severity_Predictor")

# Function to predict severity and return confidence score
def predict_severity(text):
    # Get prediction from the pipeline
    prediction = pipe(text)

    # Interpret the label and get the confidence score
    label = prediction[0]['label']
    confidence = prediction[0]['score']
    severity = "Severe" if label == "LABEL_1" else "Non-Severe"

    # Return severity and confidence as separate outputs
    return severity, confidence

# Define the Gradio interface with a title, specific placeholder message, and a progress bar for confidence
iface = gr.Interface(
    fn=predict_severity,
    inputs=gr.Textbox(lines=2, placeholder="Please Enter Bug Report Summary"),
    outputs=[
        gr.Textbox(label="Prediction"),
        gr.Number(label="Confidence", precision=2)
    ],
    title="SevPredict: Exploring the Potential of Large Language Models in Software Maintenance",
    description="Enter text and predict its severity (Severe or Non-severe).",
    examples=[
        ["Can't open multiple bookmarks at once from the bookmarks sidebar using the context menu"],
        ["Minor enhancements to make-source-package.sh"]
    ]
)

# Launch the interface
iface.launch()