testing

app.py

@@ -1,27 +1,17 @@
 import gradio as gr
 import pandas as pd
 from huggingface_hub import list_models
-
-
-def get_submissions(category):
-    submissions = list_models(filter=["dreambooth-hackathon", category], full=True)
-    leaderboard_models = []
-
-    for submission in submissions:
-        # user, model, likes
-        user_id = submission.id.split("/")[0]
-        leaderboard_models.append(
-            (
-                make_clickable_user(user_id),
-                make_clickable_model(submission.id),
-                submission.likes,
-            )
-        )
-
-    df = pd.DataFrame(data=leaderboard_models, columns=["User", "Model", "Likes"])
-    df.sort_values(by=["Likes"], ascending=False, inplace=True)
-    df.insert(0, "Rank", list(range(1, len(df) + 1)))
-    return df
+import plotly.express as px
+
+def get_plots(task_df):
+    grouped_df = task_df[['total_gpu_energy', 'model']].groupby('model').mean().sort_values('total_gpu_energy',ascending = False)
+    grouped_df = grouped_df.reset_index()
+    grouped_df['model'] = grouped_df['model'].str.split('/').str[-1]
+    grouped_df['task'] = 'text_classification'
+    grouped_df['total_gpu_energy (Wh)'] = grouped_df['total_gpu_energy']*1000
+    grouped_df['energy_star'] = pd.cut(grouped_df['total_gpu_energy (Wh)'], 3, labels=["⭐⭐⭐", "⭐⭐", "⭐"])
+    grouped_df = px.scatter(grouped_df, x="model", y="total_gpu_energy (Wh)", height= 500, width= 800, color = 'energy_star', color_discrete_map={"⭐": 'red', "⭐⭐": "yellow", "⭐⭐⭐": "green"})
+    return grouped_df
 
 # %% app.ipynb 3
 demo = gr.Blocks()
@@ -45,9 +35,7 @@ with demo:
                 )
         with gr.TabItem("Text Classification 🎭"):
             with gr.Row():
-                food_data = gr.components.Dataframe(
-                    type="pandas", datatype=["number", "markdown", "markdown", "number"]
-                )
+                plot = gr.Plot(get_plots('data/text_classification.csv'))
         with gr.TabItem("Image Classification 🖼️"):
             with gr.Row():
                 landscape_data = gr.components.Dataframe(

data/text_classification.csv

@@ -0,0 +1,14 @@
+,timestamp,task,model,dataset,total_gpu_energy
+0,2024-09-04-20-43-25,text_classification,nlptown/bert-base-multilingual-uncased-sentiment,EnergyStarAI/text_classification,0.0003565813408208385
+0,2024-09-04-20-53-52,text_classification,bhadresh-savani/electra-base-emotion,EnergyStarAI/text_classification,0.0002521368683758851
+0,2024-09-04-20-46-36,text_classification,distilbert/distilbert-base-uncased-finetuned-sst-2-english,EnergyStarAI/text_classification,0.00021773597974501514
+0,2024-09-04-20-51-02,text_classification,michelecafagna26/gpt2-medium-finetuned-sst2-sentiment,EnergyStarAI/text_classification,0.0008015121412091375
+0,2024-09-04-20-49-06,text_classification,lxyuan/distilbert-base-multilingual-cased-sentiments-student,EnergyStarAI/text_classification,0.00022141689935395448
+0,2024-09-04-20-48-33,text_classification,mnoukhov/gpt2-imdb-sentiment-classifier,EnergyStarAI/text_classification,0.00031876844945895045
+0,2024-09-04-20-28-47,text_classification,lvwerra/distilbert-imdb,EnergyStarAI/text_classification,0.00021602192281751088
+0,2024-09-04-20-44-33,text_classification,Cheng98/bert-large-sst2,EnergyStarAI/text_classification,0.0009180142621886489
+0,2024-09-04-22-01-22,text_classification_t5,google-t5/t5-large,EnergyStarAI/text_classification,0.00791976258580498
+0,2024-09-04-22-09-36,text_classification_t5,google-t5/t5-11b,EnergyStarAI/text_classification,0.027787078868534286
+0,2024-09-04-22-01-30,text_classification_t5,google-t5/t5-3b,EnergyStarAI/text_classification,0.011680515899960752
+0,2024-09-04-21-35-23,text_classification_t5,google-t5/t5-base,EnergyStarAI/text_classification,0.004126364467755295
+0,2024-09-04-22-01-48,text_classification_t5,google-t5/t5-small,EnergyStarAI/text_classification,0.0022160669117411657