import streamlit as st
from PIL import Image
import numpy as np
import io
import zipfile

# Title of the app
st.title("Image Transformation App")

# Upload image files
uploaded_files = st.file_uploader("Upload Images", type=["jpg", "jpeg", "png"], accept_multiple_files=True)

if uploaded_files:
    images = [Image.open(file) for file in uploaded_files]

    st.header("Uploaded Images")
    for i, image in enumerate(images):
        st.image(image, caption=f"Image {i+1}", use_container_width=True)

    # Transformation options
    st.header("Select Transformations")
    options = st.multiselect("Choose transformations:", [
        "Rotation",
        "Scaling",
        "Shearing",
        "Cropping",
    ])

    # Parameters for transformations
    angle = scale_factor = shear_factor = left = top = right = bottom = None

    if "Rotation" in options:
        angle = st.slider("Rotation Angle (degrees):", 0, 360, 90)

    if "Scaling" in options:
        scale_factor = st.slider("Scaling Factor:", 0.1, 3.0, 1.0)

    if "Shearing" in options:
        shear_factor = st.slider("Shearing Factor:", -0.5, 0.5, 0.0)

    if "Cropping" in options:
        left = st.slider("Left Crop:", 0, min([image.width for image in images]) - 1, 0)
        top = st.slider("Top Crop:", 0, min([image.height for image in images]) - 1, 0)
        right = st.slider("Right Crop:", left + 1, min([image.width for image in images]), min([image.width for image in images]))
        bottom = st.slider("Bottom Crop:", top + 1, min([image.height for image in images]), min([image.height for image in images]))

    # Perform transformations
    transformed_images = []

    for image in images:
        transformed_image = image.copy()

        if "Rotation" in options and angle is not None:
            transformed_image = transformed_image.rotate(angle)

        if "Scaling" in options and scale_factor is not None:
            new_size = (int(transformed_image.width * scale_factor), int(transformed_image.height * scale_factor))
            transformed_image = transformed_image.resize(new_size)

        if "Shearing" in options and shear_factor is not None:
            matrix = [
                1, shear_factor, 0,
                0, 1, 0
            ]
            transformed_image = transformed_image.transform(transformed_image.size, Image.AFFINE, matrix)

        if "Cropping" in options and left is not None and top is not None and right is not None and bottom is not None:
            transformed_image = transformed_image.crop((left, top, right, bottom))

        transformed_images.append(transformed_image)

    # Display transformed images
    st.header("Transformed Images")
    for i, transformed_image in enumerate(transformed_images):
        st.image(transformed_image, caption=f"Transformed Image {i+1}", use_column_width=True)

    # Download all transformed images as a zip file
    if transformed_images:
        zip_buffer = io.BytesIO()
        with zipfile.ZipFile(zip_buffer, "w") as zf:
            for i, transformed_image in enumerate(transformed_images):
                img_buffer = io.BytesIO()
                transformed_image.save(img_buffer, format="PNG")
                zf.writestr(f"transformed_image_{i+1}.png", img_buffer.getvalue())

        zip_buffer.seek(0)
        st.download_button(
            label="Download All Transformed Images",
            data=zip_buffer,
            file_name="Transformed_images.zip",
            mime="application/zip",
        )