Adding application

app.py

@@ -0,0 +1,3 @@
+from src.display import display_app
+
+display_app()

requirements.txt

@@ -0,0 +1,57 @@
+altair==5.2.0
+attrs==23.2.0
+backports.zoneinfo==0.2.1
+blinker==1.7.0
+cachetools==5.3.3
+certifi==2024.2.2
+charset-normalizer==3.3.2
+click==8.1.7
+contourpy==1.1.1
+cycler==0.12.1
+fonttools==4.49.0
+gitdb==4.0.11
+GitPython==3.1.42
+idna==3.6
+importlib-metadata==7.0.1
+importlib_resources==6.1.2
+Jinja2==3.1.3
+jsonschema==4.21.1
+jsonschema-specifications==2023.12.1
+kiwisolver==1.4.5
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+matplotlib==3.7.5
+mdurl==0.1.2
+numpy==1.24.4
+packaging==23.2
+pandas==2.0.3
+pillow==10.2.0
+pkgutil_resolve_name==1.3.10
+plotly==5.19.0
+protobuf==4.25.3
+pyarrow==15.0.0
+pydeck==0.8.1b0
+Pygments==2.17.2
+pyparsing==3.1.1
+python-dateutil==2.9.0.post0
+python-decouple==3.8
+pytz==2024.1
+referencing==0.33.0
+requests==2.31.0
+rich==13.7.1
+rpds-py==0.18.0
+six==1.16.0
+smmap==5.0.1
+streamlit==1.31.1
+streamlit-aggrid==0.3.4.post3
+tenacity==8.2.3
+toml==0.10.2
+toolz==0.12.1
+tornado==6.4
+typing_extensions==4.10.0
+tzdata==2024.1
+tzlocal==5.2
+urllib3==2.2.1
+validators==0.22.0
+watchdog==4.0.0
+zipp==3.17.0

src/__init__.py

@@ -0,0 +1 @@
+# src/__init__.py

src/content.py

@@ -0,0 +1 @@
+# Nothing for now

src/display.py

@@ -0,0 +1,80 @@
+import streamlit as st
+from src.load_data import load_dataframe, sort_by
+from src.plot import plot_radar_chart_index, plot_radar_chart_name
+from st_aggrid import GridOptionsBuilder, AgGrid
+
+def display_app():
+    st.markdown("# Open LLM Leaderboard Viz")
+    st.markdown("This is a visualization of the results in [open-llm-leaderboard/results](https://huggingface.co/datasets/open-llm-leaderboard/results)")
+    st.markdown("To select a model, click on the checkbox beside its name.")
+
+    
+    
+    #container = st.container(height = 150)
+
+    dataframe = load_dataframe()
+
+    sort_selection = st.selectbox(label = "Sort by:", options = list(dataframe.columns))
+    ascending = True
+    indexes = None
+    if sort_selection is None:
+        sort_selection = "model_name"
+        ascending = True
+    elif sort_selection == "model_name":
+        ascending = True
+    else:
+        ascending = False
+    name = st.text_input(label = ":mag: Search by name")
+    if name is not None:
+        indexes = dataframe["model_name"].str.contains(name)
+        if len(indexes) > 0:
+            dataframe = dataframe[indexes]
+        else:
+            dataframe = load_dataframe()
+
+    dataframe = sort_by(dataframe=dataframe, column_name=sort_selection, ascending= ascending)
+    dataframe_display = dataframe.copy()
+    dataframe_display[["ARC", "HellaSwag", "TruthfulQA", "Winogrande", "GSM8K" ,"MMLU", "Average"]] = dataframe[["ARC", "HellaSwag", "TruthfulQA", "Winogrande", "GSM8K" ,"MMLU", "Average"]].astype(float)
+    dataframe_display[["ARC", "HellaSwag", "TruthfulQA", "Winogrande", "GSM8K" ,"MMLU", "Average"]] = dataframe_display[["ARC", "HellaSwag", "TruthfulQA", "Winogrande", "GSM8K" ,"MMLU", "Average"]] *100
+    dataframe_display[["ARC", "HellaSwag", "TruthfulQA", "Winogrande", "GSM8K" ,"MMLU", "Average"]] = dataframe_display[["ARC", "HellaSwag", "TruthfulQA", "Winogrande", "GSM8K" ,"MMLU", "Average"]].round(2)
+
+    #Infer basic colDefs from dataframe types
+    gb = GridOptionsBuilder.from_dataframe(dataframe_display)
+    gb.configure_selection(selection_mode = "single", use_checkbox=True)
+    gb.configure_grid_options(domLayout='normal')
+    gridOptions = gb.build()
+
+    column1,col3, column2 = st.columns([0.26, 0.05, 0.69], gap = "small")
+
+    with column1:
+        #with container:
+        #st.dataframe(dataframe_display)
+        grid_response = AgGrid(
+    dataframe_display, 
+    gridOptions=gridOptions,
+    height=300, 
+    width='40%'
+    )
+        
+    subdata = dataframe.head(1)
+    if len(subdata) > 0:
+        model_name = subdata["model_name"].values[0]
+    else:
+        model_name = ""
+
+    with column2:
+        if grid_response['selected_rows'] is not None and len(grid_response['selected_rows']) > 0:
+            model_name = grid_response['selected_rows'][0]["model_name"]
+            figure = plot_radar_chart_name(dataframe=dataframe, model_name=model_name)
+            st.plotly_chart(figure, use_container_width=False)
+        else:
+            if len(subdata)>0:
+                figure = plot_radar_chart_name(dataframe=subdata, model_name=model_name)
+                st.plotly_chart(figure, use_container_width=True)
+
+    if grid_response['selected_rows'] is not None and len(grid_response['selected_rows']) > 0:
+        st.markdown("**Model name:**   %s" % grid_response['selected_rows'][0]["model_name"])
+    else:
+        st.markdown("**Model name:**   %s" % model_name)
+
+

