Update app.py

app.py

@@ -1,67 +1,55 @@
-import gradio as gr
-from briarmbg import BriaRMBG
-from PIL import Image
-import torch
-import torchvision.transforms as T
-import os
-import json
-from fastapi import Request, HTTPException
-
-# ===== Load API key from Hugging Face secret or fallback =====
-API_KEY = os.getenv("API_KEY", "sk_muhammad8815_Z4gXr93nPqT2LwV8")
-
-# ===== Load model =====
-model = BriaRMBG.from_pretrained("./")
-model.load_state_dict(torch.load("model.pth", map_location="cpu"))
-model.eval()
-
-# ===== Define preprocessing =====
-transform = T.Compose([
-    T.Resize((512, 512)),
-    T.ToTensor()
-])
-
-# ===== Background removal function with API key check =====
-def remove_background(image, request: Request = None):
-    # Check for API key in Authorization header
-    if request is not None:
-        auth = request.headers.get("authorization")
-        if not auth or not auth.startswith("Bearer ") or auth.split(" ")[1] != API_KEY:
-            raise HTTPException(status_code=401, detail="Invalid or missing API key")
-
-    # Preprocess image
-    img = transform(image).unsqueeze(0)
-
-    with torch.no_grad():
-        result = model(img)
-
-    # Handle different result formats
-    if isinstance(result, dict) and "pred" in result:
-        result = result["pred"]
-    elif isinstance(result, (tuple, list)):
-        result = result[0]
-    if isinstance(result, list):
-        result = result[0]
-
-    result = result.squeeze().numpy()
-
-    # Apply transparency mask
-    image = image.resize((result.shape[1], result.shape[0]))
-    image = image.convert("RGBA")
-    pixels = image.load()
-
-    for y in range(image.height):
-        for x in range(image.width):
-            if result[y][x] < 0.5:
-                pixels[x, y] = (255, 255, 255, 0)
-
-    return image
-
-# ===== Launch Gradio app with secure FastAPI request passthrough =====
-gr.Interface(
-    fn=remove_background,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Image(type="pil"),
-    title="Background Remover",
-    allow_flagging="never"
-).launch()
+import gradio as gr
+from briarmbg import BriaRMBG
+from PIL import Image
+import torch
+import torchvision.transforms as T
+
+# ===== Load model =====
+model = BriaRMBG.from_pretrained("./")
+model.load_state_dict(torch.load("model.pth", map_location="cpu"))
+model.eval()
+
+# ===== Define preprocessing =====
+transform = T.Compose([
+    T.Resize((512, 512)),
+    T.ToTensor()
+])
+
+# ===== Background removal function (no auth needed) =====
+def remove_background(image):
+    # Preprocess image
+    img = transform(image).unsqueeze(0)
+
+    with torch.no_grad():
+        result = model(img)
+
+    # Handle different result formats
+    if isinstance(result, dict) and "pred" in result:
+        result = result["pred"]
+    elif isinstance(result, (tuple, list)):
+        result = result[0]
+    if isinstance(result, list):
+        result = result[0]
+
+    result = result.squeeze().numpy()
+
+    # Apply transparency mask
+    image = image.resize((result.shape[1], result.shape[0]))
+    image = image.convert("RGBA")
+    pixels = image.load()
+
+    for y in range(image.height):
+        for x in range(image.width):
+            if result[y][x] < 0.5:
+                pixels[x, y] = (255, 255, 255, 0)
+
+    return image
+
+# ===== Launch Gradio app (public access) =====
+gr.Interface(
+    fn=remove_background,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Image(type="pil"),
+    title="Background Remover",
+    allow_flagging="never"
+).launch()