Upload app.py

app.py

@@ -1,27 +1,38 @@
 import os
 import io
-import PIL.Image       # Workaround for PIL/Gradio bug :contentReference[oaicite:13]{index=13}
+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
+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}
+
+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}
+    # 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
+    # 4) Call the named endpoint with handle_file inputs
     result = client.predict(
-        person_file,      # Person Image
-        garment_file,     # Garment Image
+        person_file,  # Person Image
+        garment_file,  # Garment Image
         ref_acceleration=False,
         step=30,
         scale=2.5,
@@ -29,22 +40,38 @@ def virtual_tryon(person_path, garment_path):
         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
+        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("## V_TRY 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")
+        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", )
+        out = gr.Image(
+            type="filepath",
+            label="Result",
+        )
     btn = gr.Button("Generate")
-    btn.click(virtual_tryon, [src, ref], out)
+    btn.click(virtual_tryon, [src, ref, vt_garment_type], out)
+
+demo.launch(
+    share=True,
+    show_error=True,
+    pwa=True,
+)
 
-demo.launch(share=True,
-            show_error=True,
-            pwa=True,)