model.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import pickle
from torchvision import transforms

import numpy as np
from PIL import Image


class FaceClassifier(nn.Module):
    def __init__(self, num_classes):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
        self.conv3 = nn.Conv2d(64, 128, 3, padding=1)
        self.pool  = nn.MaxPool2d(2, 2)
        self.dropout = nn.Dropout(0.1)
        self.fc1 = nn.Linear(128 * 16 * 16, 512)
        self.fc2 = nn.Linear(512, num_classes)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = self.pool(F.relu(self.conv3(x)))
        x = x.view(-1, 128 * 16 * 16)
        x = self.dropout(F.relu(self.fc1(x)))
        return self.fc2(x)


class EmotionPredictor:
    def __init__(self):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        with open("classes.pkl", "wb") as f:
            self.classes = pickle.load(f)

        self.model = FaceClassifier(len(self.classes))
        self.model.load_state_dict(
            torch.load("face_classifier.pth", map_location=self.device, weights_only=True)
        )
        self.model.to(self.device).eval()

        self.transform = transforms.Compose([
            transforms.Resize((128, 128)),
            transforms.ToTensor(),
            transforms.Normalize((0.5,), (0.5,)),
        ])

    @torch.inference_mode()
    def predict(self, image_np: np.ndarray) -> str:
        """Return the top predicted emotion label."""
        img    = Image.fromarray(image_np)
        tensor = self.transform(img).unsqueeze(0).to(self.device)
        output = self.model(tensor)
        return self.classes[output.argmax(1).item()]

    @torch.inference_mode()
    def predict_with_confidence(self, image_np: np.ndarray) -> tuple[str, dict]:
        """Return (top_label, {label: confidence_float}) using softmax probabilities."""
        img    = Image.fromarray(image_np)
        tensor = self.transform(img).unsqueeze(0).to(self.device)
        logits = self.model(tensor)
        probs  = F.softmax(logits, dim=1).squeeze(0).cpu().tolist()
        scores = {cls: round(p, 4) for cls, p in zip(self.classes, probs)}
        top    = max(scores, key=scores.get)
        return top, scores