Update app.py

app.py

@@ -1,22 +1,37 @@
-import gradio as gr
-from ultralytics import YOLO
-from PIL import Image
-import numpy as np
-
-# Load model
-model = YOLO("best.pt")
-
-def detect(image):
-    results = model(image)
-    annotated_image = results[0].plot()
-    return Image.fromarray(annotated_image)
-
-interface = gr.Interface(
-    fn=detect,
-    inputs=gr.Image(type="pil"),
-    outputs=gr.Image(type="pil"),
-    title="Food & Object Detection",
-    description="Upload an image to detect objects."
-)
-
-interface.launch()
+import torch
+import torch.nn as nn
+import gradio as gr
+from ultralytics import YOLO
+from ultralytics.nn.tasks import DetectionModel
+from ultralytics.nn.modules.conv import Conv
+from PIL import Image
+
+# ---- FIX for PyTorch 2.6+ ----
+torch.serialization.add_safe_globals([DetectionModel, nn.Sequential, Conv])
+
+# ---- Load trained YOLO model ----
+model = YOLO("best.pt")   # your junk food model
+model.to("cpu")           # Required for Hugging Face Spaces
+
+# ---- Prediction function ----
+def predict(image):
+    results = model.predict(image, conf=0.25)
+    annotated = results[0].plot()   # BGR numpy image
+    
+    # Convert BGR → RGB
+    annotated = annotated[:, :, ::-1]
+    
+    return Image.fromarray(annotated)
+
+# ---- Gradio Interface ----
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload Food Image"),
+    outputs=gr.Image(type="pil", label="Detection Result"),
+    title="🍔 Junk Food Detection (YOLO)",
+    description="Upload an image to detect junk food items like Pizza, Burger, Ice Cream, Fries, etc.",
+    allow_flagging="never"
+)
+
+if __name__ == "__main__":
+    iface.launch()