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import streamlit as st
import requests
import os
import sys
from PIL import Image
import io
import time
from pathlib import Path
# Set API URL
API_URL = "http://localhost:8001" # Local FastAPI server URL
st.set_page_config(
page_title="NAFNet Image Deblurring",
page_icon="🔍",
layout="wide",
)
st.title("NAFNet Image Deblurring Application")
st.markdown("""
Transform your blurry photos into clear, sharp images using the state-of-the-art NAFNet AI model.
Upload an image to get started!
""")
# File uploader
uploaded_file = st.file_uploader(
"Choose a blurry image...", type=["jpg", "jpeg", "png", "bmp"])
# Sidebar controls
with st.sidebar:
st.header("About NAFNet")
st.markdown("""
**NAFNet** (Nonlinear Activation Free Network) is a state-of-the-art image restoration model designed for tasks like deblurring.
Key features:
- High-quality image deblurring
- Fast processing time
- Preservation of image details
""")
st.markdown("---")
# Check API status
if st.button("Check API Status"):
try:
response = requests.get(f"{API_URL}/status/", timeout=5)
if response.status_code == 200 and response.json().get("status") == "ok":
st.success("✅ API is running and ready")
# Display additional info if available
memory_info = response.json().get("memory", {})
if memory_info:
st.info(f"CUDA Memory: {memory_info.get('cuda_memory_allocated', 'N/A')}")
else:
st.error("❌ API is not responding properly")
except:
st.error("❌ Cannot connect to API")
# Process when upload is ready
if uploaded_file is not None:
# Display the original image
col1, col2 = st.columns(2)
with col1:
st.subheader("Original Image")
image = Image.open(uploaded_file)
st.image(image, use_container_width=True)
# Process image button
process_button = st.button("Deblur Image")
if process_button:
with st.spinner("Deblurring your image... Please wait."):
try:
# Prepare simplified file structure
files = {
"file": ("image.jpg", uploaded_file.getvalue(), "image/jpeg")
}
# Send request to API
response = requests.post(f"{API_URL}/deblur/", files=files, timeout=60)
if response.status_code == 200:
with col2:
st.subheader("Deblurred Result")
deblurred_img = Image.open(io.BytesIO(response.content))
st.image(deblurred_img, use_column_width=True)
# Option to download the deblurred image
st.download_button(
label="Download Deblurred Image",
data=response.content,
file_name=f"deblurred_{uploaded_file.name}",
mime="image/png"
)
else:
try:
error_details = response.json().get('detail', 'Unknown error')
except:
error_details = response.text
st.error(f"Error: {error_details}")
except Exception as e:
st.error(f"An error occurred: {str(e)}")
# Footer
st.markdown("---")
st.markdown("Powered by NAFNet - Image Restoration Project")
def main():
pass # Streamlit already runs the script from top to bottom
if __name__ == "__main__":
main() |