Create app.py

app.py

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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 requests
+
+# 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 or provide a URL 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):
+    url = st.text_input("Enter image URL", "")
+    uploaded_file = st.file_uploader("Or upload an image", type=["jpg", "jpeg", "png"])
+
+def load_image_from_url(url):
+    response = requests.get(url)
+    img = Image.open(BytesIO(response.content)).convert('RGB')
+    return np.array(img)
+
+if uploaded_file is not None or url:
+    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)
+    elif url:
+        # Read the image from URL
+        image = load_image_from_url(url)
+        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+
+    # 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' if uploaded_file is not None else 'Image from URL')
+    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="processed_image.jpg",
+        mime="image/jpeg"
+    )