Update app.py

app.py

@@ -5,7 +5,8 @@ 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
+from PIL import Image
+import requests
 
 # Authenticate and download model from Hugging Face
 repo_id = "Hammad712/closed_eye_detection"
@@ -65,16 +66,27 @@ 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)
+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):
-    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)
+    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))
@@ -90,7 +102,7 @@ if uploaded_file is not None:
     label = get_label(prediction)
 
     # Display the image and prediction
-    st.image(image, channels="BGR", caption='Uploaded Image')
+    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)
@@ -102,6 +114,6 @@ if uploaded_file is not None:
     st.download_button(
         label="Download Image",
         data=img_byte_arr,
-        file_name="uploaded_image.jpg",
+        file_name="processed_image.jpg",
         mime="image/jpeg"
     )