Certainly! Below is the complete Streamlit app code that combines both basic image transformations and a simulation of latent space augmentation to generate synthetic images. This version doesn't require training a model and instead uses traditional image processing techniques along with simulated noise addition to create augmented images. Users can upload 1 to 10 images, specify the desired number of augmented samples per original image, and download a ZIP file containing all the generated synthetic images.

### Complete Streamlit App Code

```python
import streamlit as st
from PIL import Image, ImageEnhance, ImageOps
import numpy as np
import io
import zipfile

def apply_basic_augmentations(image):
    """Applies basic augmentations such as rotation and color jitter."""
    image = image.rotate(np.random.uniform(-30, 30))
    enhancer = ImageEnhance.Color(image)
    image = enhancer.enhance(np.random.uniform(0.75, 1.25))
    if np.random.rand() > 0.5:
        image = ImageOps.mirror(image)
    return image

def simulate_latent_space_noising(image, noise_scale=25):
    """Simulates latent space manipulation by adding noise."""
    image_array = np.array(image)
    noise = np.random.normal(0, noise_scale, image_array.shape)
    noised_image_array = np.clip(image_array + noise, 0, 255).astype(np.uint8)
    return Image.fromarray(noised_image_array)

def augment_image(image, augmentations_count):
    """Generates augmented versions of a single image."""
    augmented_images = []
    for _ in range(augmentations_count):
        augmented_image = apply_basic_augmentations(image)
        augmented_image = simulate_latent_space_noising(augmented_image)
        augmented_images.append(augmented_image)
    return augmented_images

def create_downloadable_zip(augmented_images):
    """Creates a ZIP file in memory for downloading."""
    zip_buffer = io.BytesIO()
    with zipfile.ZipFile(zip_buffer, "a", zipfile.ZIP_DEFLATED, False) as zip_file:
        for idx, image in enumerate(augmented_images):
            img_byte_arr = io.BytesIO()
            image.save(img_byte_arr, format="JPEG")
            zip_file.writestr(f"augmented_image_{idx+1}.jpg", img_byte_arr.getvalue())
    zip_buffer.seek(0)
    return zip_buffer

st.title("Batch Image Augmentation for Dataset Creation")

uploaded_files = st.file_uploader("Choose images (1-10)", accept_multiple_files=True, type=["jpg", "jpeg", "png"])
augmentations_count = st.number_input("Number of augmented samples per image", min_value=1, max_value=10, value=3)

if uploaded_files:
    all_augmented_images = []
    for uploaded_file in uploaded_files:
        image = Image.open(uploaded_file).convert("RGB")
        augmented_images = augment_image(image, augmentations_count)
        all_augmented_images.extend(augmented_images)

    if st.button("Download Augmented Dataset") and all_augmented_images:
        zip_buffer = create_downloadable_zip(all_augmented_images)
        st.download_button(
            label="Download ZIP",
            data=zip_buffer,
            file_name="augmented_images.zip",
            mime="application/zip"
        )