Update model.py

model.py

@@ -1,113 +1,60 @@
 import torch
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 import torch.nn as nn
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 import torch.nn.functional as F
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 import pickle
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 from torchvision import transforms
 
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 import numpy as np
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 from PIL import Image
 
 
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 class FaceClassifier(nn.Module):
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     def __init__(self, num_classes):
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         super().__init__()
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         self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
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         self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
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         self.conv3 = nn.Conv2d(64, 128, 3, padding=1)
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         self.pool = nn.MaxPool2d(2, 2)
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         self.dropout = nn.Dropout(0.1)
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         self.fc1 = nn.Linear(128 * 16 * 16, 512)
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         self.fc2 = nn.Linear(512, num_classes) 
 
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     def forward(self, x):
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         x = self.pool(F.relu(self.conv1(x)))
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         x = self.pool(F.relu(self.conv2(x)))
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         x = self.pool(F.relu(self.conv3(x)))
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         x = x.view(-1, 128 * 16 * 16)
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         x = self.dropout(F.relu(self.fc1(x)))
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         x = self.fc2(x)
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         return x
 
 
 
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 class EmotionPredictor:
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     def __init__(self):
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         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
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         with open("classes.pkl", "rb") as f:
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             self.classes = pickle.load(f)
 
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         self.model = FaceClassifier(len(self.classes))
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         self.model.load_state_dict(
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             torch.load("face_classifier.pth", map_location=self.device)
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         )
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         self.model.to(self.device).eval()
 
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         self.transform = transforms.Compose([
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             transforms.Resize((128, 128)),
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             transforms.ToTensor(),
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             transforms.Normalize((0.5,), (0.5,))
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         ])
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     @torch.inference_mode()
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     def predict(self, image_np: np.ndarray) -> str:
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         img = Image.fromarray(image_np)
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         tensor = self.transform(img).unsqueeze(0).to(self.device)
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         output = self.model(tensor)
+        return self.classes[output.argmax(1).item()]
+
+
 
-        return self.classes[output.argmax(1).item()]