Create app.py

app.py

@@ -0,0 +1,54 @@
+import torch
+import gradio as gr
+from PIL import Image
+import numpy as np
+from torchvision import transforms
+
+from model import FoodIngredientClassifier
+from utils import load_model_and_mlb
+
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load model + label binarizer
+model, mlb, threshold = load_model_and_mlb("model.pth", DEVICE)
+model.eval()
+
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406],
+                         [0.229, 0.224, 0.225])
+])
+
+def predict(image):
+    image = image.convert("RGB")
+    img_tensor = transform(image).unsqueeze(0).to(DEVICE)
+
+    with torch.no_grad():
+        output = model(img_tensor)
+        probs = torch.sigmoid(output).cpu().numpy()[0]
+
+    pred_indices = np.where(probs > threshold)[0]
+    ingredients = mlb.classes_[pred_indices]
+    confidences = probs[pred_indices]
+
+    results = sorted(
+        [(ing, float(conf)) for ing, conf in zip(ingredients, confidences)],
+        key=lambda x: x[1],
+        reverse=True
+    )
+
+    if not results:
+        return {"No ingredient detected": 1.0}
+
+    return {k: v for k, v in results}
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=10),
+    title="Food Ingredient Classifier",
+    description="Upload a food image to detect ingredients."
+)
+
+iface.launch()