has data and is sortable, but does not have the model names as a row

mmlu_data_to_example.py

@@ -1,43 +1,62 @@
 import gradio as gr
 import requests
 import pandas as pd
-from huggingface_hub.hf_api import SpaceInfo
-
-class BlocksSpace:
-    def __init__(self, url, name):
-        self.url = url
-        self.name = name
-
-    def fetch_spaces(self):
-        r = requests.get(self.url)
-        d = r.json()
-        spaces = [SpaceInfo(**x) for x in d]
-        blocks_spaces = {}
-        for i in range(0,len(spaces)):
-            if spaces[i].id.split('/')[0] == self.name and hasattr(spaces[i], 'likes') and spaces[i].id != f'{self.name}/Leaderboard' and spaces[i].id != f'{self.name}/README':
-                blocks_spaces[spaces[i].id]=spaces[i].likes
-        return blocks_spaces
-
-    def get_spaces_dataframe(self):
-        blocks_spaces = self.fetch_spaces()
-        df = pd.DataFrame(
-            [{"Spaces_Name": Spaces, "likes": likes} for Spaces,likes in blocks_spaces.items()])
-        df = df.sort_values(by=['likes'],ascending=False)
-        return df
+
+class MultiURLData:
+    def __init__(self, file_path):
+        self.file_path = file_path
+
+    def fetch_data(self):
+        # Read URLs from a file, one per line
+        with open(self.file_path, 'r') as f:
+            file_urls = [line.strip() for line in f.readlines()]
+
+        dataframes = []
+        for url in file_urls:
+            # Derive column names from the URLs
+            column_name = url.split('/')[-1].split('_')[0]
+
+            # Load data from URL
+            response = requests.get(url)
+            data = response.json()
+
+            # Convert data into a DataFrame
+            df = pd.DataFrame(data['results']).T
+
+            # Rename 'acc' column to respective file names
+            df = df.rename(columns={'acc': column_name})
+
+            # Remove 'hendrycksTest-' from the index
+            df.index = df.index.str.replace('hendrycksTest-', '')
+
+            dataframes.append(df[[column_name]])  # keep only the column of interest
+
+        # Merge the dataframes
+        # Merge the dataframes on index
+        data = pd.concat(dataframes, axis=1)
+
+        # Transpose the dataframe to swap rows and columns
+        data = data.transpose()
+
+        # Select only columns 'moral_scenarios' and 'moral_disputes'
+        data = data[['moral_scenarios', 'moral_disputes']]
+
+        return data
+
+data_provider = MultiURLData("file_urls.txt")
 
 block = gr.Blocks()
-my_blocks_space = BlocksSpace("https://huggingface.co/api/spaces", "Gradio-Blocks")
 
 with block:    
-    gr.Markdown("""Leaderboard for the most popular Blocks Event Spaces. To learn more and join, see Blocks Party Event""")
+    gr.Markdown("""Leaderboard""")
     with gr.Tabs():
-        with gr.TabItem("Blocks Party Leaderboard"):
+        with gr.TabItem("Leaderboard"):
             with gr.Row():
                 data = gr.outputs.Dataframe(type="pandas")
             with gr.Row():
                 data_run = gr.Button("Refresh")
-                data_run.click(my_blocks_space.get_spaces_dataframe, inputs=None, outputs=data)
+                data_run.click(data_provider.fetch_data, inputs=None, outputs=data)
     # running the function on page load in addition to when the button is clicked
-    block.load(my_blocks_space.get_spaces_dataframe, inputs=None, outputs=data)               
+    block.load(data_provider.fetch_data, inputs=None, outputs=data)               
 
 block.launch()