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import streamlit as st
import tensorflow as tf
import numpy as np
import cv2
from huggingface_hub import hf_hub_download
from tensorflow.keras.models import load_model
from io import BytesIO
from PIL import Image # Import PIL Image
# Authenticate and download model from Hugging Face
repo_id = "Hammad712/closed_eye_detection"
filename = "Closed_Eye_Detection_98.h5"
model_path = hf_hub_download(repo_id=repo_id, filename=filename)
# Load the downloaded model
model = load_model(model_path)
# Set image dimensions
img_height, img_width = 150, 150
# Custom CSS
def set_css(style):
st.markdown(f"<style>{style}</style>", unsafe_allow_html=True)
combined_css = """
.main, .sidebar .sidebar-content { background-color: #1c1c1c; color: #f0f2f6; }
.block-container { padding: 1rem 2rem; background-color: #333; border-radius: 10px; box-shadow: 0px 4px 10px rgba(0, 0, 0, 0.5); }
.stButton>button, .stDownloadButton>button { background: linear-gradient(135deg, #ff7e5f, #feb47b); color: white; border: none; padding: 10px 24px; text-align: center; text-decoration: none; display: inline-block; font-size: 16px; margin: 4px 2px; cursor: pointer; border-radius: 5px; }
.stSpinner { color: #4CAF50; }
.title {
font-size: 3rem;
font-weight: bold;
display: flex;
align-items: center;
justify-content: center;
}
.colorful-text {
background: -webkit-linear-gradient(135deg, #ff7e5f, #feb47b);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
}
.black-white-text {
color: black;
}
.small-input .stTextInput>div>input {
height: 2rem;
font-size: 0.9rem;
}
.small-file-uploader .stFileUploader>div>div {
height: 2rem;
font-size: 0.9rem;
}
.custom-text {
font-size: 1.2rem;
color: #feb47b;
text-align: center;
margin-top: -20px;
margin-bottom: 20px;
}
"""
# Streamlit application
st.set_page_config(layout="wide")
st.markdown(f"<style>{combined_css}</style>", unsafe_allow_html=True)
st.markdown('<div class="title"><span class="colorful-text">Eye</span> <span class="black-white-text">Detection Model</span></div>', unsafe_allow_html=True)
st.markdown('<div class="custom-text">Upload an image to predict whether the eyes are open or closed.</div>', unsafe_allow_html=True)
# Input for image URL or path
with st.expander("Input Options", expanded=True):
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
# Read the uploaded image
file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
image = cv2.imdecode(file_bytes, 1)
# Resize and preprocess the image
resized_image = cv2.resize(image, (img_height, img_width))
input_image = resized_image.reshape((1, img_height, img_width, 3)) / 255.0
# Perform inference
predictions = model.predict(input_image)
prediction = predictions[0][0]
def get_label(prediction):
return "Open Eye" if prediction >= 0.5 else "Closed Eye"
label = get_label(prediction)
# Display the image and prediction
st.image(image, channels="BGR", caption='Uploaded Image')
st.markdown(f"### Prediction: {prediction:.2f}, Label: {label}")
# Provide a download button for the uploaded image (optional)
img_byte_arr = BytesIO()
img = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
img.save(img_byte_arr, format='JPEG')
img_byte_arr = img_byte_arr.getvalue()
st.download_button(
label="Download Image",
data=img_byte_arr,
file_name="uploaded_image.jpg",
mime="image/jpeg"
)
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