evaluate.py

from datasets import load_dataset
from transformers import pipeline
from sklearn.metrics import (
    accuracy_score, precision_score, recall_score, f1_score,
    confusion_matrix, ConfusionMatrixDisplay
)
import matplotlib.pyplot as plt

# Label mapping from the dataset
LABEL_NAMES = ["Mild_Demented", "Moderate_Demented", "Non_Demented", "Very_Mild_Demented"]

# Load the test split
print("Loading dataset...")
dataset = load_dataset("Falah/Alzheimer_MRI", split="test")

# Load your model
print("Loading model...")
clf = pipeline("image-classification", model="Thamer/resnet-fine_tuned")

# Run inference on every test image
y_true, y_pred = [], []
for i, example in enumerate(dataset):
    img = example["image"].convert("RGB")
    preds = clf(img)
    top = max(preds, key=lambda x: x["score"])["label"]
    true = LABEL_NAMES[example["label"]]
    y_true.append(true)
    y_pred.append(top)
    if i % 100 == 0:
        print(f"  {i}/{len(dataset)} done...")

# Print metrics
print("\n--- Results ---")
print(f"Accuracy:  {accuracy_score(y_true, y_pred):.2%}")
print(f"Precision: {precision_score(y_true, y_pred, average='macro', zero_division=0):.2%}")
print(f"Recall:    {recall_score(y_true, y_pred, average='macro', zero_division=0):.2%}")
print(f"F1:        {f1_score(y_true, y_pred, average='macro', zero_division=0):.2%}")

# Save confusion matrix
cm = confusion_matrix(y_true, y_pred, labels=LABEL_NAMES)
disp = ConfusionMatrixDisplay(cm, display_labels=LABEL_NAMES)
fig, ax = plt.subplots(figsize=(7, 7))
disp.plot(ax=ax, cmap="Blues", colorbar=False, values_format="d")
plt.title("Confusion Matrix — ResNet50")
plt.xticks(rotation=45, ha="right")
plt.tight_layout()
plt.savefig("confusion_matrix.png", dpi=200)
print("\nSaved confusion_matrix.png")