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app.py

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+import gradio as gr
+import os
+import pandas as pd
+import numpy as np
+from PIL import Image
+import time
+
+# Load test data
+label_df = pd.read_csv("labels.csv")  # columns: filename,label
+
+test_dir = "test_images"
+test_data = [(os.path.join(test_dir, row["filename"]), row["label"]) for _, row in label_df.iterrows()]
+
+LEADERBOARD_PATH = "leaderboard.csv"
+if not os.path.exists(LEADERBOARD_PATH):
+    pd.DataFrame(columns=["Name", "Accuracy", "Avg Time (ms)"]).to_csv(LEADERBOARD_PATH, index=False)
+
+def evaluate_submission(file, name):
+    try:
+        local_env = {}
+        exec(file.read().decode(), local_env)
+
+        detect_olive_fly = local_env.get("detect_olive_fly")
+        if detect_olive_fly is None or not callable(detect_olive_fly):
+            return "❌ Your file must define a function: `def detect_olive_fly(image) -> bool`"
+
+        correct = 0
+        total_time = 0
+
+        for img_path, true_label in test_data:
+            image = Image.open(img_path).convert("RGB")
+
+            start = time.time()
+            pred = detect_olive_fly(image)
+            total_time += (time.time() - start) * 1000  # ms
+
+            if isinstance(pred, bool) and pred == bool(true_label):
+                correct += 1
+
+        accuracy = 100 * correct / len(test_data)
+        avg_time = total_time / len(test_data)
+
+        # Update leaderboard
+        leaderboard = pd.read_csv(LEADERBOARD_PATH)
+        leaderboard = pd.concat([leaderboard, pd.DataFrame([{
+            "Name": name,
+            "Accuracy": round(accuracy, 2),
+            "Avg Time (ms)": round(avg_time, 2)
+        }])])
+        leaderboard.to_csv(LEADERBOARD_PATH, index=False)
+
+        return leaderboard
+
+    except Exception as e:
+        return f"❌ Error during evaluation: {e}"
+
+interface = gr.Interface(
+    fn=evaluate_submission,
+    inputs=[
+        gr.File(label="Upload Python file with `detect_olive_fly(image)`"),
+        gr.Text(label="Your Name")
+    ],
+    outputs=gr.Dataframe(label="Leaderboard"),
+    title="Olive Fly Detection Leaderboard",
+    description="Upload a `.py` file that defines the function `detect_olive_fly(image) -> bool`. We will test it on a set of labeled images."
+)
+
+interface.launch()