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import streamlit as st
from PIL import Image
from io import BytesIO
from transformers import pipeline
# Title and App Description
st.title("👗 Virtual Dress Try-On")
st.write("""
Upload a **Human Body Image** and a **Garment Image** to generate a Virtual Try-On.
Images will be compressed to meet Hugging Face size constraints.
""")
# Function to load and compress images
def compress_image(image_file, max_size_kb=512):
"""
Compresses the input image to meet size constraints while preserving the aspect ratio.
Args:
image_file: Uploaded file from Streamlit
max_size_kb: Maximum file size in kilobytes
Returns:
Compressed PIL Image object
"""
img = Image.open(image_file).convert("RGB")
quality = 95 # Initial compression quality
img_format = "JPEG"
# Compress image iteratively until it meets size constraints
while True:
img_bytes = BytesIO()
img.save(img_bytes, format=img_format, quality=quality)
size_kb = len(img_bytes.getvalue()) / 1024 # Size in KB
if size_kb <= max_size_kb or quality <= 10:
break
quality -= 5 # Reduce quality to compress further
compressed_img = Image.open(img_bytes)
return compressed_img
# Load Model (Hugging Face Pipeline)
@st.cache_resource
def load_model():
model_pipeline = pipeline("image-to-image", model="ares1123/virtual-dress-try-on")
return model_pipeline
model = load_model()
# Sidebar for Image Upload
st.sidebar.header("Upload Images")
uploaded_person = st.sidebar.file_uploader("Upload Human Body Image", type=["jpg", "jpeg", "png"])
uploaded_clothing = st.sidebar.file_uploader("Upload Garment Image", type=["jpg", "jpeg", "png"])
# Process and Display Images
if uploaded_person and uploaded_clothing:
# Compress uploaded images
st.sidebar.info("Compressing images to meet size constraints...")
person_image = compress_image(uploaded_person)
garment_image = compress_image(uploaded_clothing)
# Display compressed images
col1, col2 = st.columns(2)
col1.subheader("Compressed Human Body Image")
col1.image(person_image, use_column_width=True)
col2.subheader("Compressed Garment Image")
col2.image(garment_image, use_column_width=True)
# Process button
if st.button("👗 Generate Virtual Try-On"):
with st.spinner("Processing images... Please wait ⏳"):
try:
# Prepare inputs for the model
inputs = {"image": person_image, "clothing": garment_image}
# Generate output using Hugging Face model
output_image = model(inputs)
# Display output image
st.subheader("✨ Virtual Try-On Result")
st.image(output_image, use_column_width=True, caption="Composite Virtual Try-On Image")
except Exception as e:
st.error(f"An error occurred during processing: {e}")
else:
st.warning("Please upload both the Human Body Image and Garment Image.")
# Footer
st.markdown("---")
st.write("Developed with ❤️ using Streamlit and Hugging Face.")
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