fix labels not found; handle input too long

app_text_classification.py

@@ -201,6 +201,7 @@ def get_demo():
     gr.on(
         triggers=[
             model_id_input.change,
+            model_id_input.input,
             dataset_id_input.change,
             dataset_config_input.change,
             dataset_split_input.change,

text_classification.py

@@ -28,10 +28,14 @@ def get_labels_and_features_from_dataset(ds):
         if len(label_keys) == 0: # no labels found
             # return everything for post processing
             return list(dataset_features.keys()), list(dataset_features.keys()), None
+        
+        labels = None
         if not isinstance(dataset_features[label_keys[0]], datasets.ClassLabel):
             if hasattr(dataset_features[label_keys[0]], "feature"):
                 label_feat = dataset_features[label_keys[0]].feature
                 labels = label_feat.names
+            else:
+                labels = ds.unique(label_keys[0])
         else:
             labels = dataset_features[label_keys[0]].names
         return labels, features, label_keys
@@ -83,11 +87,19 @@ def hf_inference_api(model_id, hf_token, payload):
     url = f"{hf_inference_api_endpoint}/models/{model_id}"
     headers = {"Authorization": f"Bearer {hf_token}"}
     response = requests.post(url, headers=headers, json=payload)
+
     if not hasattr(response, "status_code") or response.status_code != 200:
         logger.warning(f"Request to inference API returns {response}")
+  
     try:
+        output = response.json()
+        if "error" in output and "Input is too long" in output["error"]:
+          payload.update({"parameters": {"truncation": True, "max_length": 512}})
+          response = requests.post(url, headers=headers, json=payload)
+          if not hasattr(response, "status_code") or response.status_code != 200:
+              logger.warning(f"Request to inference API returns {response}")
         return response.json()
-    except Exception:
+    except Exception:                
         return {"error": response.content}
     
 def preload_hf_inference_api(model_id):

text_classification_ui_helpers.py

@@ -341,8 +341,8 @@ def align_columns_and_show_prediction(
     ):
         return (
             gr.update(value=MAPPING_STYLED_ERROR_WARNING, visible=True),
-            gr.update(visible=False),
-            gr.update(visible=False),
+            gr.update(value=prediction_input, lines=min(len(prediction_input)//225 + 1, 5), visible=True),
+            gr.update(value=prediction_response, visible=True),
             gr.update(visible=True, open=True),
             gr.update(interactive=(run_inference and inference_token != "")),
             "",
@@ -351,7 +351,7 @@ def align_columns_and_show_prediction(
 
     return (
         gr.update(value=VALIDATED_MODEL_DATASET_STYLED, visible=True),
-        gr.update(value=prediction_input, lines=len(prediction_input)//225 + 1, visible=True),
+        gr.update(value=prediction_input, lines=min(len(prediction_input)//225 + 1, 5), visible=True),
         gr.update(value=prediction_response, visible=True),
         gr.update(visible=True, open=False),
         gr.update(interactive=(run_inference and inference_token != "")),
@@ -428,7 +428,7 @@ def try_submit(m_id, d_id, config, split, inference, inference_token, uid):
     ds = datasets.load_dataset(d_id, config, split=split, trust_remote_code=True)
     ds_labels, ds_features, label_keys = get_labels_and_features_from_dataset(ds)
     label_mapping, feature_mapping = construct_label_and_feature_mapping(all_mappings, ds_labels, ds_features, label_keys)
-
+    
     eval_str = f"[{m_id}]<{d_id}({config}, {split} set)>"
     save_job_to_pipe(
         uid,