Update app.py

app.py

@@ -2,6 +2,42 @@ import streamlit as st
 import pandas as pd
 from PIL import Image, ImageDraw, ImageFont
 import io
+import os, socket
+from huggingface_hub import hf_hub_download
+
+HF_REPO_ID = "AIEnergyScore/Leaderboard"   # this is a Space
+HF_REPO_TYPE = "space"
+HF_DATA_PREFIX = "data/energy"             # where the CSVs live in the Space
+
+def read_csv_from_hub(file_name: str) -> pd.DataFrame:
+    """
+    Download a CSV from the HF Space path data/energy/<file_name>
+    and return it as a pandas DataFrame.
+    Falls back to local file if Hub is unavailable.
+    """
+    hub_path = f"{HF_DATA_PREFIX}/{file_name}"
+    try:
+        # fast DNS sanity check to give a useful error if DNS is down
+        socket.gethostbyname("huggingface.co")
+
+        local_path = hf_hub_download(
+            repo_id=HF_REPO_ID,
+            repo_type=HF_REPO_TYPE,
+            filename=hub_path,
+            revision="main",          # adjust if you use a branch/tag
+            resume_download=True
+        )
+        return pd.read_csv(local_path)
+    except Exception as e:
+        # Fallback to local file if present
+        try:
+            return pd.read_csv(file_name)
+        except Exception:
+            # re-raise with context so it surfaces nicely in Streamlit
+            raise RuntimeError(
+                f"Unable to load '{file_name}' from Hub path '{hub_path}' or locally. "
+                f"Original error: {e}"
+            )
 
 def main():
     # Inject custom CSS to change the color of selected tasks
@@ -63,6 +99,7 @@ def main():
     # Define the ordered list of tasks.
     task_order = [
         "Text Generation", 
+        "Reasoning", 
         "Image Generation", 
         "Text Classification", 
         "Image Classification", 
@@ -77,17 +114,18 @@ def main():
     st.sidebar.write("#### 1. Select task(s) to view models")
     selected_tasks = st.sidebar.multiselect("", options=task_order, default=["Text Generation"])
     # Mapping from task to CSV file name.
-    task_to_file = {
-        "Text Generation": "text_gen_energyscore.csv",
-        "Image Generation": "image_generation_energyscore.csv",
-        "Text Classification": "text_classification_energyscore.csv",
-        "Image Classification": "image_classification_energyscore.csv",
-        "Image Captioning": "image_caption_energyscore.csv",
-        "Summarization": "summarization_energyscore.csv",
-        "Speech-to-Text (ASR)": "asr_energyscore.csv",
-        "Object Detection": "object_detection_energyscore.csv",
-        "Question Answering": "question_answering_energyscore.csv",
-        "Sentence Similarity": "sentence_similarity_energyscore.csv"
+    TASK_TO_CSV = {
+        "Text Generation": "text_generation.csv",
+        "Reasoning": "reasoning.csv",
+        "Image Generation": "image_generation.csv",
+        "Text Classification": "text_classification.csv",
+        "Image Classification": "image_classification.csv",
+        "Image Captioning": "image_captioning.csv",
+        "Summarization": "summarization.csv",
+        "Speech-to-Text (ASR)": "asr.csv",
+        "Object Detection": "object_detection.csv",
+        "Question Answering": "question_answering.csv",
+        "Sentence Similarity": "sentence_similarity.csv"
     }
     st.sidebar.write("#### 2. Select a model to generate label")
     default_model_data = {
@@ -107,7 +145,7 @@ def main():
         for task in selected_tasks:
             file_name = task_to_file[task]
             try:
-                df = pd.read_csv(file_name)
+                df = read_csv_from_hub(file_name)
             except FileNotFoundError:
                 st.sidebar.error(f"Could not find '{file_name}' for task {task}!")
                 continue