Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+
+model_checkpoint = "Shubham555/biobert-finetuned-ner"
+token_classifier = pipeline("token-classification", model=model_checkpoint, aggregation_strategy="simple")
+
+
+examples = [
+    ["Clustering of missense mutations in the ataxia - telangiectasia gene in a sporadic T - cell leukaemia."],
+    ["Ataxia - telangiectasia ( A - T ) is a recessive multi - system disorder caused by mutations in the ATM gene at 11q22 - q23 ( ref . 3 )."],
+    ["The risk of cancer , especially lymphoid neoplasias , is substantially elevated in A - T patients and has long been associated with chromosomal instability."],
+    ["These clustered in the region corresponding to the kinase domain , which is highly conserved in ATM - related proteins in mouse , yeast and Drosophila."],
+    ["Constitutional RB1 - gene mutations in patients with isolated unilateral retinoblastoma ."],
+    ["The evidence of a significant proportion of loss - of - function mutations and a complete absence of the normal copy of ATM in the majority of mutated tumours establishes somatic inactivation of this gene in the pathogenesis of sporadic T - PLL and suggests that ATM acts as a tumour suppressor."],
+]
+
+
+def ner(text):
+    output = token_classifier(text)
+    for hmap in output:
+      hmap['entity'] = hmap['entity_group']
+      del hmap['entity_group']
+    return {"text": text, "entities": output}    
+
+demo = gr.Interface(ner,
+             gr.Textbox(placeholder="Enter sentence here..."), 
+             gr.HighlightedText(),
+             examples=examples,
+             allow_flagging = 'never',
+             title="Named Entity Recognition for Disease Identification",
+             description="The app uses BioBERT finetuned on NCBI Dataset and can be used to detect the name of diseases appearing in the given text")
+
+demo.launch()