Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,15 +1,35 @@
 import streamlit as st
 import pandas as pd
+from datasets import load_dataset # Import the Hugging Face datasets library
 
 # Page configuration
 st.set_page_config(layout="wide", page_title="TranslateBench EN-ES Leaderboard")
 
 # Caching the data loading function
-@st.cache_data
-def load_data(file_path="model_benchmark_summary.csv"):
-    """Loads and preprocesses the benchmark data."""
+@st.cache_data # Use st.cache_data for dataframes and serializable objects
+def load_data_from_hf():
+    """Loads and preprocesses the benchmark data from Hugging Face."""
     try:
-        df = pd.read_csv(file_path)
+        st.info("Fetching data from Hugging Face (Thermostatic/TranslateBench-EN-ES)... This may take a moment.")
+        # Load the specific CSV file from the dataset
+        # The 'data_files' argument points to the specific file within the dataset repository.
+        # 'load_dataset' returns a DatasetDict. For a single CSV, it's typically under the 'train' key.
+        dataset_dict = load_dataset("Thermostatic/TranslateBench-EN-ES", data_files="model_benchmark_summary.csv")
+        
+        # Access the dataset (it will be the 'train' split by default for a single file)
+        if 'train' in dataset_dict:
+            dataset = dataset_dict['train']
+        else:
+            # Fallback in case the default split name isn't 'train'
+            # This gets the first (and likely only) key in the DatasetDict
+            first_split_name = list(dataset_dict.keys())[0]
+            dataset = dataset_dict[first_split_name]
+            st.warning(f"Using split '{first_split_name}' as 'train' split was not found.")
+
+        df = dataset.to_pandas()
+        st.success("Data loaded successfully from Hugging Face!")
+
+        # --- Preprocessing (same as your original code) ---
         # Extract provider from Model Name
         df['Provider'] = df['Model Name'].apply(lambda x: x.split('_')[0].capitalize())
         # Ensure score columns are numeric
@@ -17,22 +37,20 @@ def load_data(file_path="model_benchmark_summary.csv"):
         for col in score_cols:
             df[col] = pd.to_numeric(df[col], errors='coerce')
         return df
-    except FileNotFoundError:
-        st.error(f"Error: The file '{file_path}' was not found. Please make sure it's in the same directory as the script.")
-        return None
     except Exception as e:
-        st.error(f"An error occurred while loading or processing the data: {e}")
+        st.error(f"An error occurred while loading or processing data from Hugging Face: {e}")
         return None
 
 # --- Main Application ---
 st.title("🏆 TranslateBench EN-ES Leaderboard")
 st.markdown("""
 This leaderboard shows the performance of various models on the English-to-Spanish translation task.
+Data is sourced directly from the [Thermostatic/TranslateBench-EN-ES](https://huggingface.co/datasets/Thermostatic/TranslateBench-EN-ES) dataset on Hugging Face.
 You can sort the table by different metrics and filter by model provider.
 """)
 
 # Load data
-data_df = load_data()
+data_df = load_data_from_hf()
 
 if data_df is not None:
     # --- Sidebar for Controls ---
@@ -79,10 +97,14 @@ if data_df is not None:
             delta=f"{top_model[sort_by]:.4f} ({sort_by})",
             delta_color="off" # No up/down arrow needed here
         )
+        # Ensure all sortable_metrics exist in the top_model Series before trying to access them
         cols = st.columns(len(sortable_metrics))
         for i, metric in enumerate(sortable_metrics):
             with cols[i]:
-                st.metric(label=metric, value=f"{top_model[metric]:.4f}")
+                if metric in top_model:
+                    st.metric(label=metric, value=f"{top_model[metric]:.4f}")
+                else:
+                    st.metric(label=metric, value="N/A")
     else:
         st.info("No data to display for top performer based on current filters.")
 
@@ -97,8 +119,10 @@ if data_df is not None:
     formatter = {col: "{:.4f}" for col in sortable_metrics}
 
     if not sorted_df.empty:
+        # Ensure only existing columns are selected for display
+        existing_display_columns = [col for col in display_columns if col in sorted_df.columns]
         st.dataframe(
-            sorted_df[display_columns].style.format(formatter),
+            sorted_df[existing_display_columns].style.format(formatter),
             use_container_width=True,
             hide_index=True,
         )
@@ -106,12 +130,12 @@ if data_df is not None:
         st.info("No models match the current filter criteria.")
 
     # --- Show Raw Data (Optional) ---
-    if st.checkbox("Show Raw Data (Unsorted, Unfiltered)"):
+    if st.checkbox("Show Raw Data (Downloaded, Unsorted, Unfiltered)"):
         st.subheader("Raw Data")
         st.dataframe(data_df)
 
 else:
-    st.warning("Data could not be loaded. Please check the console for errors and ensure the CSV file is correct.")
+    st.warning("Data could not be loaded from Hugging Face. Please check the console for errors, your internet connection, and ensure the dataset/file path is correct.")
 
 st.markdown("---")
-st.markdown("Created with Streamlit and Pandas.")
+st.markdown("Created with Streamlit, Pandas, and data from Hugging Face Datasets.")