Create inference.py

inference.py

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+import torch
+import torchvision.transforms as transforms
+from PIL import Image
+import pickle
+
+# load model
+model = pickle.load(open("resnet50_c3_lr3e-04_bs32_aug_heavy_opt_adam_dr.pkl", "rb"))
+model.eval()
+
+transform = transforms.Compose([
+    transforms.Resize((224,224)),
+    transforms.ToTensor()
+])
+
+labels = ["angry","happy","sad","fear","surprise","neutral"]
+
+
+def predict(image):
+    image = transform(image).unsqueeze(0)
+
+    with torch.no_grad():
+        output = model(image)
+        pred = torch.argmax(output,1).item()
+
+    return {"emotion": labels[pred]}