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"

# Custom CSS for Light Blue Background
custom_css = """
body {
    background-color: #d0f0fd;  /* Light Blue */
}
"""

# Create a Gradio interface with Dockerfile-specific examples and visuals
demo = gr.Interface(
    fn=classify_commit,
    inputs=gr.Textbox(
        label="Enter Commit Message",
        placeholder="Type your Dockerfile-related commit message here...",
        lines=3,
        max_lines=5,
    ),
    outputs=gr.Textbox(label="Predicted Labels"),
    title="🐳 Dockerfile Commit Message Classifier",
    description="🔍 Classify commit messages related to Dockerfiles into categories like 'bug fix', 'feature addition', and more. Enter a commit message to get the predictions.",
    examples=[
        ["Fixed an issue with the base image version in Dockerfile"],
        ["Added a new multistage build to optimize Docker image size"],
        ["Updated the Python version in Dockerfile to 3.10"],
        ["Sort Dockerfile"],
        ["Added COPY instruction for configuration files"],
    ],
    article="""
    **How to Use**:
    - Enter a commit message related to Dockerfiles.
    - The tool predicts categories such as 'bug fix', 'code refactoring', 'feature addition', or 'maintenance/other'.
    
    **Examples**:
    - Fixing base image versions.
    - Adding new Docker build stages.
    - Updating dependencies in Dockerfiles.
    """,
    css=custom_css,
)

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