Update app.py

app.py

@@ -5,12 +5,19 @@ from PIL import Image
 import os
 import time
 import cloudpickle
+import traceback
 
+# =========================
+# Configuration
+# =========================
 IMAGE_SIZE = (64, 64)
 LEADERBOARD_PATH = "leaderboard.csv"
 LABEL_FILE = "labels.csv"
 TEST_DIR = "test_images"
 
+# =========================
+# Helpers
+# =========================
 def image_to_features(image: Image.Image) -> np.ndarray:
     image = image.resize(IMAGE_SIZE)
     return np.array(image.convert("L")).flatten()
@@ -34,6 +41,9 @@ def load_test_data():
 if not os.path.exists(LEADERBOARD_PATH):
     pd.DataFrame(columns=["Name", "Accuracy", "Avg Time (ms)"]).to_csv(LEADERBOARD_PATH, index=False)
 
+# =========================
+# Evaluation
+# =========================
 def evaluate_model(file, name):
     try:
         with open(file.name, "rb") as f:
@@ -45,32 +55,55 @@ 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."
+            return "❌ Model output length does not match test set.", None
 
         accuracy = 100.0 * (y_pred == y_test).mean()
         avg_time = elapsed / len(X_test)
 
         leaderboard = pd.read_csv(LEADERBOARD_PATH)
-        leaderboard = pd.concat([leaderboard, pd.DataFrame([{
+        new_entry = pd.DataFrame([{
             "Name": name,
             "Accuracy": round(accuracy, 2),
             "Avg Time (ms)": round(avg_time, 2)
-        }])])
+        }])
+        leaderboard = pd.concat([leaderboard, new_entry], ignore_index=True)
         leaderboard.to_csv(LEADERBOARD_PATH, index=False)
 
-        return leaderboard
+        return "", leaderboard
 
     except Exception as e:
-        import traceback
-        return f"❌ Error:\n{traceback.format_exc()}"
-
-gr.Interface(
-    fn=evaluate_model,
-    inputs=[
-        gr.File(label="Upload your `classifier-joblib.joblib` file"),
-        gr.Text(label="Your name or team")
-    ],
-    outputs=gr.Dataframe(label="Leaderboard"),
-    title="🧠 Olive Fly Classifier Leaderboard",
-    description="Upload your `classifier-joblib.joblib` model trained on 64×64 grayscale images. We'll evaluate it and update the leaderboard."
-).launch()
+        return f"❌ Error:\n{traceback.format_exc()}", None
+
+# =========================
+# UI with Gradio Blocks
+# =========================
+with gr.Blocks(title="Olive Fly Classifier Leaderboard") as demo:
+    gr.Markdown("## 🧠 Olive Fly Classifier Leaderboard", elem_id="title")
+    gr.Markdown(
+        "Upload your `classifier-joblib.joblib` model trained on 64×64 grayscale images. "
+        "We'll evaluate it and update the leaderboard."
+    )
+
+    with gr.Row(variant="default"):
+        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)
+
+    with gr.Row():
+        leaderboard_table = gr.Dataframe(
+            label="Leaderboard",
+            headers=["Name", "Accuracy", "Avg Time (ms)"],
+            interactive=False
+        )
+
+    # Define button logic
+    submit_btn.click(
+        fn=evaluate_model,
+        inputs=[file_input, name_input],
+        outputs=[error_box, leaderboard_table]
+    )
+
+demo.launch()