adding overall tab

app.py

@@ -3,6 +3,11 @@ import pandas as pd
 from huggingface_hub import list_models
 import plotly.express as px
 
+
+tasks = ['asr.csv', 'object_detection.csv', 'text_classification.csv', 'image_captioning.csv',
+        'question_answering.csv', 'text_generation.csv', 'image_classification.csv',
+        'sentence_similarity.csv', 'image_generation.csv', 'summarization.csv']
+
 def get_plots(task):
     #TO DO : hover text with energy efficiency number, parameters
     task_df= pd.read_csv('data/energy/'+task)
@@ -21,25 +26,65 @@ def get_plots(task):
     )
     return fig
 
+def get_all_plots():
+    for task in tasks:
+        task_df= pd.read_csv('data/energy/'+task)
+        params_df = pd.read_csv('data/params/'+task)
+        params_df= params_df.rename(columns={"Link": "model"})
+        tasks_df = pd.merge(task_df, params_df, on='model')
+        all_df = pd.DataFrame(columns = tasks_df.columns)
+        all_df = all_df.append(tasks_df)
+    all_df['Total GPU Energy (Wh)'] = all_df['total_gpu_energy']*1000
+    all_df = all_df.sort_values(by=['Total GPU Energy (Wh)'])
+    all_df['parameters'] = all_df['parameters'].apply(format_params)
+    all_df['energy_star'] = pd.cut(all_df['Total GPU Energy (Wh)'], 3, labels=["⭐⭐⭐", "⭐⭐", "⭐"])
+    fig = px.scatter(all_df, x="model", y='Total GPU Energy (Wh)', custom_data=['parameters'], height= 500, width= 800, color = 'energy_star', color_discrete_map={"⭐": 'red', "⭐⭐": "yellow", "⭐⭐⭐": "green"})
+    fig.update_traces(
+    hovertemplate="<br>".join([
+        "Total Energy: %{y}",
+        "Parameters: %{customdata[0]}"])
+    )
+    return fig
+
 def make_link(mname):
     link = "["+ str(mname).split('/')[1] +'](https://huggingface.co/'+str(mname)+")"
     return link
 
-def get_model_names(task_data):
-    #TODO: add link to results in model card of each model
-    task_df= pd.read_csv('data/params/'+task_data)
-    energy_df= pd.read_csv('data/energy/'+task_data)
+def get_model_names(task):
+    task_df= pd.read_csv('data/params/'+task)
+    energy_df= pd.read_csv('data/energy/'+task)
     task_df= task_df.rename(columns={"Link": "model"})
     all_df = pd.merge(task_df, energy_df, on='model')
     all_df=all_df.drop_duplicates(subset=['model'])
-    all_df['parameters'] = all_df['parameters'].apply(format_params)
-    all_df['model'] = all_df['model'].apply(make_link)
+    all_df['Parameters'] = all_df['parameters'].apply(format_params)
+    all_df['Model'] = all_df['model'].apply(make_link)
+    all_df['Total GPU Energy (Wh)'] = all_df['total_gpu_energy']*1000
+    all_df['Total GPU Energy (Wh)'] = all_df['Total GPU Energy (Wh)'].round(2)
+    all_df['Rating'] = pd.cut(all_df['Total GPU Energy (Wh)'], 3, labels=["⭐⭐⭐", "⭐⭐", "⭐"])
+    model_names= model_names.sort_values('Total GPU Energy (Wh)')
+    model_names = all_df[['Model','Rating','Total GPU Energy (Wh)', 'Parameters']]
+    return model_names
+
+def get_all_model_names():
+    #TODO: add link to results in model card of each model
+    for task in tasks:
+        task_df= pd.read_csv('data/params/'+task)
+        energy_df= pd.read_csv('data/energy/'+task)
+        task_df= task_df.rename(columns={"Link": "model"})
+        tasks_df = pd.merge(task_df, energy_df, on='model')
+        all_df = pd.DataFrame(columns = tasks_df.columns)
+        all_df = all_df.append(tasks_df)
+    all_df=all_df.drop_duplicates(subset=['model'])
+    all_df['Parameters'] = all_df['parameters'].apply(format_params)
+    all_df['Model'] = all_df['model'].apply(make_link)
     all_df['Total GPU Energy (Wh)'] = all_df['total_gpu_energy']*1000
     all_df['Total GPU Energy (Wh)'] = all_df['Total GPU Energy (Wh)'].round(2)
     all_df['Rating'] = pd.cut(all_df['Total GPU Energy (Wh)'], 3, labels=["⭐⭐⭐", "⭐⭐", "⭐"])
-    model_names = all_df[['model','parameters','Rating', 'Total GPU Energy (Wh)']]
+    model_names= model_names.sort_values('Total GPU Energy (Wh)')
+    model_names = all_df[['Model','Rating','Total GPU Energy (Wh)', 'Parameters']]
     return model_names
 
+
 def format_params(num):
     if num > 1000000000:
         if not num % 1000000000:
@@ -129,10 +174,18 @@ with demo:
                     plot = gr.Plot(get_plots('question_answering.csv'))
                 with gr.Column():
                     table = gr.Dataframe(get_model_names('question_answering.csv'), datatype="markdown")
+
+        with gr.TabItem("Overall"):
+            with gr.Row():
+                with gr.Column():
+                    plot = gr.Plot(get_all_plots)
+                with gr.Column():
+                    table = gr.Dataframe(get_all_model_names)
+                    
     with gr.Accordion("Methodology", open = False):
         gr.Markdown(
         """For each of the ten tasks above, we created a custom dataset with 1,000 entries (see all of the datasets on our [org Hub page](https://huggingface.co/EnergyStarAI)).
-        We then tested each of the models from the leaderboard on the appropriate task, measuring the energy consumed using [Code Carbon](https://mlco2.github.io/codecarbon/), an open-source Python package for tracking the environmental impacts of code.
+        We then tested each of the models from the leaderboard on the appropriate task on Nvidia A100 GPUs, measuring the energy consumed using [Code Carbon](https://mlco2.github.io/codecarbon/), an open-source Python package for tracking the environmental impacts of code.
         We developed and used a [Docker container](https://github.com/huggingface/EnergyStarAI/) to maximize the reproducibility of results, and to enable members of the community to benchmark internal models.
         Reach out to us if you want to collaborate!
         """)