import gradio as gr
import tensorflow as tf
from PIL import Image
import numpy as np

# Load your pre-trained model (e.g., TensorFlow Keras model)
# Replace "currency_model.h5" with the actual path to your model
model = tf.keras.models.load_model("counterfeitmodel.h5")

# Define the prediction function
def detect_currency(image):
    """
    Function to detect if the input image is of a real or fake currency note.
    :param image: Uploaded image (.jpg)
    :return: String indicating 'Real' or 'Fake'
    """
    # Resize the image to match the input size expected by the model
    input_size = (200, 200)  # Replace with your model's expected input size
    image = image.resize(input_size)
    
    # Convert the image to a NumPy array
    image_array = np.array(image) / 255.0  # Normalize pixel values to [0, 1]
    
    # Add batch dimension
    image_array = np.expand_dims(image_array, axis=0)
    
    # Perform prediction
    prediction = model.predict(image_array)
    
    # Assume the model outputs a probability for 'Real' (1) and 'Fake' (0)
    is_real = prediction[0][0] > 0.5
    return "Real Currency Note" if is_real else "Fake Currency Note"
css ="""
#currency-image {
    border: 2px solid #00BFFF;
    border-radius: 10px;
    padding: 10px;
}

#prediction-output {
    font-size: 20px;
    font-weight: bold;
    color: #2c3e50;
    margin-top: 20px;
    text-align: center;
    background-color: #ecf0f1;
    border: 2px solid #00BFFF;
    border-radius: 10px;
    padding: 10px;
}
"""
# Create the Gradio interface
interface = gr.Interface(
    fn=detect_currency,  # The function to run
    inputs=gr.Image(type="pil", label="Upload Currency Image (.jpg)"),  # Input type
    outputs="text",  # Output type
    title="Currency Note Detector",
    description="Upload a .jpg image of a currency note to check if it's real or fake.",
    theme=gr.themes.Base(),  # Hugging Face theme for a professional look
    live=True,
    css=css,
)

# Add the custom CSS to the interface

# Launch the app
interface.launch(share=True)