Delete main.py

main.py

@@ -1,49 +0,0 @@
-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,)