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

st.set_page_config(page_title="Grayscale Image Converter", layout="centered")

st.title("🖼️ Grayscale Image Converter with Filters & Resize")
st.write("Upload an image, apply filters, resize it, and download the grayscale version!")

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

if uploaded_file is not None:
    # Convert uploaded file to numpy array
    file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8)
    image = cv2.imdecode(file_bytes, 1)  # Load image in color (BGR)

    # Convert to grayscale
    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Convert OpenCV images to PIL format for Streamlit display
    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    original_pil = Image.fromarray(image_rgb)
    gray_pil = Image.fromarray(gray_image)

    # Show original and grayscale images side-by-side
    st.subheader("Original vs Grayscale Image")
    col1, col2 = st.columns(2)
    with col1:
        st.image(original_pil, caption="Original Image", use_column_width=True)
    with col2:
        st.image(gray_pil, caption="Grayscale Image", use_column_width=True)

    # Filter options
    st.subheader("Apply Filters")
    filter_option = st.selectbox(
        "Choose a filter to apply",
        ["None", "Gaussian Blur", "Sharpen", "Edge Detection"]
    )

    if filter_option == "Gaussian Blur":
        image = cv2.GaussianBlur(image, (15, 15), 0)
        gray_image = cv2.GaussianBlur(gray_image, (15, 15), 0)
    elif filter_option == "Sharpen":
        kernel = np.array([[0, -1, 0], [-1, 5,-1], [0, -1, 0]])
        image = cv2.filter2D(image, -1, kernel)
        gray_image = cv2.filter2D(gray_image, -1, kernel)
    elif filter_option == "Edge Detection":
        image = cv2.Canny(image, 100, 200)
        gray_image = cv2.Canny(gray_image, 100, 200)

    # Resize options
    st.subheader("Resize Image")
    width = st.slider("Select width", 50, 500, 300)
    height = int((width / original_pil.width) * original_pil.height)
    resized_image = cv2.resize(image, (width, height))
    resized_gray_image = cv2.resize(gray_image, (width, height))

    # Display filtered and resized images
    st.subheader("Filtered & Resized Image")
    filtered_resized_rgb = cv2.cvtColor(resized_image, cv2.COLOR_BGR2RGB)
    filtered_resized_pil = Image.fromarray(filtered_resized_rgb)
    filtered_resized_gray_pil = Image.fromarray(resized_gray_image)
    
    col1, col2 = st.columns(2)
    with col1:
        st.image(filtered_resized_pil, caption="Filtered Resized Image", use_column_width=True)
    with col2:
        st.image(filtered_resized_gray_pil, caption="Filtered Resized Grayscale", use_column_width=True)

    # Convert filtered grayscale image to bytes for download
    buf = BytesIO()
    filtered_resized_gray_pil.save(buf, format="PNG")
    byte_im = buf.getvalue()

    # Add download button
    st.download_button(
        label="📥 Download Filtered & Resized Grayscale Image",
        data=byte_im,
        file_name="filtered_resized_grayscale_image.png",
        mime="image/png"
    )