added evaluations

data/models/expression_predictor_cnn.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:dbe74499e1659461a187bd820854520513fb94b2e9c094da7c2adf2915038d32
+oid sha256:8f5740fcfd8235c0acf7242a591c0991391a8cea8962d179e7e9680c02cd4c45
 size 1576685

evaluation.py

@@ -0,0 +1,67 @@
+import torch
+from torchvision import transforms
+from torchvision.datasets import ImageFolder
+from torch.utils.data import DataLoader
+from sklearn.metrics import classification_report, confusion_matrix
+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+from utils.model_loader import load_model
+
+# ----- CONFIG -----
+MODEL_PATH = 'data/models/expression_predictor_cnn.pth'
+TEST_DIR = 'data/validation'
+BATCH_SIZE = 32
+CLASSES = ['Angry', 'Disgust', 'Scared', 'Happy', 'Neutral', 'Sad', 'Surprised']
+
+# Load model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = load_model(MODEL_PATH, device)
+
+# Transforms
+transform = transforms.Compose([
+    transforms.Grayscale(),
+    transforms.Resize((48, 48)),
+    transforms.ToTensor(),
+    transforms.Normalize((0.5,), (0.5,))
+])
+
+# Load test data
+test_data = ImageFolder(TEST_DIR, transform=transform)
+test_loader = DataLoader(test_data, batch_size=BATCH_SIZE, shuffle=False)
+
+# Predict
+all_preds = []
+all_labels = []
+with torch.no_grad():
+    for images, labels in test_loader:
+        images = images.to(device)
+        outputs = model(images)
+        _, preds = torch.max(outputs, 1)
+        all_preds.extend(preds.cpu().numpy())
+        all_labels.extend(labels.numpy())
+
+# Classification report
+report = classification_report(all_labels, all_preds, target_names=CLASSES, output_dict=True)
+report_df = pd.DataFrame(report).transpose()
+print("\nClassification Report:")
+print(report_df)
+
+# Confusion Matrix
+cm = confusion_matrix(all_labels, all_preds)
+plt.figure(figsize=(10, 8))
+sns.heatmap(cm, annot=True, fmt='d', xticklabels=CLASSES, yticklabels=CLASSES, cmap="Blues")
+plt.xlabel("Predicted")
+plt.ylabel("True")
+plt.title("Confusion Matrix")
+plt.tight_layout()
+plt.savefig("confusion_matrix.png")
+print("\n✅ Saved confusion matrix as 'confusion_matrix.png'")
+
+# Save classification report heatmap (Precision, Recall, F1, Support)
+plt.figure(figsize=(12, 6))
+sns.heatmap(report_df.iloc[:-1, :-1], annot=True, fmt=".2f", cmap="YlGnBu")
+plt.title("Classification Report (Precision, Recall, F1-score, Support)")
+plt.tight_layout()
+plt.savefig("classification_report_matrix.png")
+print("✅ Saved classification_report_matrix.png")

src/streamlit_app.py

@@ -13,6 +13,7 @@ from utils.model_loader import load_model
 
 
 st.title("Facial Expression Recognition")
+st.caption("dataset: https://www.kaggle.com/datasets/jonathanoheix/face-expression-recognition-dataset")
 
 # Load model
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

utils/train.py

@@ -8,7 +8,7 @@ from PIL import ImageFile
 
 # Configuration
 BATCH_SIZE = 64
-EPOCHS = 10
+EPOCHS = 100
 IMG_SIZE = 48
 MODEL_PATH = "data/models/expression_predictor_cnn.pth"
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")