app.py

import os
import io
# Workaround for PIL/Gradio bug :contentReference[oaicite:13]{index=13}
import PIL.Image
import gradio as gr
from gradio_client import Client, handle_file

from gradio_client.client import re
from numpy import array
# 1. Load your HF token from env
HF_TOKEN = os.getenv("HF_TOKEN")  # export HF_TOKEN="hf_..."
# 1) Connect to the Leffa Gradio app’s predict endpoint
# Use the full "/call/predict" API path as shown on the View API page
client = Client(
    "GenieLamp/VTON",
    hf_token=HF_TOKEN,
)  # Gradio Python client


def virtual_tryon(
    person_path,
    garment_path,
    garment_type,
):
    # 2) Wrap file inputs so Gradio client uploads them correctly
    person_file = handle_file(
        person_path
    )  # handle_file uploads the image :contentReference[oaicite:6]{index=6}
    garment_file = handle_file(garment_path)

    # 3) Build inputs in the exact order shown on the “Use via API” page :contentReference[oaicite:7]{index=7}

    # 4) Call the named endpoint with handle_file inputs
    result = client.predict(
        person_file,  # Person Image
        garment_file,  # Garment Image
        ref_acceleration=False,
        step=30,
        scale=2.5,
        seed=42,
        vt_model_type="viton_hd",
        vt_garment_type=garment_type,
        vt_repaint=False,
        api_name="/leffa_predict_vt",)
    # result[0] is the generated image filepath on the server
    return result[0]  # Gradio will download & display this file

    # 5) Gradio UI


with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("## V_TRY DEMO")
    with gr.Row():
        with gr.Column():
            gr.Markdown("####UPLOAD PERSON IMAGE")
            src = gr.Image(sources="upload", type="filepath",
                           label="Person Image")
        with gr.Column():
            gr.Markdown("####UPLOAD GARMENT IMAGE")
            ref = gr.Image(sources="upload", type="filepath",
                           label="Garment Image")
        with gr.Column():
            gr.Markdown("####Select the Garment type")
            garment_type = gr.Radio(
                choices=[("Upper", "upper_body"),
                         ("Lower", "lower_body"), ("Dress", "dresses")],
                value="upper_body",
                label="Garment Type",
            )
        with gr.Column():
            gr.Markdown("####Output Image")
            out = gr.Image(type="filepath", label="Result",)
            with gr.Row():
                btn = gr.Button("Generate")

        btn.click(virtual_tryon, [src, ref, garment_type], out)

demo.launch(
    share=True,
    show_error=True,
    pwa=True,
)