app.py

import gradio as gr
from gradio_client import Client, handle_file
from PIL import Image, ImageOps
import os
import tempfile

def resize_and_pad(image_path, dimension):
    # Open the image
    img = Image.open(image_path)
    original_width, original_height = img.size

    if dimension == "Square (1024x1024)":
        target_width, target_height = 1024, 1024
    elif dimension == "Portrait (768x1360)":
        target_width, target_height = 768, 1360
    elif dimension == "Landscape (1360x768)":
        target_width, target_height = 1360, 768
    else:
        raise ValueError("Invalid dimension selected")

    # Calculate the scaling factor
    width_ratio = target_width / original_width
    height_ratio = target_height / original_height
    scaling_factor = min(width_ratio, height_ratio)

    # Calculate new dimensions
    new_width = int(original_width * scaling_factor)
    new_height = int(original_height * scaling_factor)

    # Resize the image
    img = img.resize((new_width, new_height), Image.LANCZOS)

    # Create a new white image with target dimensions
    new_img = Image.new("RGB", (target_width, target_height), (255, 255, 255))

    # Calculate padding
    offset = ((target_width - new_width) // 2, (target_height - new_height) // 2)

    # Paste the resized image into the new image
    new_img.paste(img, offset)

    # Save the new image temporarily
    padded_image_path = os.path.join(tempfile.gettempdir(), "padded_image.png")
    new_img.save(padded_image_path)

    return padded_image_path

def image_to_image(prompt, image, dimension, flux_client):
    # First, resize and pad the image
    padded_image_path = resize_and_pad(image, dimension)

    prompt = "Transforms a user-provided input image based on their prompt while maintaining consistency and fidelity to the original image. Generate an output image that accurately reflects the user's requested modifications. " + prompt

    image = flux_client.predict(
    input_image=handle_file(padded_image_path),
    prompt=prompt,
    guidance_scale=2.5,
    steps=25,
    randomize_seed=True,
    api_name="/infer"
    )

    print(image)
    # Clean up the temporary file (optional, but good practice)
    try:
        os.remove(padded_image_path)
    except:
        pass
    return image[0]

def image_to_video(prompt, image, duration, ltx_client):
    result = ltx_client.predict(
        prompt=prompt,
        input_image_url=None,
        middle_image_url=handle_file(image),
        final_image_url=None,
        duration_ui=4,
        api_name="/generate_video"
    )
    print(result)
    return result

def personalized_video(prompt, image, dimension, duration, request: gr.Request):
    x_ip_token = request.headers['x-ip-token']
    flux_client = Client("black-forest-labs/FLUX.1-Kontext-Dev", headers={"x-ip-token": x_ip_token})
    ltx_client = Client("KingNish/ltx-video-distilled", headers={"x-ip-token": x_ip_token})
    image = image_to_image(prompt, image, dimension, flux_client)
    yield image, None
    video = image_to_video(prompt, image, duration, ltx_client)
    yield image, video

with gr.Blocks() as demo:
    gr.Markdown("## Personalized Video Generator")
    with gr.Row():
        with gr.Column():
            input_image = gr.Image(label="Input Image", type="filepath")
            prompt = gr.Textbox(label="Prompt")
            dimension = gr.Radio(["Square (1024x1024)", "Portrait (768x1360)", "Landscape (1360x768)"], label="Dimension", value="Square (1024x1024)")
            duration = gr.Slider(minimum=1, maximum=5, value=4, label="Video Duration")
            submit_button = gr.Button("Submit")
        with gr.Column():
            edited_image = gr.Image(label="Edited Image")
            output_video = gr.Video(label="Output Video")
    submit_button.click(personalized_video, [prompt, input_image, dimension, duration], [edited_image, output_video])
    prompt.submit(personalized_video, [prompt, input_image, dimension, duration], [edited_image, output_video])

demo.launch(show_error=True)