import gradio as gr
import pickle

# Load the trained spam classifier model
with open('spam_classifier_model.pkl', 'rb') as file:
    loaded_model = pickle.load(file)
#a='<div style="font-size: 24px; color: #f90a0a; font-weight: bold; text-align: center;">Spam</div>'
#b='<div style="font-size: 24px; color: #78ff00; font-weight: bold; text-align: center;">Not Spam</div>'

# Prediction function
def predict_spam(email_text):
    prediction = loaded_model.predict([email_text])
    if prediction[0] == 1:
        return (
            f'<div style="font-size: 30px; color: #f90a0a; font-weight: bold; text-align: center;">Spam</div>'
        )
    else:
        return (
            f'<div style="font-size: 30px; color: #78ff00; font-weight: bold; text-align: center;">Not Spam</div>'
        )

# Define the Gradio interface
interface = gr.Interface(
    theme=gr.themes.Citrus(
        primary_hue="cyan", 
        neutral_hue="neutral"
    ),
    fn=predict_spam,
    inputs=gr.Textbox(
        label="Email Content", 
        lines=3, 
        placeholder="Enter the email content here..."
    ),
    outputs=gr.HTML(label="Output"),# Single output styled for size
    title="<span style='color: #00fff7; font-size:40px; font-weight: bold;'>Email Spam Classifier</span>",
    description="<span style='color: lightblue; font-size:26px;'>Enter an email's content to check if it's Spam or Not Spam.</span>"
)

# Launch the Gradio app
interface.launch()