Update app.py

app.py

@@ -5,6 +5,7 @@ import numpy as np
 from huggingface_hub import HfApi, hf_hub_download
 from PIL import Image
 from io import BytesIO
+import requests
 
 # Hugging Face credentials
 api = HfApi()
@@ -39,6 +40,11 @@ def infer_image(model, image):
     generated_img = (generated_img * 127.5 + 127.5).numpy().astype(np.uint8)  # De-normalize to [0, 255]
     return generated_img
 
+def load_image_from_url(url):
+    response = requests.get(url)
+    img = Image.open(BytesIO(response.content))
+    return img
+
 # Custom CSS
 combined_css = """
     .main, .sidebar .sidebar-content { background-color: #1c1c1c; color: #f0f2f6; }
@@ -82,15 +88,24 @@ 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">Photo</span> <span class="black-white-text">to Art</span></div>', unsafe_allow_html=True)
-st.markdown('<div class="custom-text">Convert Photos to Art using CycleGAN</div>', unsafe_allow_html=True)
+st.markdown('<div class="title"><span class="colorful-text">Photo</span> <span class="black-white-text">to Van Gogh</span></div>', unsafe_allow_html=True)
+st.markdown('<div class="custom-text">Convert photos to Van Gogh style using AI</div>', unsafe_allow_html=True)
 
 # Streamlit UI
 uploaded_file = st.file_uploader("Choose an image...", type="jpg")
+image_url = st.text_input("Or enter an image URL:")
 
+image = None
 if uploaded_file is not None:
-    # Load and display the uploaded image
     image = Image.open(uploaded_file)
+elif image_url:
+    try:
+        image = load_image_from_url(image_url)
+    except Exception as e:
+        st.error(f"Failed to load image from URL: {e}")
+
+if image is not None:
+    st.image(image, caption='Original Image', use_column_width=True)
     
     if st.button("Run Inference"):
         # Perform inference