app.py

import streamlit as st
import numpy as np
import tensorflow as tf
from PIL import Image

# Load the trained model
@st.cache_resource
def load_model():
    return tf.keras.models.load_model("signature_verification_model.h5")

model = load_model()

# Function to preprocess the image
def preprocess_image(image):
    image = image.convert("L")  # Convert to grayscale
    image = image.resize((128, 128))  # Resize to match model input size
    image = np.array(image) / 255.0  # Normalize
    image = np.expand_dims(image, axis=0)  # Add batch dimension
    return image

# Streamlit UI
st.title("Signature Verification System")
uploaded_file = st.file_uploader("Upload a signature image", type=["png", "jpg", "jpeg"])

if uploaded_file is not None:
    image = Image.open(uploaded_file)
    st.image(image, caption="Uploaded Signature", use_column_width=True)

    # Preprocess and predict
    processed_img = preprocess_image(image)
    prediction = model.predict(processed_img)

    # Display result
    if prediction[0, 1] > 0.5:
        st.success("✅ Valid Signature")
    else:
        st.error("❌ Invalid Signature")