Update app.py

app.py

@@ -1,16 +1,44 @@
 import gradio as gr
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
-model = AutoModelForSeq2SeqLM.from_pretrained("Mbilal755/Radiology_Bart") 
-tokenizer = AutoTokenizer.from_pretrained("Mbilal755/Radiology_Bart")
+model_checkpoint = "Mbilal755/Radiology_Bart"  
+model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
 
-def summarize(input):
-    inputs = tokenizer(input, return_tensors="pt")
-    output = model.generate(inputs["input_ids"]) 
-    summary = tokenizer.decode(output[0], skip_special_tokens=True)
-    return summary
+from transformers import SummarizationPipeline
+summarizer = SummarizationPipeline(model=model, tokenizer=tokenizer)
 
-iface = gr.Interface(fn=summarize, inputs="text", outputs="text")
+import gradio as gr
+
+examples = [
+  "prevoid bladder volume cc postvoid bladder volume cc bladder grossly normal appearance",
+  "heart mediastinal contours normal left sided subclavian line position tip distal svc lungs remain clear active disease effusions",
+  """
+  heart size normal mediastinal hilar contours remain stable small right pneumothorax remains unchanged surgical lung staples overlying
+  left upper lobe seen linear pattern consistent prior upper lobe resection soft tissue osseous structures appear unremarkable nasogastric
+  endotracheal tubes remain satisfactory position atelectatic changes right lower lung field remain unchanged prior study
+  """ 
+]
+
+description = """
+We fine-tuned the BioBart 440M parameter model on a dataset of 52,000 radiology reports scraped from MIMIC-III specifically for the task of summarization.
+
+The model is able to generate impressions summarizing key findings from the longer radiology reports. 
+
+Enter a radiology report to see the generated impression summary!
+"""
+
+def summarize(radiology_report):
+  summary = summarizer(radiology_report)[0]['summary_text']
+  return summary
 
+iface = gr.Interface(fn=summarize, 
+             inputs=gr.inputs.Textbox(lines=5, label="Radiology Report"),
+             outputs=gr.outputs.Textbox(label="Summary"),
+             examples=examples,
+             title="Radiology Report Summarization",
+             description=description,
+             theme="huggingface")
+             
 if __name__ == "__main__":
     iface.launch(share=False)