Upload main.py

main.py

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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 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):
+        # 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")
+        out = gr.Image(type="filepath", label="Result", )
+    btn = gr.Button("Generate")
+    btn.click(virtual_tryon, [src, ref], out)
+
+demo.launch(share=True,
+            show_error=True,
+            pwa=True,)