Update app.py

app.py

@@ -7,8 +7,42 @@ import plotly.graph_objs as go
 from huggingface_hub import HfApi
 from huggingface_hub.utils import RepositoryNotFoundError, RevisionNotFoundError
 
-from yall import create_yall
-
+#from yall import create_yall
+
+def place_holder_dataframe():
+    list_dict = [
+        {"gist_id":"mistralai/Mistral-7B-Instruct-v0.3",
+        "filename":"https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3/blob/main/README.md",
+        "url":"https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3",
+        "model_name":"Mistral-7B-Instruct-v0.3",
+        "model_id":"mistralai/Mistral-7B-Instruct-v0.3",
+        "Model":"Mistral-7B-Instruct-v0.3",
+        "Elo":1200,
+        "Undetected rate":0.27
+        },
+        {
+        "gist_id":"mistralai/Mixtral-8x22B-Instruct-v0.1",
+        "filename":"https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1/blob/main/README.md",
+        "url":"https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1",
+        "model_name":"Mixtral-8x22B-Instruct-v0.1",
+        "model_id":"mistralai/Mixtral-8x22B-Instruct-v0.1",
+        "Model":"Mixtral-8x22B-Instruct-v0.1",
+        "Elo":1950,
+        "Undetected rate":0.63
+        },
+        {
+        "gist_id":"mistralai/Mixtral-8x7B-Instruct-v0.1",
+        "filename":"https://huggingface.co/mistralai/Mixtral-8x7B-v0.1/blob/main/README.md",
+        "url":"https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1",
+        "model_name":"Mixtral-8x7B-Instruct-v0.1",
+        "model_id":"mistralai/Mixtral-8x7B-Instruct-v0.1",
+        "Model":"Mixtral-8x7B-Instruct-v0.1",
+        "Elo":1467,
+        "Undetected rate":0.41
+        }
+    ]
+    df = pd.DataFrame(list_dict)
+    return df
 
 
 def convert_markdown_table_to_dataframe(md_content):
@@ -89,61 +123,34 @@ def create_bar_chart(df, category):
 
 
 def main():
-    st.set_page_config(page_title="YALL - Yet Another LLM Leaderboard", layout="wide")
+    st.set_page_config(page_title="LLM Roleplay Leaderboard", layout="wide")
 
-    st.title("🏆 YALL - Yet Another LLM Leaderboard")
-    st.markdown("Leaderboard made with 🧐 [LLM AutoEval](https://github.com/mlabonne/llm-autoeval) using [Nous](https://huggingface.co/NousResearch) benchmark suite.")
-    content = create_yall()
-    tab1, tab2 = st.tabs(["🏆 Leaderboard", "📝 About"])
+    st.title("🏆🎭 LLM Roleplay Leaderboard")
+    st.markdown("LLM Roleplay Leaderboard that uses scores from the matou garou roleplay game 🏠🐈‍.")
+    #content = create_yall()
+    tab1, tab2 = st.tabs(["🏆🎭 Leaderboard", "📝 About"])
 
-    # Leaderboard tab
+    df = place_holder_dataframe()
     with tab1:
-        if content:
+        if len(df)>0:
             try:
-                score_columns = ['Elo']
-
-                # Display dataframe
-                full_df = convert_markdown_table_to_dataframe(content)
-                for col in score_columns:
-                    # Corrected use of pd.to_numeric
-                    full_df[col] = pd.to_numeric(full_df[col].str.strip(), errors='coerce')
-                full_df = get_model_info(full_df)
-                full_df['Tags'] = full_df['Tags'].fillna('')
-                df = pd.DataFrame(columns=full_df.columns)
-
-                # Create a DataFrame based on selected filters
-                dfs_to_concat = []
-
-                # Concatenate the DataFrames
-                if dfs_to_concat:
-                    df = pd.concat(dfs_to_concat, ignore_index=True)
-
-                # Sort values
                 df = df.sort_values(by='Elo', ascending=False)
-
                 # Add a search bar
                 search_query = st.text_input("Search models", "")
-
-                # Filter the DataFrame based on the search query
-                if search_query:
-                    df = df[df['Model'].str.contains(search_query, case=False)]
-
                 # Display the filtered DataFrame or the entire leaderboard
                 st.dataframe(
-                    df[['Model'] + score_columns + ['Likes', 'URL']],
+                    df[['Model', 'Elo', 'url', 'Undetected rate']],
                     use_container_width=True,
                     column_config={
-                        "Likes": st.column_config.NumberColumn(
-                            "Likes",
-                            help="Number of likes on Hugging Face",
-                            format="%d ❤️",
-                        ),
-                        "URL": st.column_config.LinkColumn("URL"),
+                        "url": st.column_config.LinkColumn("url"),
                     },
                     hide_index=True,
-                    height=int(len(df) * 36.2),
                 )
 
+                # Filter the DataFrame based on the search query
+                if search_query:
+                    df = df[df['Model'].str.contains(search_query, case=False)]
+
                 # Comparison between models
                 selected_models = st.multiselect('Select models to compare', df['Model'].unique())
                 comparison_df = df[df['Model'].isin(selected_models)]
@@ -151,12 +158,7 @@ def main():
                     comparison_df,
                     use_container_width=True,
                     column_config={
-                        "Likes": st.column_config.NumberColumn(
-                            "Likes",
-                            help="Number of likes on Hugging Face",
-                            format="%d ❤️",
-                        ),
-                        "URL": st.column_config.LinkColumn("URL"),
+                        "url": st.column_config.LinkColumn("url"),
                     },
                     hide_index=True,
                 )
@@ -176,21 +178,12 @@ def main():
                     )
 
                 # Full-width plot for the first category
-                create_bar_chart(df, score_columns[0])
+                create_bar_chart(df, "Elo")
 
                 # Next two plots in two columns
                 col1, col2 = st.columns(2)
                 with col1:
-                    create_bar_chart(df, score_columns[1])
-                with col2:
-                    create_bar_chart(df, score_columns[2])
-
-                # Last two plots in two columns
-                col3, col4 = st.columns(2)
-                with col3:
-                    create_bar_chart(df, score_columns[3])
-                with col4:
-                    create_bar_chart(df, score_columns[4])
+                    create_bar_chart(df, "Undetected rate")
 
 
             except Exception as e: