Update app.py

app.py

@@ -43,6 +43,82 @@ def abstractive_text(text):
 
 
 import gradio as gr
-iface = gr.Interface(fn=abstractive_text, inputs= ["text"],outputs=["text"],title="Case summary generation")
-iface.launch(share=False)
+sum_iface = gr.Interface(fn=abstractive_text, inputs= ["text"],outputs=["text"],title="Case summary generation")
+sum_iface.launch(share=False)
+
+
+
+import transformers
+from transformers import BloomForCausalLM
+from transformers import BloomTokenizerFast
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import gradio as gr
+
+tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-560m")
+
+model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-560m")
+
+def get_result_with_bloom(text):
+  result_length = 200
+  inputs1 = tokenizer(text, return_tensors="pt")
+  output1 = tokenizer.decode(model.generate(inputs1["input_ids"],
+                       max_length=result_length, 
+                       num_beams=2, 
+                       no_repeat_ngram_size=2,
+                       early_stopping=True
+                      )[0])
+  return output1
+
+
+
+
+txtgen_iface = gr.Interface(fn=get_result_with_bloom,inputs = "text",outputs=["text"],title="Text generation with Bloom")
+txtgen_iface.launch(share=True)
+
+
+import spacy.cli
+import en_core_med7_lg
+import spacy
+import gradio as gr
+
+spacy.cli.download("en_core_web_lg")
+
+
+med7 = en_core_med7_lg.load()
+
+# create distinct colours for labels
+col_dict = {}
+seven_colours = ['#e6194B', '#3cb44b', '#ffe119', '#ffd8b1', '#f58231', '#f032e6', '#42d4f4']
+for label, colour in zip(med7.pipe_labels['ner'], seven_colours):
+    col_dict[label] = colour
+
+options = {'ents': med7.pipe_labels['ner'], 'colors':col_dict}
+
+#text = 'A patient was prescribed Magnesium hydroxide 400mg/5ml suspension PO of total 30ml bid for the next 5 days.'
+def ner_drugs(text):
+  doc = med7(text)
+
+  spacy.displacy.render(doc, style='ent', jupyter=True, options=options)
+
+  return [(ent.text, ent.label_) for ent in doc.ents]
+
+
+
+med_iface = gr.Interface(fn=ner_drugs,inputs = "text",outputs=["text"],title="Drugs Named Entity Recognition")
+med_iface.launch(share=True)
+
+
+
+demo = gr.TabbedInterface(
+
+    [txtgen_iface, sum_iface, med_iface], ["Text Generation", "Summary Generation", "Named-entity recognition"],
+    title=title,
+
+)
+
+demo.queue()
+demo.launch(share=False)
+
+