app.py

import gradio as gr
from joblib import load

# Load the model and preprocessing artifacts
model = load("logistic_model.joblib")
tfidf_vectorizer = load("tfidf_vectorizer.joblib")
mlb = load("label_binarizer.joblib")

# Define a function to classify commit messages
def classify_commit(message):
    # Preprocess the input message
    X_tfidf = tfidf_vectorizer.transform([message])

    # Predict the labels
    prediction = model.predict(X_tfidf)
    predicted_labels = mlb.inverse_transform(prediction)

    # Return the predicted labels as a comma-separated string
    return ", ".join(predicted_labels[0]) if predicted_labels[0] else "No labels"

# Create a Gradio interface with enhanced aesthetics
demo = gr.Interface(
    fn=classify_commit,
    inputs=gr.Textbox(
        label="Enter Commit Message",
        placeholder="Type your commit message here...",
        lines=3,
        max_lines=5,
    ),
    outputs=gr.Textbox(label="Predicted Labels"),
    title="🚀 Commit Message Classifier",
    description="📜 This tool classifies commit messages into categories like 'bug fix', 'feature addition', and more. Enter a message to see the predicted labels.",
    examples=[
        ["Fixed a bug in the login feature"],
        ["Added a new user dashboard"],
        ["Updated order processing logic"],
        ["Refactored the payment module for better performance"],
    ],
    theme="compact",  # Optional: A compact Gradio theme
)

# Launch the Gradio app
demo.launch(share=True, server_port=7860)