import gradio as gr
import joblib

model = joblib.load("spam_rf_model.joblib")

def classify_message(message):
    if not message or message.strip() == "":
        return "Please enter a message.", 0.0

    prediction = model.predict([message])[0]

    if hasattr(model.named_steps["classifier"], "predict_proba"):
        probabilities = model.predict_proba([message])[0]
        classes = list(model.classes_)
        confidence = probabilities[classes.index(prediction)]
    else:
        confidence = 0.0

    if prediction.lower() == "spam":
        label = "SPAM Message"
    else:
        label = "NOT SPAM Message"

    return label, round(float(confidence), 4)

custom_css = """
body {
    background: radial-gradient(circle at top, #1e1b4b, #020617 55%, #000000);
}

.gradio-container {
    background: transparent !important;
    color: #e5e7eb !important;
}

#title-box {
    text-align: center;
    padding: 28px;
    border-radius: 24px;
    background: linear-gradient(135deg, rgba(79,70,229,0.35), rgba(6,182,212,0.25));
    border: 1px solid rgba(125, 211, 252, 0.35);
    box-shadow: 0 0 40px rgba(34, 211, 238, 0.22);
}

#title-box h1 {
    font-size: 42px;
    margin-bottom: 8px;
    background: linear-gradient(90deg, #67e8f9, #a78bfa, #f0abfc);
    -webkit-background-clip: text;
    -webkit-text-fill-color: transparent;
}

#title-box p {
    color: #cbd5e1;
    font-size: 17px;
}

textarea, input {
    background: rgba(15, 23, 42, 0.85) !important;
    color: #f8fafc !important;
    border: 1px solid rgba(125, 211, 252, 0.35) !important;
    border-radius: 18px !important;
}

button {
    border-radius: 18px !important;
    background: linear-gradient(90deg, #2563eb, #7c3aed, #06b6d4) !important;
    color: white !important;
    font-weight: 800 !important;
    border: none !important;
    box-shadow: 0 0 24px rgba(59, 130, 246, 0.35);
}

.output-class {
    background: rgba(15, 23, 42, 0.85) !important;
    border-radius: 18px !important;
    border: 1px solid rgba(167, 139, 250, 0.35) !important;
}
"""

with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
    gr.HTML(
        """
        <div id="title-box">
            <h1>AI Spam Message Detector</h1>
            <p>CountVectorizer Encoder + Random Forest Classifier</p>
        </div>
        """
    )

    with gr.Row():
        with gr.Column():
            message = gr.Textbox(
                label="Enter SMS / Email Message",
                placeholder="Example: Congratulations! You won a free prize. Click now...",
                lines=8
            )
            classify_btn = gr.Button("Analyze Message")

        with gr.Column():
            result = gr.Textbox(label="Prediction", elem_classes="output-class")
            confidence = gr.Number(label="Confidence Score", precision=4)

    classify_btn.click(
        fn=classify_message,
        inputs=message,
        outputs=[result, confidence]
    )

    gr.Examples(
        examples=[
            ["Free entry in 2 a weekly competition to win FA Cup final tickets"],
            ["Hi, are we still meeting for lunch tomorrow?"],
            ["Congratulations! You have won a cash prize. Reply now to claim."],
            ["Can you send me the report by tonight?"]
        ],
        inputs=message
    )

demo.launch(server_name="0.0.0.0", server_port=7860)