import streamlit as st
from run import process
import time
import cv2
from PIL import Image
import numpy as np

# Global variable
index = 1

def mainTest(inputpath, outpath):
    watermark = deep_nude_process(inputpath)
    watermark1 = cv2.cvtColor(watermark, cv2.COLOR_BGRA2RGBA)
    return watermark1

def deep_nude_process(inputpath):
    dress = cv2.imread(inputpath)
    h = dress.shape[0]
    w = dress.shape[1]
    dress = cv2.resize(dress, (512, 512), interpolation=cv2.INTER_CUBIC)
    watermark = process(dress)
    watermark = cv2.resize(watermark, (w, h), interpolation=cv2.INTER_CUBIC)
    return watermark

def inference(img):
    global index
    bgra = cv2.cvtColor(img, cv2.COLOR_RGBA2BGRA)
    inputpath = f"input_{index}.jpg"
    cv2.imwrite(inputpath, bgra)

    outputpath = f"out_{index}.jpg"
    index += 1
    print(time.strftime("START!!!!!!!!! %Y-%m-%d %H:%M:%S", time.localtime()))
    output = mainTest(inputpath, outputpath)
    print(time.strftime("Finish!!!!!!!!! %Y-%m-%d %H:%M:%S", time.localtime()))
    return output

def load_image_from_file(file_path, new_height=None):
    try:
        img = Image.open(file_path)
        
        if new_height is not None:
            aspect_ratio = img.width / img.height
            new_width = int(new_height * aspect_ratio)
            img = img.resize((new_width, new_height), Image.LANCZOS)
        
        return img
    except FileNotFoundError:
        print(f"File not found: {file_path}")
        return None
    except Image.UnidentifiedImageError:
        print(f"Cannot identify image file: {file_path}")
        return None
    except Exception as e:
        print(f"Error loading image from file: {e}")
        return None

# Streamlit app
st.title("Undress AI")
st.markdown("⛔ Input photos of people, similar to the test picture at the bottom, and undress pictures will be produced. You may have to wait 30 seconds for a picture. 🔞 Do not upload personal photos 🔞 There is a queue system. According to the logic of first come, first served, only one picture will be made at a time. Must be able to at least see the outline of a human body ⛔")

# Example images
examples = [
    load_image_from_file('example9.webp'),
    load_image_from_file('example2.png'),
    load_image_from_file('example1.png'),
    load_image_from_file('example5.webp'),
    load_image_from_file('example6.webp'),
    load_image_from_file('example8.webp'),
]

# Display example images
st.subheader("Examples")
for example in examples:
    st.image(example, use_column_width=True)

# Image upload
uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "webp"])

if uploaded_file is not None:
    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
    img = cv2.imdecode(file_bytes, 1)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

    if st.button("Run"):
        processed_img = inference(img)
        st.image(processed_img, caption="Processed Image", use_column_width=True)