Create app.py

app.py

@@ -0,0 +1,38 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load the token classification model
+pipe = pipeline("token-classification", model="Clinical-AI-Apollo/Medical-NER", aggregation_strategy='simple')
+
+def classify_text(text):
+    # Get token classification results
+    result = pipe(text)
+    
+    # Format the results to resemble the UI shown in the image
+    formatted_output = ""
+    for res in result:
+        entity = res['entity_group']
+        word = res['word']
+        score = res['score']
+        start = res['start']
+        end = res['end']
+        formatted_output += f"Entity: {entity}, Word: {word}, Score: {score:.4f}, Span: [{start}:{end}]\n"
+    
+    return formatted_output
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=classify_text,
+    inputs=gr.Textbox(lines=5, label="Enter Medical Text"),
+    outputs=gr.Textbox(label="Entity Classification"),
+    title="Medical Entity Classification",
+    description="Enter medical-related text, and the model will classify medical entities.",
+    examples=[
+        ["45 year old woman diagnosed with CAD"],
+        ["A 65-year-old male presents with acute chest pain and a history of hypertension."],
+        ["The patient underwent a laparoscopic cholecystectomy."]
+    ]
+)
+
+if __name__ == "__main__":
+    demo.launch()