Update app.py

app.py

@@ -25,7 +25,6 @@ def image_to_features(image: Image.Image) -> np.ndarray:
 def load_test_data():
     if not os.path.exists(LABEL_FILE) or not os.path.exists(TEST_DIR):
         raise FileNotFoundError("Missing labels.csv or test_images/ folder.")
-
     df = pd.read_csv(LABEL_FILE)
     X_test, y_test = [], []
     for _, row in df.iterrows():
@@ -38,11 +37,16 @@ def load_test_data():
             print(f"❌ Error loading {img_path}: {e}")
     return np.array(X_test), np.array(y_test)
 
-if not os.path.exists(LEADERBOARD_PATH):
-    pd.DataFrame(columns=["Name", "Accuracy", "Avg Time (ms)"]).to_csv(LEADERBOARD_PATH, index=False)
+def load_leaderboard():
+    if os.path.exists(LEADERBOARD_PATH):
+        return pd.read_csv(LEADERBOARD_PATH)
+    else:
+        df = pd.DataFrame(columns=["Name", "Accuracy", "Avg Time (ms)"])
+        df.to_csv(LEADERBOARD_PATH, index=False)
+        return df
 
 # =========================
-# Evaluation
+# Evaluation Logic
 # =========================
 def evaluate_model(file, name):
     try:
@@ -55,12 +59,12 @@ def evaluate_model(file, name):
         elapsed = (time.time() - start) * 1000
 
         if len(y_pred) != len(y_test):
-            return "❌ Model output length does not match test set.", None
+            return "❌ Model output length does not match test set.", load_leaderboard()
 
         accuracy = 100.0 * (y_pred == y_test).mean()
         avg_time = elapsed / len(X_test)
 
-        leaderboard = pd.read_csv(LEADERBOARD_PATH)
+        leaderboard = load_leaderboard()
         new_entry = pd.DataFrame([{
             "Name": name,
             "Accuracy": round(accuracy, 2),
@@ -72,34 +76,34 @@ def evaluate_model(file, name):
         return "", leaderboard
 
     except Exception as e:
-        return f"❌ Error:\n{traceback.format_exc()}", None
+        return f"❌ Error:\n{traceback.format_exc()}", load_leaderboard()
 
 # =========================
-# UI with Gradio Blocks
+# UI
 # =========================
 with gr.Blocks(title="Olive Fly Classifier Leaderboard") as demo:
-    gr.Markdown("# Olive Fly Classifier Leaderboard", elem_id="title")
+    gr.Markdown("## Olive Fly Classifier Leaderboard")
     gr.Markdown(
-        "Upload your `.cloudpkl` model trained on olive fly images. "
+        "Upload your `.cloudpkl` model trained on Olive Fly images. "
         "We'll evaluate it and update the leaderboard."
     )
 
-    with gr.Row(variant="default"):
+    with gr.Row():
         with gr.Column(scale=1):
             file_input = gr.File(label="Upload your model")
             name_input = gr.Text(label="Your name or team")
             submit_btn = gr.Button("Submit model")
 
-    error_box = gr.Textbox(label="Output log", visible=False)
+    error_box = gr.Textbox(label="Output log", visible=True)
 
-    with gr.Row():
-        leaderboard_table = gr.Dataframe(
-            label="Leaderboard",
-            headers=["Name", "Accuracy", "Avg Time (ms)"],
-            interactive=False
-        )
+    leaderboard_table = gr.Dataframe(
+        value=load_leaderboard(),
+        label="Leaderboard",
+        interactive=False,
+        wrap=True
+    )
 
-    # Define button logic
+    # Always update leaderboard after submission
     submit_btn.click(
         fn=evaluate_model,
         inputs=[file_input, name_input],