Deploy.py

import streamlit as st
import cv2
import numpy as np
from tensorflow.keras.models import load_model

# Load your trained model
model = load_model('Fake-currency.keras')

# Function to preprocess image for the model
def preprocess_image(image):
    # Resize image to the size your model expects
    image = cv2.resize(image, (224, 224))
    # Normalize the image if needed
    image = image / 255.0
    # Expand dimensions to match input shape (1, height, width, channels)
    image = np.expand_dims(image, axis=0)
    return image

# Function for prediction
def predict_currency(image):
    processed_image = preprocess_image(image)
    prediction = model.predict(processed_image)
    return prediction

# Streamlit UI
st.title("Fake Currency Detection")

# Image upload section
uploaded_file = st.file_uploader("Choose an image...", type="jpg")

if uploaded_file is not None:
    # Convert the uploaded file to an OpenCV image
    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
    image = cv2.imdecode(file_bytes, 1)

    # Display the uploaded image
    st.image(image, channels="BGR")

    if st.button('Detect'):
        st.write('Processing...')
        prediction = predict_currency(image)
        label = 'Fake Currency' if prediction[0][0] > 0.5 else 'Real Currency'
        st.write(f"Prediction: {label}")