Update app.py

app.py

@@ -8,40 +8,16 @@ import gradio as gr
 from huggingface_hub import HfApi, snapshot_download, ModelInfo, list_models
 from enum import Enum
 
-
 OWNER = "AIEnergyScore"
 COMPUTE_SPACE = f"{OWNER}/launch-computation-example"
-
-
 TOKEN = os.environ.get("DEBUG")
 API = HfApi(token=TOKEN)
 
-
-
-task_mappings = {'automatic speech recognition':'automatic-speech-recognition', 'Object Detection': 'object-detection', 'Text Classification': 'text-classification', 
-                 'Image to Text':'image-to-text', 'Question Answering':'question-answering', 'Text Generation': 'text-generation',
-                 'Image Classification':'image-classification', 'Sentence Similarity': 'sentence-similarity',
-                 'Image Generation':'image-generation', 'Summarization':'summarization'}
-@dataclass
-class ModelDetails:
-    name: str
-    display_name: str = ""
-    symbol: str = "" # emoji
-
-def start_compute_space():
-    API.restart_space(COMPUTE_SPACE)  
-    gr.Info(f"Okay! {COMPUTE_SPACE} should be running now!")
-
-
-def get_model_size(model_info: ModelInfo):
-    """Gets the model size from the configuration, or the model name if the configuration does not contain the information."""
-    try:
-        model_size = round(model_info.safetensors["total"] / 1e9, 3)
-    except (AttributeError, TypeError):
-        return 0  # Unknown model sizes are indicated as 0, see NUMERIC_INTERVALS in app.py
-    return model_size
-
-def add_docker_eval(zip_file):
+def add_docker_eval_with_agreement(zip_file, agreement):
+    if not agreement:
+        gr.Warning("You must agree to the terms before submitting your energy score data.")
+        return
+    
     new_fid_list = zip_file.split("/")
     new_fid = new_fid_list[-1]
     if new_fid.endswith('.zip'):
@@ -52,130 +28,31 @@ def add_docker_eval(zip_file):
             repo_type="dataset",
             commit_message="Adding logs via submission Space.",
             token=TOKEN
-            )
+        )
         gr.Info('Uploaded logs to dataset! We will validate their validity and add them to the next version of the leaderboard.')
     else:
         gr.Info('You can only upload .zip files here!')
 
