Update app.py

checking if model has already been tested

app.py

@@ -18,6 +18,8 @@ TOKEN = os.environ.get("DEBUG")
 API = HfApi(token=TOKEN)
 
 requests= load_dataset("EnergyStarAI/requests_debug", split="test", token=TOKEN)
+requests_dset = requests.to_pandas()
+model_list= requests_dset[requests_dset['status'] == 'COMPLETED']
 
 tasks = ['ASR', 'Object Detection', 'Text Classification', 'Image Captioning', 'Question Answering', 'Text Generation', 'Image Classification',
         'Sentence Similarity', 'Image Generation', 'Summarization']
@@ -50,47 +52,46 @@ def add_new_eval(
     model_name = repo_id.split("/")[1]  
     
     current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
-
-    # Is the model info correctly filled?
-    try:
-        model_info = API.model_info(repo_id=repo_id)
-    except Exception:
-        print("Could not find information for model %s" % (model))
+    if repo_id in model_list:
+        print('This model has already been run!')
         return
-    #    return styled_error("Could not get your model information. Please fill it up properly.")
-
-    model_size = get_model_size(model_info=model_info)
+    else:
+        # Is the model info correctly filled?
+        try:
+            model_info = API.model_info(repo_id=repo_id)
+        except Exception:
+            print("Could not find information for model %s" % (model))
+            return
     
-    print("Adding request")
-
-    requests_dset = requests.to_pandas()
+        model_size = get_model_size(model_info=model_info)
+        
+        print("Adding request")
     
-    request_dict = {
-        "model": repo_id,
-        "precision": "N/A",
-        "status": "PENDING",
-        "submitted_time": pd.to_datetime(current_time),
-        "task": task,
-        "likes": model_info.likes,
-        "params": model_size}
-        #"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("EnergyStarAI/requests_debug", split="test", token=TOKEN)
+        
+        
+        request_dict = {
+            "model": repo_id,
+            "precision": "N/A",
+            "status": "PENDING",
+            "submitted_time": pd.to_datetime(current_time),
+            "task": task,
+            "likes": model_info.likes,
+            "params": model_size}
+            #"license": license,
+            #"private": False,
+        #}
     
-    print("Starting compute space at %s " % COMPUTE_SPACE)
-    return start_compute_space()
+        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("EnergyStarAI/requests_debug", split="test", token=TOKEN)
+        
+        print("Starting compute space at %s " % COMPUTE_SPACE)
+        return start_compute_space()
 
 def print_existing_models():
-    requests = load_dataset("EnergyStarAI/requests_debug", split="test")
-    requests_dset = requests.to_pandas()
-    model_list= requests_dset[requests_dset['status'] == 'COMPLETED']
     return model_list        
 
 with gr.Blocks() as demo: