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README.md

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+---
+title: VTRY
+emoji: 👁
+colorFrom: red
+colorTo: pink
+sdk: gradio
+sdk_version: 5.28.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+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(
+    "franciszzj/Leffa",
+    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,
+)