# Import required libraries and modules
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import models, transforms
from dataloader import DataLoader  # Custom data loader for loading electronic image data of teacher teaching process

# Define the Teacher Intelligence Model class
class TeacherIntelligenceModel(nn.Module):
    def __init__(self, num_classes):
        super(TeacherIntelligenceModel, self).__init__()
        # Use a pretrained SOLO classification model as the feature extractor
        self.solo_model = models.solo_pretrained_model()  # Pretrained SOLO classification model

        # Replace the last classifier layer of the SOLO model for teacher intelligence classification
        in_features = self.solo_model.fc.in_features
        self.solo_model.fc = nn.Linear(in_features, num_classes)

    def forward(self, inputs):
        # SOLO feature extraction process
        features = self.solo_model(inputs)

        # Teacher intelligence classification process
        predictions = F.softmax(features, dim=1)

        return predictions

# Create an instance of the Teacher Intelligence Model
num_classes = 5  # Assume there are 5 different intelligence categories
model = TeacherIntelligenceModel(num_classes)

# Define the loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# Load electronic image data of teacher teaching process
data_loader = DataLoader(dataset_path, batch_size=batch_size, shuffle=True)

# Training process
model.train()
for epoch in range(num_epochs):
    for batch_images, batch_labels in data_loader:
        # Load data to GPU (if available)
        batch_images = batch_images.to(device)
        batch_labels = batch_labels.to(device)

        # Forward pass
        predictions = model(batch_images)

        # Calculate the loss
        loss = criterion(predictions, batch_labels)

        # Backpropagation and parameter update
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # Output training information
        print(f"Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}")

# Evaluate the model on the test set
model.eval()
with torch.no_grad():
    for batch_images, batch_labels in test_data_loader:
        # Load data to GPU (if available)
        batch_images = batch_images.to(device)
        batch_labels = batch_labels.to(device)

        # Forward pass
        predictions = model(batch_images)

        # Calculate accuracy and other evaluation metrics