Refactor to use CSV files for data management

app.py

@@ -1,89 +1,22 @@
 import gradio as gr
 import pandas as pd
+import os
 
-CLASSIFICATION_DATA = {
-    "Model": [
-        "ResNet-50", "ViT-Base", "Swin-T", "InceptionV3", "SqueezeNet",
-        "ResNet-50", "ViT-Base", "Swin-T", "InceptionV3", "SqueezeNet",
-        "ResNet-50", "ViT-Base", "Swin-T", "InceptionV3", "SqueezeNet",
-    ],
-    "Organization": [
-        "Microsoft", "Google", "Microsoft", "Google", "DeepMind",
-        "Microsoft", "Google", "Microsoft", "Google", "DeepMind",
-        "Microsoft", "Google", "Microsoft", "Google", "DeepMind",
-    ],
-    "Dataset": [
-        "DoMars16", "DoMars16", "DoMars16", "DoMars16", "DoMars16",
-        "Atmospheric Dust", "Atmospheric Dust", "Atmospheric Dust", "Atmospheric Dust", "Atmospheric Dust",
-        "Martian Frost", "Martian Frost", "Martian Frost", "Martian Frost", "Martian Frost",
-    ],
-    "Accuracy": [
-        92.5, 94.2, 95.8, 93.1, 89.7,
-        88.3, 90.1, 91.5, 89.8, 87.2,
-        85.6, 87.9, 88.4, 86.7, 84.3,
-    ],
-    "F1-Score": [
-        91.8, 93.5, 94.9, 92.4, 88.6,
-        87.5, 89.2, 90.7, 88.9, 86.3,
-        84.8, 86.9, 87.5, 85.8, 83.4,
-    ],
-}
+# Load data from CSV files
+DATA_DIR = "data"
 
-DETECTION_DATA = {
-    "Model": [
-        "Faster R-CNN", "YOLOv5", "DETR", "RetinaNet", "SSD",
-        "Faster R-CNN", "YOLOv5", "DETR", "RetinaNet", "SSD",
-        "Faster R-CNN", "YOLOv5", "DETR", "RetinaNet", "SSD",
-    ],
-    "Organization": [
-        "Meta", "Ultralytics", "Meta", "Meta", "Google",
-        "Meta", "Ultralytics", "Meta", "Meta", "Google",
-        "Meta", "Ultralytics", "Meta", "Meta", "Google",
-    ],
-    "Dataset": [
-        "Mars Crater", "Mars Crater", "Mars Crater", "Mars Crater", "Mars Crater",
-        "Rover Component", "Rover Component", "Rover Component", "Rover Component", "Rover Component",
-        "Geological Feature", "Geological Feature", "Geological Feature", "Geological Feature", "Geological Feature",
-    ],
-    "mAP": [
-        78.5, 80.2, 82.1, 79.3, 77.8,
-        75.6, 77.3, 78.9, 76.7, 75.1,
-        73.4, 75.1, 76.7, 74.5, 73.0,
-    ],
-    "IoU": [
-        0.72, 0.74, 0.76, 0.73, 0.71,
-        0.69, 0.71, 0.73, 0.70, 0.68,
-        0.67, 0.69, 0.71, 0.68, 0.67,
-    ],
-}
+def load_csv_data(filename):
+    """Load data from CSV file"""
+    filepath = os.path.join(DATA_DIR, filename)
+    if os.path.exists(filepath):
+        return pd.read_csv(filepath)
+    else:
+        return pd.DataFrame()
 
-SEGMENTATION_DATA = {
-    "Model": [
-        "U-Net", "DeepLabV3+", "Mask R-CNN", "SegFormer", "HRNet",
-        "U-Net", "DeepLabV3+", "Mask R-CNN", "SegFormer", "HRNet",
-        "U-Net", "DeepLabV3+", "Mask R-CNN", "SegFormer", "HRNet",
-    ],
-    "Organization": [
-        "OpenAI", "Google", "Meta", "NVIDIA", "Microsoft",
-        "OpenAI", "Google", "Meta", "NVIDIA", "Microsoft",
-        "OpenAI", "Google", "Meta", "NVIDIA", "Microsoft",
-    ],
-    "Dataset": [
-        "Mars Terrain", "Mars Terrain", "Mars Terrain", "Mars Terrain", "Mars Terrain",
-        "Dust Storm", "Dust Storm", "Dust Storm", "Dust Storm", "Dust Storm",
-        "Geological Feature", "Geological Feature", "Geological Feature", "Geological Feature", "Geological Feature",
-    ],
-    "Dice Score": [
-        0.85, 0.87, 0.88, 0.86, 0.84,
-        0.82, 0.84, 0.85, 0.83, 0.82,
-        0.81, 0.83, 0.84, 0.82, 0.81,
-    ],
-    "IoU": [
-        0.74, 0.76, 0.78, 0.75, 0.73,
-        0.70, 0.72, 0.74, 0.71, 0.70,
-        0.68, 0.70, 0.72, 0.69, 0.68,
-    ],
-}
+# Load datasets
+CLASSIFICATION_DF = load_csv_data("classification.csv")
+DETECTION_DF = load_csv_data("detection.csv")
+SEGMENTATION_DF = load_csv_data("segmentation.csv")
 
 
 def filter_and_search(df, search, datasets, models, organizations, columns):
@@ -107,8 +40,11 @@ def filter_and_search(df, search, datasets, models, organizations, columns):
     return filtered
 
