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import torch
import gradio as gr
from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModelForTokenClassification
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_ext.model_max_length = 512
pipe_ext = gr.pipeline("ner", model=model_ext, tokenizer=tokenizer_ext)
# 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 = gr.pipeline("ner", model=model_ais, tokenizer=tokenizer_ais)
# 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)
tokenizer1 = AutoTokenizer.from_pretrained("AlGe/deberta-v3-large_Int_segment", token=auth_token)
model2 = AutoModelForSequenceClassification.from_pretrained("AlGe/deberta-v3-large_seq_ext", num_labels=1, token=auth_token)
# Define functions to process inputs
def process_ner(text, pipeline):
output = pipeline(text)
entities = []
current_entity = None
for token in output:
entity_type = token['entity'][2:]
entity_prefix = token['entity'][:1]
if current_entity is None or entity_type != current_entity['entity'] or (entity_prefix == 'B' and entity_type == current_entity['entity']):
if current_entity is not None:
entities.append(current_entity)
current_entity = {
"entity": entity_type,
"start": token['start'],
"end": token['end'],
"score": token['score']
}
else:
current_entity['end'] = token['end']
current_entity['score'] = max(current_entity['score'], token['score'])
if current_entity is not None:
entities.append(current_entity)
return {"text": text, "entities": entities}
def process_classification(text, model1, model2, tokenizer1):
inputs1 = tokenizer1(text, max_length=512, return_tensors='pt', truncation=True, padding=True)
with torch.no_grad():
outputs1 = model1(**inputs1)
outputs2 = model2(**inputs1)
prediction1 = outputs1[0].item()
prediction2 = outputs2[0].item()
score = prediction1 / (prediction2 + prediction1)
return f"{round(prediction1, 1)}", f"{round(prediction2, 1)}", f"{round(score, 2)}"
# Define Gradio interface
iface = gr.Interface(
fn={
"NER - Extended Sequence Classification": lambda text: process_ner(text, pipe_ext),
"NER - Autobiographical Interview Scoring": lambda text: process_ner(text, pipe_ais),
"Internal Detail Count": lambda text: process_classification(text, model1, model2, tokenizer1)[0],
"External Detail Count": lambda text: process_classification(text, model1, model2, tokenizer1)[1],
"Approximated Internal Detail Ratio": lambda text: process_classification(text, model1, model2, tokenizer1)[2]
},
inputs=gr.Textbox(placeholder="Enter sentence here..."),
outputs=[
gr.HighlightedText(label="NER - Extended Sequence Classification"),
gr.HighlightedText(label="NER - Autobiographical Interview Scoring"),
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.",
theme="monochrome"
)
# Launch the combined interface
iface.launch()
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