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import os
import io
import PIL.Image # Workaround for PIL/Gradio bug :contentReference[oaicite:13]{index=13}
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(
"franciszzj/Leffa",
hf_token=HF_TOKEN,
) # Gradio Python client
def virtual_tryon(
person_path,
garment_path,
vt_garment_type="upper_body",
):
# 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="upper_body",
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() as demo:
gr.Markdown("## Leffa Virtual Try-On")
with gr.Row():
src = gr.Image(sources="upload", type="filepath", label="Person Image")
ref = gr.Image(sources="upload",
type="filepath",
label="Garment Image")
vt_garment_type = gr.Radio(
label="Garment Type",
choices=[("Upper", "upper_body"), ("Lower", "lower_body"),
("Dress", "dresses")],
value="upper_body",
)
with gr.Column():
out = gr.Image(
type="filepath",
label="Result",
)
btn = gr.Button("Generate")
btn.click(virtual_tryon, [src, ref, vt_garment_type], out)
demo.launch(
share=True,
show_error=True,
pwa=True,
)
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