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import gradio as gr
from transformers import pipeline
get_completion = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
get_ner = pipeline("ner", model="dslim/bert-base-NER")
get_zero = pipeline(
"zero-shot-classification", model="MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli"
)
def summarize_text(input):
output = get_completion(input)
return output[0]["summary_text"]
def merge_tokens(tokens):
merged_tokens = []
for token in tokens:
if (
merged_tokens
and token["entity"].startswith("I-")
and merged_tokens[-1]["entity"].endswith(token["entity"][2:])
):
# If current token continues the entity of the last one, merge them
last_token = merged_tokens[-1]
last_token["word"] += token["word"].replace("##", "")
last_token["end"] = token["end"]
last_token["score"] = (last_token["score"] + token["score"]) / 2
else:
# Otherwise, add the token to the list
merged_tokens.append(token)
return merged_tokens
def named_entity_recognition(input):
output = get_ner(input)
merged_output = merge_tokens(output)
return {"text": input, "entities": output}
def zero_shot_pred(text, check_labels):
output = get_zero(text, check_labels)
return output
def label_score_dict(text, check_labels):
zero_shot_out = zero_shot_pred(text, check_labels)
out = {}
for i, j in zip(zero_shot_out["labels"], zero_shot_out["scores"]):
out.update({i: j})
print(out)
return out
interface_summarise = gr.Interface(
fn=summarize_text,
inputs=[gr.Textbox(label="Text to summarise", lines=5)],
outputs=[gr.Textbox(label="Summary")],
title="Text Summarizer",
description="Summary of text via `distillBART-CNN` model!",
)
interface_ner = gr.Interface(
fn=named_entity_recognition,
inputs=[gr.Textbox(label="Text to find entities", lines=2)],
outputs=[gr.HighlightedText(label="Text with entities")],
title="NER with dslim/bert-base-NER",
description="Find entities using the `dslim/bert-base-NER` model under the hood!",
allow_flagging="never",
examples=[
"Tim Cook is the CEO of Apple, stays in California and makes iPhones ",
"My name is Bose and I am a physicist living in Delhi",
],
)
interface_zero_shot = gr.Interface(
fn=label_score_dict,
inputs=[
gr.Textbox(label="Text to classify", lines=2),
gr.Textbox(label="Check for labels"),
],
outputs=gr.Label(num_top_classes=4),
title="Zero-Shot Preds using DeBERTa-v3-base-mnli",
description="Classify sentence on self defined target vars",
examples=[
[
"Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.",
"mobile, website, billing, account access",
],
# "My name is Bose and I am a physicist living in Delhi"
],
)
demo = gr.TabbedInterface(
[interface_summarise, interface_ner, interface_zero_shot],
["Text Summary ", "Named Entity Recognition", "Zero Shot Classifications"],
)
if __name__ == "__main__":
demo.launch(enable_queue=True)
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