Switching to normalized task name.

app.py

@@ -9,7 +9,7 @@ from huggingface_hub import HfApi, snapshot_download, ModelInfo, list_models
 from enum import Enum
 
 
-OWNER = "EnergyStarAI"
+OWNER = "AIEnergyScore"
 COMPUTE_SPACE = f"{OWNER}/launch-computation-example"
 
 
@@ -22,7 +22,6 @@ task_mappings = {'automatic speech recognition':'automatic-speech-recognition',
                  '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
@@ -47,29 +46,25 @@ def add_docker_eval(zip_file):
     new_fid = new_fid_list[-1]
     if new_fid.endswith('.zip'):
         API.upload_file(
-            path_or_fileobj=zip_file ,
-            repo_id="EnergyStarAI/tested_proprietary_models",
+            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
+            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,
-):
+def add_new_eval(repo_id: str, task: str):
     model_owner = repo_id.split("/")[0]
-    model_name = repo_id.split("/")[1]  
-    model_list=[]
+    model_name = repo_id.split("/")[1]
     current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
-    requests= load_dataset("EnergyStarAI/requests_debug", split="test", token=TOKEN)
+    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()
+    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:
@@ -80,20 +75,21 @@ def add_new_eval(
         # 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))
-            
-        model_size = get_model_size(model_info=model_info)
-        
+            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,
-            "likes": model_info.likes,
+            "task": task_mappings[task],
+            "likes": likes,
             "params": model_size,
             "leaderboard_version": "v0",}
             #"license": license,
@@ -104,17 +100,17 @@ def add_new_eval(
         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("EnergyStarAI/requests_debug", split="test", token=TOKEN)
+        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("EnergyStarAI/requests_debug", split="test", token=TOKEN)
+    requests= load_dataset("AIEnergyScore/requests_debug", split="test", token=TOKEN)
     requests_dset = requests.to_pandas()
-    model_df= requests_dset[['model','status']] 
+    model_df= requests_dset[['model', 'status']]
     model_df = model_df[model_df['status'] == 'COMPLETED']
     return model_df
 
@@ -127,7 +123,7 @@ def highlight_cols(x):
 
 # Applying the style function
 existing_models = print_existing_models()
-formatted_df = existing_models.style.apply(highlight_cols, axis = None)
+formatted_df = existing_models.style.apply(highlight_cols, axis=None)
 
 def get_leaderboard_models():
     path = r'leaderboard_v0_data/energy'
@@ -148,9 +144,9 @@ with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column():
             task = gr.Dropdown(
-                choices=task_mappings.keys(),
+                choices=list(task_mappings.keys()),
                 label="Choose a benchmark task",
-                value = 'Text Generation',
+                value='Text Generation',
                 multiselect=False,
                 interactive=True,
             )
@@ -171,15 +167,15 @@ with gr.Blocks() as demo:
             )
     with gr.Row():
         with gr.Column():
-            with gr.Accordion("Submit log files from a Docker run:", open = False):
+            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:")
                 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)             
+                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):
+                with gr.Accordion("Models that are in the latest leaderboard version:", open=False):
                     gr.Dataframe(get_leaderboard_models())
-                with gr.Accordion("Models that have been benchmarked recently:", open = False):
+                with gr.Accordion("Models that have been benchmarked recently:", open=False):
                     gr.Dataframe(formatted_df)
 demo.launch()