updating plots, data

app.py

@@ -11,11 +11,12 @@ model_performance_df = pd.read_csv('data/performance.csv', header=0)
 emissions_df = pd.read_csv('data/co2_data.csv',header=0)
 modalities_df = pd.read_csv('data/modalities_data.csv',header=0)
 finetuned_df = emissions_df[~emissions_df['task'].str.contains('zero')]
+finetuned_df['task'] = finetuned_df['task'].str.replace('_',' ')
 
-fig0 = px.scatter(finetuned_df, x="dataset", y="query emissions (g)", color="model", log_y=True)
+fig0 = px.scatter(emissions_df, x="num_params", y="query emissions (g)", color="model", log_x=True, log_y=True)
 fig0.update_layout(xaxis={'categoryorder':'mean ascending'})
 fig0.update_layout(yaxis_title='Total carbon emitted (g)')
-fig0.update_layout(xaxis_title='Dataset')
+fig0.update_layout(xaxis_title='Number of Parameters')
 
 
 fig1 = px.box(finetuned_df, x="task", y="query_energy (kWh)", color="task", log_y=True)
@@ -40,15 +41,33 @@ demo = gr.Blocks()
 
 with demo:
     gr.Markdown("# CO2 Inference Demo")
-    gr.Markdown("## Explore the plots below to get more insights about the different models and tasks from our study.")
+    gr.Markdown("## TL;DR - We ran a series of experiments to measure the energy efficiency and carbon emissions of different\
+    models from the HuggingFace Hub, and to see how different tasks and models compare.\
+    Explore the plots below to get more insights about the different models and tasks from our study.")
+    with gr.Accordion("More details about our methodology:", open=False):
+        gr.Markdown("We chose ten ML tasks: text classification, token classification, question answering, \
+        ), masked language modeling, text generation, summarization, image classification, object detection, \
+         image captioning and image generation. For each of the taks, we chose three of the most downloaded datasets and 8 of the most \
+        downloaded models from the Hugging Face Hub. We ran each of the models ten times over a 1,000 sample from each of the models and measured the energy consumed and carbon emitted.")
     with gr.Row():
         with gr.Column():
+            gr.Markdown("## All models from our study (carbon)")
+            gr.Markdown("### Double click on the model name in the list on the right to isolate its datapoints:")
+            gr.Markdown("The axes of the plot are in logarithmic scale, meaning that the difference between the least carbon-intensive and the most carbon-intensive models is over 9,000 times!")
             gr.Plot(fig0)
     with gr.Row():
         with gr.Column():
+            gr.Markdown("## Task-by-task comparison (energy)")
+            gr.Markdown("### Grouping the models by task, we can see different patterns emerge:")
+            gr.Markdown("Image generation is by far the most energy- and carbon-intensive task from the ones studied, and text classification \
+            is the least.")
             gr.Plot(fig1)
     with gr.Row():
         with gr.Column():
+            gr.Markdown("##  Modality comparison (carbon)")
+            gr.Markdown("### Grouping the models by their modality shows the different characteristics of each one:")
+            gr.Markdown("We can see that tasks involving images (image-to-text, image-to-category) require more energy and emit more carbon\
+            than ones inolving text.")
             gr.Plot(fig2)