Update app.py

app.py

@@ -1,108 +1,33 @@
 import os, glob
-import json
-from datetime import datetime, timezone
-from dataclasses import dataclass
-from datasets import load_dataset, Dataset
 import pandas as pd
 import gradio as gr
-from huggingface_hub import HfApi, snapshot_download, ModelInfo, list_models
-from enum import Enum
+from datasets import load_dataset
+from huggingface_hub import HfApi
 
 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
-    return model_size
-
-def add_docker_eval(zip_file):
-    new_fid_list = zip_file.split("/")
-    new_fid = new_fid_list[-1]
-    if new_fid.endswith('.zip'):
-        API.upload_file(
-            path_or_fileobj=zip_file,
-            repo_id="AIEnergyScore/tested_proprietary_models",
-            path_in_repo='submitted_models/' + new_fid,
-            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:
-        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",
-        }
-        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 get_leaderboard_models():
+    """
+    Reads CSV files from the leaderboard directory and returns a DataFrame
+    containing the 'model' and 'task' columns.
+    If no CSV files are found, returns an empty DataFrame with those columns.
+    """
+    path = r'leaderboard_v0_data/energy'
+    filenames = glob.glob(os.path.join(path, "*.csv"))
+    data = []
+    for filename in filenames:
+        data.append(pd.read_csv(filename))
+    if not data:
+        return pd.DataFrame(columns=['model', 'task'])
+    leaderboard_data = pd.concat(data, ignore_index=True)
+    return leaderboard_data[['model', 'task']]
 
 def print_existing_models():
+    """
+    Loads a dataset of requests and returns the models that have been benchmarked.
+    """
     requests = load_dataset("AIEnergyScore/requests_debug", split="test", token=TOKEN)
     requests_dset = requests.to_pandas()
     model_df = requests_dset[['model', 'status']]
@@ -116,23 +41,14 @@ def highlight_cols(x):
     df[df['status'] == 'FAILED'] = 'color: red'
     return df
 
-# Applying the style function for the table
+# Apply styling to the recently benchmarked models table.
 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))
-    # Return an empty dataframe with expected columns if no files are found
-    if not data:
-        return pd.DataFrame(columns=['model', 'task'])
-    leaderboard_data = pd.concat(data, ignore_index=True)
-    return leaderboard_data[['model', 'task']]
-
 def get_zip_data_link():
+    """
+    Returns an HTML link for downloading logs.
+    """
     return (
         '<a href="https://example.com/download.zip" '
         'style="text-decoration: none; font-weight: bold; font-size: 1.1em; '
@@ -153,7 +69,7 @@ with gr.Blocks() as demo:
       /* Center the subtitle text */
       .centered-subtitle {
           text-align: center;
-          font-size: 1.2em;
+          font-size: 1.4em;
           margin-bottom: 20px;
       }
       /* Full width container for matching widget edges */
@@ -163,8 +79,13 @@ with gr.Blocks() as demo:
     </style>
     ''')
 
-    # --- Header Links (at the very top) ---
+    # --- Header Links ---
     with gr.Row(elem_classes="header-links"):
+        leaderboard_link = gr.HTML(
+            '<a href="https://huggingface.co/spaces/AIEnergyScore/Leaderboard" '
+            'style="text-decoration: none; font-weight: bold; font-size: 1.1em; '
+            'color: black; font-family: \'Inter\', sans-serif;">Leaderboard</a>'
+        )
         submission_link = gr.HTML(
             '<a href="https://huggingface.co/spaces/AIEnergyScore/submission_portal" '
             'style="text-decoration: none; font-weight: bold; font-size: 1.1em; '
@@ -192,61 +113,23 @@ with gr.Blocks() as demo:
             'color: black; font-family: \'Inter\', sans-serif;">Community</a>'
         )
     
-    # --- Logo (centered) ---
+    # --- Logo (Centered) ---
     gr.HTML('''
-    <div style="margin-top: 0px;">
+    <div style="text-align: center; margin-top: 20px;">
         <img src="https://huggingface.co/spaces/AIEnergyScore/Leaderboard/resolve/main/logo.png" 
              alt="Logo" 
-             style="display: block; margin: 0 auto; max-width: 500px; height: auto;">
+             style="max-width: 500px; height: auto;">
     </div>
     ''')
 
-    # --- Subtitle (centered) ---
-    gr.Markdown('<p class="centered-subtitle">Welcome to the AI Energy Score Leaderboard. Select the task to see scored model results.</p>')
+    # --- Centered Subtitle ---
+    gr.Markdown('<p class="centered-subtitle">Welcome to the AI Energy Score Leaderboard. Explore the top-performing models below.</p>')
 
-    # --- Main UI Container (ensuring matching edges) ---
+    # --- Leaderboard Tables ---
     with gr.Column(elem_classes="full-width"):
-        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,
-                )
-        
-        # --- Docker Log Submission (Simplified) ---
-        with gr.Accordion("Submit log files from a Docker run:", open=False):
-            gr.Markdown("""
-            **⚠️ Warning: By uploading the zip file, you confirm that you have read and agree to the following terms:**
-
-            - **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.
-            - **Data Integrity:** You certify that the log files submitted are accurate, unaltered, and generated directly from testing your model as per the specified benchmarking procedures.
-            - **Model Representation:** You affirm that the model tested and submitted is representative of the production-level version, 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", interactive=True)
-            u.upload(add_docker_eval, u, file_output)
-        
-        # --- Leaderboard and Recent Models Accordions ---
-        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(), elem_classes="full-width")
-                with gr.Accordion("Models that have been benchmarked recently:", open=False, visible=False):
-                    gr.Dataframe(formatted_df, elem_classes="full-width")
+        with gr.Accordion("Latest Leaderboard", open=True):
+            gr.Dataframe(get_leaderboard_models(), elem_classes="full-width")
+        with gr.Accordion("Recently Benchmarked Models", open=False):
+            gr.Dataframe(formatted_df, elem_classes="full-width")
 
-demo.launch()
+demo.launch()