Create app.py

kitchen_model/app.py

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+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+import gradio as gr
+
+# -----------------------
+# Device
+# -----------------------
+
+device = torch.device("cpu")
+
+# -----------------------
+# Load Model
+# -----------------------
+
+MODEL_PATH = "kitchen_model"
+
+model = torch.load(
+    MODEL_PATH,
+    map_location=device
+)
+
+model.eval()
+
+# -----------------------
+# Image Transform
+# -----------------------
+
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+])
+
+# -----------------------
+# Prediction Function
+# -----------------------
+
+def predict(image):
+
+    image = transform(image).unsqueeze(0)
+
+    with torch.no_grad():
+
+        outputs = model(image)
+
+        # Binary classification
+        probability = torch.sigmoid(outputs).item()
+
+        if probability > 0.5:
+            label = "Clean Kitchen"
+        else:
+            label = "Unclean Kitchen"
+
+        confidence = round(probability * 100, 2)
+
+    return {
+        "Prediction": label,
+        "Confidence (%)": confidence
+    }
+
+# -----------------------
+# Gradio UI
+# -----------------------
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs="json",
+    title="Kitchen Hygiene Detection",
+    description="Upload a kitchen image to check cleanliness."
+)
+
+interface.launch()