Create interface.py

interface.py

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+from PIL import Image
+import torch
+from torchvision import transforms
+from model import load_model
+
+# 🔥 Load ALL models
+model1 = load_model("m1.safetensors")
+model2 = load_model("m2.safetensors")
+model3 = load_model("m3.safetensors")
+
+models = [model1, model2, model3]
+
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor()
+])
+
+def predict(image):
+    img = Image.open(image).convert("RGB")
+    x = transform(img).unsqueeze(0)
+
+    probs = []
+
+    with torch.no_grad():
+        for model in models:
+            out = model(x)
+            prob = torch.sigmoid(out).item()
+            probs.append(prob)
+
+    # 🔥 Ensemble (average)
+    final_prob = sum(probs) / len(probs)
+
+    return final_prob