added documentation and modified existing code

ApexID (ALA) - Video Documentation - Made with Clipchamp.mp4

@@ -0,0 +1,3 @@
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+size 145131767

CCS 229 - Alternative Learning Assessment (ALA) Documentation.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 436723

src/resnet_model.py

@@ -8,6 +8,7 @@ class MonkeyResNet(nn.Module):
         
         # Load pretrained ResNet18 model from torchvision
         self.model = models.resnet18(pretrained=False)
+        
         # Replace the final fully connected layer to match number of classes
         in_features = self.model.fc.in_features
         self.model.fc = nn.Linear(in_features, num_classes)

src/streamlit_app.py

@@ -1,20 +1,5 @@
 import os
 import sys
-
-# 1. Tell Streamlit to read config from our root folder
-os.environ["STREAMLIT_CONFIG_FILE"] = os.path.abspath(
-    os.path.join(os.path.dirname(__file__), ".streamlit/config.toml")
-)
-
-# 2. Redirect all Streamlit writes to /tmp
-os.environ["HOME"] = "/tmp"
-os.environ["STREAMLIT_HOME"] = "/tmp"
-os.environ["XDG_CONFIG_HOME"] = "/tmp"
-os.environ["XDG_CACHE_HOME"] = "/tmp"
-
-# 3. Ensure imports work by adding root to path
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
-
 import streamlit as st
 import torch
 import torchvision.transforms as transforms

train.py

@@ -2,7 +2,7 @@ import torch
 import torch.nn as nn
 import torch.optim as optim
 from utils.data_loader import get_data_loaders
-from models.resnet_model import MonkeyResNet  # uses your custom ResNet model
+from models.resnet_model import MonkeyResNet
 import os
 import matplotlib.pyplot as plt
 from sklearn.utils.class_weight import compute_class_weight

utils/data_loader.py

@@ -1,5 +1,3 @@
-# utils/data_loader.py
-
 from torchvision import datasets, transforms
 from torch.utils.data import DataLoader