Update app.py

app.py

@@ -1,61 +1,68 @@
+import os
 import gradio as gr
 import cv2
 import numpy as np
 from gradio_client import Client
-import io
-from PIL import Image
 
 # -----------------------------
-# Hunyuan3D API setup
+# Load Hugging Face API key safely
 # -----------------------------
-API_KEY = "HT_Token"  # Replace with your valid key
+API_KEY = os.environ.get("HF_TOKEN")  # read from environment or Secrets
+hunyuan_client = None
+
 try:
     hunyuan_client = Client("tencent/Hunyuan3D-2.1", hf_token=API_KEY)
-    # Grab first endpoint index for the 3D try-on function
-    fn_index_default = hunyuan_client.endpoints[0].fn_index if hunyuan_client.endpoints else 0
-    client_loaded = True
 except Exception as e:
-    print(f"Failed to load Hunyuan3D client: {e}")
-    hunyuan_client = None
-    fn_index_default = 0
-    client_loaded = False
+    print(f"Hunyuan3D client not loaded: {e}")
 
 # -----------------------------
-# Convert numpy image to bytes
+# Local "try-on" function
 # -----------------------------
-def np_to_bytes(img_np):
-    img_pil = Image.fromarray(cv2.cvtColor(img_np, cv2.COLOR_BGR2RGB))
-    buf = io.BytesIO()
-    img_pil.save(buf, format="PNG")
-    return buf.getvalue()
+def tryon_local(person_img, garment_img, seed, randomize_seed):
+    if person_img is None or garment_img is None:
+        return None, None, "Empty image"
+
+    h_person, w_person = person_img.shape[:2]
+    h_garment, w_garment = garment_img.shape[:2]
+
+    scale = w_person / w_garment
+    new_w = int(w_garment * scale)
+    new_h = int(h_garment * scale)
+    garment_resized = cv2.resize(garment_img, (new_w, new_h))
+
+    overlay = person_img.copy()
+    y_offset = 0
+    x_offset = max(0, (w_person - new_w) // 2)
+
+    if garment_resized.shape[2] == 4:
+        alpha = garment_resized[:, :, 3] / 255.0
+        for c in range(3):
+            overlay[y_offset:y_offset+new_h, x_offset:x_offset+new_w, c] = \
+                alpha * garment_resized[:, :, c] + (1 - alpha) * overlay[y_offset:y_offset+new_h, x_offset:x_offset+new_w, c]
+    else:
+        overlay[y_offset:y_offset+new_h, x_offset:x_offset+new_w] = garment_resized
+
+    info = "Success (local simulation)"
+    return overlay, seed, info
 
 # -----------------------------
-# Try-on function using Hunyuan3D API
+# Try-on via Hunyuan3D
 # -----------------------------
 def tryon_to_3d(person_img, garment_img, seed, randomize_seed):
-    if not client_loaded:
-        return None, "Hunyuan3D client not loaded. Please check your API key."
+    overlay_img, seed_used, info = tryon_local(person_img, garment_img, seed, randomize_seed)
 
-    if person_img is None or garment_img is None:
-        return None, "Empty image provided."
+    if hunyuan_client is None:
+        return overlay_img, "Hunyuan3D client not loaded. Please check your API key."
 
     try:
-        # Convert to bytes
-        person_bytes = np_to_bytes(person_img)
-        garment_bytes = np_to_bytes(garment_img)
-
-        # Call the 3D try-on endpoint
-        result = hunyuan_client.predict(
-            person_bytes,
-            garment_bytes,
-            fn_index=fn_index_default
-        )
-
-        # Convert returned bytes to image
-        result_img = np.array(Image.open(io.BytesIO(result)))
-        return cv2.cvtColor(result_img, cv2.COLOR_RGB2BGR), "3D try-on successful!"
+        # Convert image to bytes before sending to API
+        _, buffer = cv2.imencode(".png", overlay_img)
+        img_bytes = buffer.tobytes()
+        # Call the API (most HF endpoints accept a single positional argument)
+        result = hunyuan_client.predict(img_bytes)
+        return result, "3D try-on completed!"
     except Exception as e:
-        return None, f"Hunyuan3D API error: {e}"
+        return overlay_img, f"Hunyuan3D API error: {e}"
 
 # -----------------------------
 # Gradio UI