app.py

import gradio as gr
from transformers import pipeline

# Load the Hugging Face model for text classification
classifier = pipeline(
    task="text-classification",
    model="CIRCL/cwe-parent-vulnerability-classification-roberta-base",
    return_all_scores=True
)

def predict_cwe(commit_message: str):
    """
    Predict CWE(s) from a commit message using the model.
    """
    results = classifier(commit_message)[0]
    # Sort the results by score descending
    sorted_results = sorted(results, key=lambda x: x["score"], reverse=True)
    # Return top 5 predictions as a dictionary
    return {item["label"]: round(float(item["score"]), 4) for item in sorted_results[:5]}

# Build the Gradio interface
demo = gr.Interface(
    fn=predict_cwe,
    inputs=gr.Textbox(lines=3, placeholder="Enter your commit message here..."),
    outputs=gr.Label(num_top_classes=5),
    title="CWE Prediction from Commit Message",
    description="Type a Git commit message and get the most likely CWE classes predicted by the model.",
    examples=[
        ["Fixed buffer overflow in input parsing"],
        ["SQL injection possible in user login endpoint"]
    ]
)

if __name__ == "__main__":
    demo.launch()