updated tabs

app.py

@@ -1,35 +1,84 @@
-# my_table_app_gradio.py
-
 import gradio as gr
 import pandas as pd
+import json
+import os
+
+# Given list of tasks for clustering and pair classification
+TASKS_CLUSTERING = [
+    "ArxivClusteringP2P",
+    "ArxivClusteringS2S",
+    "BiorxivClusteringP2P",
+    "BiorxivClusteringS2S",
+    "MedrxivClusteringP2P",
+    "MedrxivClusteringS2S",
+    "RedditClustering",
+    "RedditClusteringP2P",
+    "StackExchangeClustering",
+    "StackExchangeClusteringP2P",
+    "TwentyNewsgroupsClustering",
+]
+
+TASKS_PAIR_CLASSIFICATION = [
+    "SprintDuplicateQuestions",
+    "TwitterSemEval2015",
+    "TwitterURLCorpus",
+]
 
 def display_table():
     # Create a sample dataframe
     data = {
         "Model": ["ModelA", "ModelB", "ModelC"],
+        "Model Size (MB)": [293, 793, 1000],
         "Score": [0.92, 0.85, 0.89],
         "Quantized Score": [0.91, 0.84, 0.88]
     }
     df = pd.DataFrame(data)
 
-	df.index.name = "Rank"
-    # Convert the DataFrame to an HTML table string
-    html_table = df.to_html(classes="wide_table")
-
+    df.index.name = "Rank"
+    html_table = df.to_html()
 
-	html_content = f"""
+    html_content = f"""
     <style>
-        .wide_table {
+        .wide_table {{
             width: 100%;
-        }
+        }}
     </style>
     {html_table}
     """
 
-    return html_table
+    return html_content
+
+
+def compute_model_score(model_name):
+    results_dir = "results"
+    model_dir = os.path.join(results_dir, model_name)
+
+    scores = []
+
+    # Get scores for clustering tasks
+    for task in TASKS_CLUSTERING:
+        task_file = os.path.join(model_dir, f"{task}.json")
+        with open(task_file, 'r') as f:
+            data = json.load(f)
+            v_measure = data['test']['v_measure']
+            scores.append(v_measure)
+
+    # Get scores for pair classification tasks
+    for task in TASKS_PAIR_CLASSIFICATION:
+        task_file = os.path.join(model_dir, f"{task}.json")
+        with open(task_file, 'r') as f:
+            data = json.load(f)
+            max_ap = data['test']['max']['ap']
+            scores.append(max_ap)
+
+    # Compute average score
+    average_score = sum(scores) / len(scores)
+    return average_score
+
+
+# score = compute_model_score("ModelA")
 
 # Create Gradio interface
 iface = gr.Interface(fn=display_table, live=True, inputs=[], outputs="html")
 
 iface.launch()
-