Update app.py

app.py

@@ -6,16 +6,16 @@ import os
 auth_token = os.environ['HF_TOKEN']
 
 # Load the tokenizer and models for the first pipeline
-tokenizer_ext = AutoTokenizer.from_pretrained("AlGe/deberta-v3-large_token", token=auth_token)
-model_ext = AutoModelForTokenClassification.from_pretrained("AlGe/deberta-v3-large_token", token=auth_token)
+tokenizer_ = binAutoTokenizer.from_pretrained("AlGe/deberta-v3-large_token", token=auth_token)
+model_bin = AutoModelForTokenClassification.from_pretrained("AlGe/deberta-v3-large_token", token=auth_token)
 tokenizer_ext.model_max_length = 512
-pipe_ext = pipeline("ner", model=model_ext, tokenizer=tokenizer_ext)
+pipe_bin = pipeline("ner", model=model_bin, tokenizer=tokenizer_bin)
 
 # Load the tokenizer and models for the second pipeline
-tokenizer_ais = AutoTokenizer.from_pretrained("AlGe/deberta-v3-large_AIS-token", token=auth_token)
-model_ais = AutoModelForTokenClassification.from_pretrained("AlGe/deberta-v3-large_AIS-token", token=auth_token)
-tokenizer_ais.model_max_length = 512
-pipe_ais = pipeline("ner", model=model_ais, tokenizer=tokenizer_ais)
+tokenizer_ext = AutoTokenizer.from_pretrained("AlGe/deberta-v3-large_AIS-token", token=auth_token)
+model_ext = AutoModelForTokenClassification.from_pretrained("AlGe/deberta-v3-large_AIS-token", token=auth_token)
+tokenizer_ext.model_max_length = 512
+pipe_ext = pipeline("ner", model=model_ext, tokenizer=tokenizer_ext)
 
 # Load the tokenizer and models for the third pipeline
 model1 = AutoModelForSequenceClassification.from_pretrained("AlGe/deberta-v3-large_Int_segment", num_labels=1, token=auth_token)
@@ -65,21 +65,21 @@ def process_classification(text, model1, model2, tokenizer1):
     return f"{round(prediction1, 1)}", f"{round(prediction2, 1)}", f"{round(score, 2)}"
 
 def all(text):
-    return process_ner(text, pipe_ext), process_ner(text, pipe_ais), process_classification(text, model1, model2, tokenizer1)[0], process_classification(text, model1, model2, tokenizer1)[1], process_classification(text, model1, model2, tokenizer1)[2]
+    return process_ner(text, pipe_bin), process_ner(text, pipe_ext), process_classification(text, model1, model2, tokenizer1)[0], process_classification(text, model1, model2, tokenizer1)[1], process_classification(text, model1, model2, tokenizer1)[2]
 
 # Define Gradio interface
 iface = gr.Interface(
     fn=all,
     inputs=gr.Textbox(placeholder="Enter sentence here..."),
     outputs=[
-        gr.HighlightedText(label="NER - Extended Sequence Classification"),
-        gr.HighlightedText(label="NER - Autobiographical Interview Scoring"),
+        gr.HighlightedText(label="Binary Sequence Classification"),
+        gr.HighlightedText(label="Extended Sequence Classification"),
         gr.Label(label="Internal Detail Count"),
         gr.Label(label="External Detail Count"),
         gr.Label(label="Approximated Internal Detail Ratio")
     ],
-    title="Combined Demo",
-    description="This demo combines two different NER models and two different sequence classification models. Enter a sentence to see the results.",
+    title="Autobiographical Memory Scoring Demo",
+    description="Precision Memory Analysis: This demo combines two text - and two sequence classification models to showcase our automated Autobiographical Interview scoring Method. Enter a narrative to see the results.",
     theme="monochrome"
 )