import streamlit as st
import cv2
import numpy as np
from PIL import Image
from io import BytesIO

# Function to apply Lightroom-like filters
def apply_filter(img):
    # Convert PIL Image to OpenCV format
    img = np.array(img)
    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)

    # Exposure: Adjust brightness
    exposure = -0.31
    img = cv2.convertScaleAbs(img, alpha=1.0, beta=exposure * 100)

    # Contrast
    contrast = -18
    alpha = 1 + contrast / 100.0  # Scaling factor
    img = cv2.convertScaleAbs(img, alpha=alpha, beta=0)

    # Highlights and Shadows
    highlights = -100
    shadows = 50
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    v = hsv[:, :, 2].astype(np.float32)
    v = np.clip(v + highlights, 0, 255)
    v = np.clip(v + shadows, 0, 255)
    hsv[:, :, 2] = v.astype(np.uint8)
    img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)

    # Whites and Blacks
    whites = 12
    blacks = -20
    img = np.clip(img + whites, 0, 255).astype(np.uint8)
    img = np.clip(img + blacks, 0, 255).astype(np.uint8)

    # Temperature and Tint
    temp = -2
    tint = 2
    b, g, r = cv2.split(img)
    b = np.clip(b + temp * 10, 0, 255).astype(np.uint8)
    r = np.clip(r + tint * 10, 0, 255).astype(np.uint8)
    img = cv2.merge((b, g, r))

    # Vibrance
    vibrance = 70
    lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
    l, a, b = cv2.split(lab)
    a = np.clip(a * (1 + vibrance / 100.0), 0, 255).astype(np.uint8)
    b = np.clip(b * (1 + vibrance / 100.0), 0, 255).astype(np.uint8)
    img = cv2.merge((l, a, b))
    img = cv2.cvtColor(img, cv2.COLOR_LAB2BGR)

    # Saturation
    saturation = 20
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    s = hsv[:, :, 1].astype(np.float32)
    s = np.clip(s * (1 + saturation / 100.0), 0, 255).astype(np.uint8)
    hsv[:, :, 1] = s
    img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)

    # Texture (Clarity approximation)
    texture = 4
    kernel = np.array([[-1, -1, -1], [-1, 8 + texture, -1], [-1, -1, -1]])
    img = cv2.filter2D(img, -1, kernel)

    # Sharpening
    sharpening = 18
    radius = 1.2
    gaussian = cv2.GaussianBlur(img, (0, 0), sigmaX=radius)
    img = cv2.addWeighted(img, 1 + sharpening / 100.0, gaussian, -sharpening / 100.0, 0)

    return img

# Streamlit app
st.title("Classic Photo Editor")
st.write("Upload an image to apply the Lightroom-like filter.")

# Upload image
uploaded_file = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"])

if uploaded_file:
    # Open the uploaded image
    img = Image.open(uploaded_file)

    # Display the original image
    st.subheader("Original Image")
    st.image(img, use_column_width=True)

    # Apply the filter
    st.subheader("Filtered Image")
    filtered_img = apply_filter(img)

    # Convert back to PIL Image for display
    filtered_pil = Image.fromarray(cv2.cvtColor(filtered_img, cv2.COLOR_BGR2RGB))
    st.image(filtered_pil, use_column_width=True)

    # Provide download button
    buf = BytesIO()
    filtered_pil.save(buf, format="JPEG")
    byte_im = buf.getvalue()
    st.download_button(
        label="Download Filtered Image",
        data=byte_im,
        file_name="filtered_image.jpg",
        mime="image/jpeg"
    )