-
-def add_new_eval(repo_id: str, task: str):
-    model_owner = repo_id.split("/")[0]
-    model_name = repo_id.split("/")[1]
-    current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
-    requests = load_dataset("AIEnergyScore/requests_debug", split="test", token=TOKEN)
-    requests_dset = requests.to_pandas()
-    model_list = requests_dset[requests_dset['status'] == 'COMPLETED']['model'].tolist()
-    task_models = list(API.list_models(filter=task_mappings[task]))
-    task_model_names = [m.id for m in task_models]
-    if repo_id in model_list:
-        gr.Info('This model has already been run!')
-    elif repo_id not in task_model_names:
-        gr.Info("This model isn't compatible with the chosen task! Pick a different model-task combination")
-    else:
-        # Is the model info correctly filled?
-        try:
-            model_info = API.model_info(repo_id=repo_id)
-            model_size = get_model_size(model_info=model_info)
-            likes = model_info.likes
-        except Exception:
-            gr.Info("Could not find information for model %s" % (model_name))
-            model_size = None
-            likes = None
-
-        gr.Info("Adding request")
-
-        request_dict = {
-            "model": repo_id,
-            "status": "PENDING",
-            "submitted_time": pd.to_datetime(current_time),
-            "task": task_mappings[task],
-            "likes": likes,
-            "params": model_size,
-            "leaderboard_version": "v0",}
-            #"license": license,
-            #"private": False,
-        #}
-    
-        print("Writing out request file to dataset")
-        df_request_dict = pd.DataFrame([request_dict])
-        print(df_request_dict)
-        df_final = pd.concat([requests_dset, df_request_dict], ignore_index=True)
-        updated_dset = Dataset.from_pandas(df_final)
-        updated_dset.push_to_hub("AIEnergyScore/requests_debug", split="test", token=TOKEN)
-        
-        gr.Info("Starting compute space at %s " % COMPUTE_SPACE)
-        return start_compute_space()
-
-    
-def print_existing_models():
-    requests= load_dataset("AIEnergyScore/requests_debug", split="test", token=TOKEN)
-    requests_dset = requests.to_pandas()
-    model_df= requests_dset[['model', 'status']]
-    model_df = model_df[model_df['status'] == 'COMPLETED']
-    return model_df
-
-def highlight_cols(x): 
-    df = x.copy() 
-    df[df['status'] == 'COMPLETED'] = 'color: green'
-    df[df['status'] == 'PENDING'] = 'color: orange'
-    df[df['status'] == 'FAILED'] = 'color: red'
-    return df 
-
-# Applying the style function
-existing_models = print_existing_models()
-formatted_df = existing_models.style.apply(highlight_cols, axis=None)
-
-def get_leaderboard_models():
-    path = r'leaderboard_v0_data/energy'
-    filenames = glob.glob(path + "/*.csv")
-    data = []
-    for filename in filenames:
-        data.append(pd.read_csv(filename))
-    leaderboard_data = pd.concat(data, ignore_index=True)
-    return leaderboard_data[['model','task']]
-
-
 with gr.Blocks() as demo:
     gr.Markdown("# AI Energy Score | Submission Portal")
-    gr.Markdown("### The goal of the AI Energy Score project is to develop an energy-based rating system for AI model deployment that will guide members of the community in choosing models for different tasks based on energy efficiency.", elem_classes="markdown-text")   
-    gr.Markdown("### If you want us to evaluate a model hosted on the 🤗 Hub, enter the model ID and choose the corresponding task from the dropdown list below, then click **Run Analysis** to launch the benchmarking process.")
-    gr.Markdown("### If you've used the [Docker file](https://github.com/huggingface/AIEnergyScore/) to run your own evaluation, please submit the resulting log files at the bottom of the page.")
-    gr.Markdown("### The [Project Leaderboard](https://huggingface.co/spaces/AIEnergyScore/Leaderboard) will be updated twice a year.")
-    with gr.Row():
-        with gr.Column():
-            task = gr.Dropdown(
-                choices=list(task_mappings.keys()),
-                label="Choose a benchmark task",
-                value='Text Generation',
-                multiselect=False,
-                interactive=True,
-            )
-        with gr.Column():
-            model_name_textbox = gr.Textbox(label="Model name (user_name/model_name)")
-
-    with gr.Row():
-        with gr.Column():
-            submit_button = gr.Button("Submit for Analysis")
-            submission_result = gr.Markdown()
-            submit_button.click(
-                fn=add_new_eval,
-                inputs=[
-                    model_name_textbox,
-                    task,
-                ],
-                outputs=submission_result,
-            )
+    gr.Markdown("### The goal of the AI Energy Score project is to develop an energy-based rating system for AI model deployment that will guide members of the community in choosing models for different tasks based on energy efficiency.")
+    
     with gr.Row():
         with gr.Column():
             with gr.Accordion("Submit log files from a Docker run:", open=False):
                 gr.Markdown("If you've already benchmarked your model using the [Docker file](https://github.com/huggingface/EnergyStarAI/) provided, please upload the **entire run log directory** (in .zip format) below:")
+                
+                agreement_checkbox = gr.Checkbox(label="I agree to the following terms:")
+                agreement_text = gr.Markdown("""
+                By checking the box below and submitting your energy score data, you confirm and agree to the following:
+                1. **Public Data Sharing**: You consent to the public sharing of the energy performance data derived from your submission. No additional information related to this model including proprietary configurations will be disclosed.
+                2. **Data Integrity**: You validate that the log files submitted are accurate, unaltered, and generated directly from testing your model as per the specified benchmarking procedures.
+                3. **Model Representation**: You verify that the model tested and submitted is representative of the production-level version of the model, including its level of quantization and any other relevant characteristics impacting energy efficiency and performance.
+                """)
+                
                 file_output = gr.File(visible=False)
                 u = gr.UploadButton("Upload a zip file with logs", file_count="single")
-                u.upload(add_docker_eval, u, file_output)
-    with gr.Row():
-        with gr.Column():
-                with gr.Accordion("Models that are in the latest leaderboard version:", open=False, visible=False):
-                   gr.Dataframe(get_leaderboard_models())
-                with gr.Accordion("Models that have been benchmarked recently:", open=False, visible=False):
-                    gr.Dataframe(formatted_df)
-demo.launch()
+                
+                u.upload(add_docker_eval_with_agreement, [u, agreement_checkbox], file_output)
+
+demo.launch()