Update app.py

app.py

@@ -201,30 +201,23 @@
 
 import gradio as gr
 import pandas as pd
-from tqdm import tqdm
-
-# Parameters
-models = ["modelA", "modelB", "modelC"]  # Replace with your actual models
-dataset = "my_dataset"  # Replace with your actual dataset name
-ROUNDS = 3  # Number of rounds
-
-# Load and concatenate data
-data = []
-for model in tqdm(models):
-    model_name = model.replace("/", "_")
-    for i in range(ROUNDS):
-        try:
-            df = pd.read_pickle(f"./results/tagged/{dataset}_{model_name}_{i}.pkl")[["Category", "Sub-Category", "model", "round", "tag"]]
-            data.append(df)
-        except Exception as e:
-            print(f"skipping {dataset}_{model_name}_{i}")
-
-raw_data = pd.concat(data)
+
+# Sample DataFrame
+data = {
+    'Model': ['Model A', 'Model B', 'Model C'],
+    'Accuracy': [0.95, 0.90, 0.85],
+    'F1 Score': [0.96, 0.89, 0.84]
+}
+df = pd.DataFrame(data)
+
+# Function to display the DataFrame
+def display_table():
+    return df
 
 # Gradio Interface
 with gr.Blocks() as demo:
-    gr.Markdown("# Aggregated Benchmark Results")
-    gr.DataFrame(value=raw_data, label="Benchmark Table", interactive=False)  # Display the DataFrame
+    gr.Markdown("# Benchmark Results")
+    gr.DataFrame(value=df, label="Benchmark Table", interactive=False)  # Display the DataFrame
 
 # Launch the Gradio app
 demo.launch()