Fix quality

app.py

@@ -34,9 +34,9 @@ TASK_TO_ID = {
 SUPPORTED_TASKS = list(TASK_TO_ID.keys())
 
 
-###########
-### APP ###
-###########
+#######
+# APP #
+#######
 st.title("Evaluation as a Service")
 st.markdown(
     """
@@ -65,18 +65,22 @@ if metadata is None:
     st.warning("No evaluation metadata found. Please configure the evaluation job below.")
 
 with st.expander("Advanced configuration"):
-    ## Select task
+    # Select task
     selected_task = st.selectbox(
         "Select a task",
         SUPPORTED_TASKS,
         index=SUPPORTED_TASKS.index(metadata[0]["task_id"]) if metadata is not None else 0,
     )
-    ### Select config
+    # Select config
     configs = get_dataset_config_names(selected_dataset)
     selected_config = st.selectbox("Select a config", configs)
 
-    ## Select splits
-    splits_resp = http_get(path="/splits", domain=DATASETS_PREVIEW_API, params={"dataset": selected_dataset})
+    # Select splits
+    splits_resp = http_get(
+        path="/splits",
+        domain=DATASETS_PREVIEW_API,
+        params={"dataset": selected_dataset},
+    )
     if splits_resp.status_code == 200:
         split_names = []
         all_splits = splits_resp.json()
@@ -90,11 +94,15 @@ with st.expander("Advanced configuration"):
             index=split_names.index(metadata[0]["splits"]["eval_split"]) if metadata is not None else 0,
         )
 
-    ## Select columns
+    # Select columns
     rows_resp = http_get(
         path="/rows",
         domain=DATASETS_PREVIEW_API,
-        params={"dataset": selected_dataset, "config": selected_config, "split": selected_split},
+        params={
+            "dataset": selected_dataset,
+            "config": selected_config,
+            "split": selected_split,
+        },
     ).json()
     col_names = list(pd.json_normalize(rows_resp["rows"][0]["row"]).columns)
 
@@ -136,7 +144,7 @@ with st.expander("Advanced configuration"):
             st.markdown("`tags` column")
         with col2:
             tokens_col = st.selectbox(
-                "This column should contain the parts of the text (as an array of tokens) you want to assign labels to",
+                "This column should contain the array of tokens",
                 col_names,
                 index=col_names.index(get_key(metadata[0]["col_mapping"], "tokens")) if metadata is not None else 0,
             )
@@ -247,7 +255,11 @@ with st.form(key="form"):
     print("Selected models:", selected_models)
 
     selected_models = filter_evaluated_models(
-        selected_models, selected_task, selected_dataset, selected_config, selected_split
+        selected_models,
+        selected_task,
+        selected_dataset,
+        selected_config,
+        selected_split,
     )
     print("Selected models:", selected_models)
 
@@ -278,7 +290,10 @@ with st.form(key="form"):
             }
             print(f"Payload: {payload}")
             project_json_resp = http_post(
-                path="/projects/create", payload=payload, token=HF_TOKEN, domain=AUTOTRAIN_BACKEND_API
+                path="/projects/create",
+                payload=payload,
+                token=HF_TOKEN,
+                domain=AUTOTRAIN_BACKEND_API,
             ).json()
             print(project_json_resp)
 
@@ -293,7 +308,11 @@ with st.form(key="form"):
                     payload=payload,
                     token=HF_TOKEN,
                     domain=AUTOTRAIN_BACKEND_API,
-                    params={"type": "dataset", "config_name": selected_config, "split_name": selected_split},
+                    params={
+                        "type": "dataset",
+                        "config_name": selected_config,
+                        "split_name": selected_split,
+                    },
                 ).json()
                 print(data_json_resp)
                 if data_json_resp["download_status"] == 1:
@@ -306,10 +325,11 @@ with st.form(key="form"):
                     if train_json_resp["success"]:
                         st.success(f"✅ Successfully submitted evaluation job with project ID {project_id}")
                         st.markdown(
-                            f"""
+                            """
                         Evaluation takes appoximately 1 hour to complete, so grab a ☕ or 🍵 while you wait:
 
-                        * 📊 Click [here](https://huggingface.co/spaces/autoevaluate/leaderboards) to view the results from your submission
+                        * 📊 Click [here](https://huggingface.co/spaces/autoevaluate/leaderboards) to view the \
+                            results from your submission
                         """
                         )
                     else:

utils.py

@@ -27,7 +27,11 @@ def http_post(path: str, token: str, payload=None, domain: str = None, params=No
     """HTTP POST request to the AutoNLP API, raises UnreachableAPIError if the API cannot be reached"""
     try:
         response = requests.post(
-            url=domain + path, json=payload, headers=get_auth_headers(token=token), allow_redirects=True, params=params
+            url=domain + path,
+            json=payload,
+            headers=get_auth_headers(token=token),
+            allow_redirects=True,
+            params=params,
         )
     except requests.exceptions.ConnectionError:
         print("❌ Failed to reach AutoNLP API, check your internet connection")
@@ -39,7 +43,10 @@ def http_get(path: str, domain: str, token: str = None, params: dict = None) ->
     """HTTP POST request to the AutoNLP API, raises UnreachableAPIError if the API cannot be reached"""
     try:
         response = requests.get(
-            url=domain + path, headers=get_auth_headers(token=token), allow_redirects=True, params=params
+            url=domain + path,
+            headers=get_auth_headers(token=token),
+            allow_redirects=True,
+            params=params,
         )
     except requests.exceptions.ConnectionError:
         print("❌ Failed to reach AutoNLP API, check your internet connection")
@@ -58,7 +65,9 @@ def get_metadata(dataset_name: str) -> Union[Dict, None]:
 def get_compatible_models(task, dataset_name):
     # TODO: relax filter on PyTorch models once supported in AutoTrain
     filt = ModelFilter(
-        task=AUTOTRAIN_TASK_TO_HUB_TASK[task], trained_dataset=dataset_name, library=["transformers", "pytorch"]
+        task=AUTOTRAIN_TASK_TO_HUB_TASK[task],
+        trained_dataset=dataset_name,
+        library=["transformers", "pytorch"],
     )
     compatible_models = api.list_models(filter=filt)
     return [model.modelId for model in compatible_models]