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import streamlit as st
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
# Load the model
model = load_model('cnn_model.h5', compile=False)
# Function to process the uploaded image
def process_image(img):
img = img.resize((128, 128))
img = np.array(img)
img = img / 255.0
img = np.expand_dims(img, axis=0)
return img
# Title of the application
st.title('👶 Age Detection from Image 📸')
st.write("Upload a photo, and the model will predict the age.")
# Sidebar for additional interaction options
st.sidebar.header("Instructions")
st.sidebar.write("""
1. Upload an image of a face.
2. The model will predict the age based on the image.
3. The output will be displayed below the image.
""")
# File uploader for the user to upload an image
file = st.file_uploader('Select an image (jpg, jpeg, png)', type=['jpg', 'jpeg', 'png'])
if file is not None:
# Displaying the uploaded image
img = Image.open(file)
st.image(img, caption='Uploaded Image')
# Process the image and predict the result
image = process_image(img)
prediction = model.predict(image)
prediction = np.round(prediction).astype(int) # Rounding the prediction
# Show result in a more interactive format
st.subheader("Prediction Result:")
st.write(f"Predicted Age: **{prediction[0][0]}** years old")
# Optionally, you can add a confidence message
st.markdown(f"""
**Confidence:** The model has made this prediction based on its trained data, but the prediction may vary depending on the quality of the image and other factors.
""")
# A divider for clarity
st.markdown("---")
else:
st.write("Please upload an image to get started.")
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