 
-def build_tab(data, name):
-    df = pd.DataFrame(data)
+def build_tab(df, name):
+    """Build a leaderboard tab from a DataFrame"""
+    if df.empty:
+        return gr.TabItem(name)
+
     datasets = sorted(df["Dataset"].unique().tolist())
     models = sorted(df["Model"].unique().tolist())
     organizations = sorted(df["Organization"].unique().tolist())
@@ -240,9 +176,9 @@ with demo:
     gr.Markdown(INTRO, elem_classes="markdown-text")
 
     with gr.Tabs(elem_classes="tab-buttons"):
-        build_tab(CLASSIFICATION_DATA, "🏅 Classification")
-        build_tab(SEGMENTATION_DATA, "🏅 Segmentation")
-        build_tab(DETECTION_DATA, "🏅 Object Detection")
+        build_tab(CLASSIFICATION_DF, "🏅 Classification")
+        build_tab(SEGMENTATION_DF, "🏅 Segmentation")
+        build_tab(DETECTION_DF, "🏅 Object Detection")
 
         with gr.TabItem("📝 Submit", elem_id="submit-tab"):
             gr.Markdown("""

data/classification.csv

@@ -0,0 +1,16 @@
+Model,Organization,Dataset,Accuracy,F1-Score
+ResNet-50,Microsoft,DoMars16,92.5,91.8
+ViT-Base,Google,DoMars16,94.2,93.5
+Swin-T,Microsoft,DoMars16,95.8,94.9
+InceptionV3,Google,DoMars16,93.1,92.4
+SqueezeNet,DeepMind,DoMars16,89.7,88.6
+ResNet-50,Microsoft,Atmospheric Dust,88.3,87.5
+ViT-Base,Google,Atmospheric Dust,90.1,89.2
+Swin-T,Microsoft,Atmospheric Dust,91.5,90.7
+InceptionV3,Google,Atmospheric Dust,89.8,88.9
+SqueezeNet,DeepMind,Atmospheric Dust,87.2,86.3
+ResNet-50,Microsoft,Martian Frost,85.6,84.8
+ViT-Base,Google,Martian Frost,87.9,86.9
+Swin-T,Microsoft,Martian Frost,88.4,87.5
+InceptionV3,Google,Martian Frost,86.7,85.8
+SqueezeNet,DeepMind,Martian Frost,84.3,83.4

data/detection.csv

@@ -0,0 +1,16 @@
+Model,Organization,Dataset,mAP,IoU
+Faster R-CNN,Meta,Mars Crater,78.5,0.72
+YOLOv5,Ultralytics,Mars Crater,80.2,0.74
+DETR,Meta,Mars Crater,82.1,0.76
+RetinaNet,Meta,Mars Crater,79.3,0.73
+SSD,Google,Mars Crater,77.8,0.71
+Faster R-CNN,Meta,Rover Component,75.6,0.69
+YOLOv5,Ultralytics,Rover Component,77.3,0.71
+DETR,Meta,Rover Component,78.9,0.73
+RetinaNet,Meta,Rover Component,76.7,0.70
+SSD,Google,Rover Component,75.1,0.68
+Faster R-CNN,Meta,Geological Feature,73.4,0.67
+YOLOv5,Ultralytics,Geological Feature,75.1,0.69
+DETR,Meta,Geological Feature,76.7,0.71
+RetinaNet,Meta,Geological Feature,74.5,0.68
+SSD,Google,Geological Feature,73.0,0.67

data/segmentation.csv

@@ -0,0 +1,16 @@
+Model,Organization,Dataset,Dice Score,IoU
+U-Net,OpenAI,Mars Terrain,0.85,0.74
+DeepLabV3+,Google,Mars Terrain,0.87,0.76
+Mask R-CNN,Meta,Mars Terrain,0.88,0.78
+SegFormer,NVIDIA,Mars Terrain,0.86,0.75
+HRNet,Microsoft,Mars Terrain,0.84,0.73
+U-Net,OpenAI,Dust Storm,0.82,0.70
+DeepLabV3+,Google,Dust Storm,0.84,0.72
+Mask R-CNN,Meta,Dust Storm,0.85,0.74
+SegFormer,NVIDIA,Dust Storm,0.83,0.71
+HRNet,Microsoft,Dust Storm,0.82,0.70
+U-Net,OpenAI,Geological Feature,0.81,0.68
+DeepLabV3+,Google,Geological Feature,0.83,0.70
+Mask R-CNN,Meta,Geological Feature,0.84,0.72
+SegFormer,NVIDIA,Geological Feature,0.82,0.69
+HRNet,Microsoft,Geological Feature,0.81,0.68