updated app.py and README.md files

README.md

@@ -12,3 +12,6 @@ short_description: 'inent classification: comparision in DeBERTa, RoBERTa & BERT
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+
+First load may take a few seconds while models are being downloaded from the Hugging Face Hub.

app.py

@@ -39,9 +39,11 @@ models_info = {
         "model_class": TFDebertaForSequenceClassification
     }
 }
-
+cached_models = {}
 # loading models and tokenizers:
 def load_model_and_tokenizer(model_choice):
+    if model_choice in cached_models:
+        return cached_models[model_choice]
     config = models_info[model_choice]
     tokenizer = config["tokenizer"].from_pretrained(config["path"])
     model = config["model_class"].from_pretrained(config["path"])
@@ -51,6 +53,7 @@ def load_model_and_tokenizer(model_choice):
             id2label = true_id2label
     except:
         id2label = true_id2label
+    cached_models[model_choice] = (tokenizer, model, id2label)
     return tokenizer, model, id2label
 
 explainer = LimeTextExplainer(class_names = class_names)
@@ -87,9 +90,14 @@ interface = gr.Interface(
         gr.HTML(label="LIME Explanation"),
         gr.Plot(label = "Lime exp PLOTS")
     ],
-    title="Intent Classification using Transformers",
+    title="Intent Classification using Transformers Models- BERT, RoBERTa, DeBERTa",
     description="Choose a model, input a customer support query, and get intent predictions with a LIME explanation."
 )
 
 if __name__ == "__main__":
+    # to dummy test the models
+    for model_key in models_info.keys():
+        tokenizer, model, id2label = load_model_and_tokenizer(model_key)
+        _ = model(**tokenizer("warmup", return_tensors="tf", padding=True, truncation=True, max_length=128))
+
     interface.launch()