src/load_data.py

@@ -0,0 +1,26 @@
+import pandas as pd
+
+def load_dataframe() -> pd.DataFrame:
+    """
+    Load dataframe from the csv file in public directory
+    Returns
+    dataframe: a pd.DataFrame of the average scores of the LLMs on each task
+    """
+
+    dataframe = pd.read_csv("public/datasets/models_scores.csv")
+    dataframe = dataframe.drop(columns = "Unnamed: 0")
+    return dataframe
+
+def sort_by(dataframe: pd.DataFrame, column_name: str, ascending:bool = False) -> pd.DataFrame:
+    """
+    Sort the dataframe by column_name
+    
+    Arguments:
+    - dataframe: a pandas dataframe to sort
+    - column_name: a string stating the column to sort the dataframe by
+    - ascending: a boolean stating to sort in ascending order or not, default to False
+
+    Returns:
+    a sorted dataframe
+    """
+    return dataframe.sort_values(by = column_name, ascending = ascending )

src/plot.py

@@ -0,0 +1,102 @@
+from src.load_data import load_dataframe
+import plotly.graph_objects as go
+import numpy as np
+import pandas as pd
+
+# Hugging Face Colors
+fillcolor = "#FFD21E"
+line_color = "#FF9D00"
+
+# opacity of the plot
+opacity = 0.75
+
+# categories to show radar chart
+categories = ["ARC", "GSM8K", "TruthfulQA", "Winogrande", "HellaSwag", "MMLU"]
+
+def plot_radar_chart_index(dataframe: pd.DataFrame, index: int, categories: list = categories, fillcolor: str = fillcolor, line_color:str = line_color):
+    """
+    plot the index-th row of the dataframe
+    
+    Arguments:
+    dataframe: a pandas DataFrame 
+    index: the index of the row we want to plot
+    categories: the list of the metrics
+    fillcolor: a string specifying the color to fill the area
+    line_color: a string specifying the color of the lines in the graph
+    """
+    fig = go.Figure()
+    data = dataframe.loc[index,categories].to_numpy()*100
+    data  = data.astype(float)
+    # rounding data
+    data = data.round(decimals = 2)
+    
+    # add data to close the area of the radar chart
+    data = np.append(data, data[0])
+    categories_theta = categories.copy()
+    categories_theta.append(categories[0])
+    model_name = dataframe.loc[index,"model_name"] 
+    #print("Printing data ", data, " for ", model_name)
+
+    fig.add_trace(go.Scatterpolar(
+          r=data,
+          theta=categories_theta,
+          fill='toself',
+          fillcolor = fillcolor,
+          opacity = opacity,
+          line=dict(color = line_color),
+          name= model_name
+    ))
+    fig.update_layout(
+      polar=dict(
+        radialaxis=dict(
+          visible=True,
+          range=[0, 100.]
+        )),
+      showlegend=False
+    )
+
+    return fig
+
+def plot_radar_chart_name(dataframe: pd.DataFrame, model_name: str, categories: list = categories, fillcolor: str = fillcolor, line_color:str = line_color):
+    """
+    plot the results of the model named model_name row of the dataframe
+    
+    Arguments:
+    dataframe: a pandas DataFrame 
+    model_name: a string stating the name of the model
+    categories: the list of the metrics
+    fillcolor: a string specifying the color to fill the area
+    line_color: a string specifying the color of the lines in the graph
+    """
+    fig = go.Figure()
+    data = dataframe[dataframe["model_name"] == model_name][categories].to_numpy()*100
+    data  = data.astype(float)
+    # rounding data
+    data = data.round(decimals = 2)
+    
+    # add data to close the area of the radar chart
+    data = np.append(data, data[0])
+    categories_theta = categories.copy()
+    categories_theta.append(categories[0])
+    model_name = model_name 
+    #print("Printing data ", data, " for ", model_name)
+
+    fig.add_trace(go.Scatterpolar(
+          r=data,
+          theta=categories_theta,
+          fill='toself',
+          fillcolor = fillcolor,
+          opacity = opacity,
+          line=dict(color = line_color),
+          name= model_name
+    ))
+    fig.update_layout(
+      polar=dict(
+        radialaxis=dict(
+          visible=True,
+          range=[0, 100.]
+        )),
+      showlegend=False
+    )
+
+    return fig