Update app.py

app.py

@@ -6,39 +6,43 @@ import os
 import time
 import joblib
 
-# Load test data
-label_df = pd.read_csv("labels.csv")
-test_dir = "test_images"
+LEADERBOARD_PATH = "leaderboard.csv"
+IMAGE_SIZE = (64, 64)
+TEST_DIR = "test_images"
+LABEL_FILE = "labels.csv"
 
 def image_to_features(image: Image.Image) -> np.ndarray:
-    image = image.resize((64, 64))
+    image = image.resize(IMAGE_SIZE)
     return np.array(image.convert("L")).flatten()
 
-X_test = []
-y_test = []
+def load_test_data():
+    if not os.path.exists(LABEL_FILE) or not os.path.exists(TEST_DIR):
+        raise FileNotFoundError("Missing test data or label file.")
+    
+    label_df = pd.read_csv(LABEL_FILE)
+    X_test = []
+    y_test = []
 
-for _, row in label_df.iterrows():
-    img_path = os.path.join(test_dir, row["filename"])
-    label = row["label"]
-    try:
-        img = Image.open(img_path)
-        X_test.append(image_to_features(img))
-        y_test.append(label)
-    except Exception as e:
-        print(f"Failed to load {img_path}: {e}")
+    for _, row in label_df.iterrows():
+        img_path = os.path.join(TEST_DIR, row["filename"])
+        label = row["label"]
+        try:
+            img = Image.open(img_path)
+            X_test.append(image_to_features(img))
+            y_test.append(label)
+        except Exception as e:
+            print(f"Failed to load {img_path}: {e}")
 
-X_test = np.array(X_test)
-y_test = np.array(y_test)
+    return np.array(X_test), np.array(y_test)
 
-# Initialize leaderboard
-LEADERBOARD_PATH = "leaderboard.csv"
+# Create leaderboard file if missing
 if not os.path.exists(LEADERBOARD_PATH):
     pd.DataFrame(columns=["Name", "Accuracy", "Avg Time (ms)"]).to_csv(LEADERBOARD_PATH, index=False)
 
-# Evaluation function
 def evaluate_model(file, name):
     try:
         model = joblib.load(file.name)
+        X_test, y_test = load_test_data()
 
         start = time.time()
         y_pred = model.predict(X_test)
@@ -61,7 +65,6 @@ def evaluate_model(file, name):
     except Exception as e:
         return f"❌ Error during evaluation: {e}"
 
-# Gradio interface
 gr.Interface(
     fn=evaluate_model,
     inputs=[