create app.py

app.py

@@ -0,0 +1,56 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load the model
+classifier = pipeline(
+    "text-classification", 
+    model="ashishkgpian/biolinkbert_base_icd9_classifier_ehr_symptoms_text_icd9_150_epochs"
+)
+
+def classify_symptoms(text):
+    """
+    Classify medical symptoms and return top ICD9 codes
+    
+    Args:
+        text (str): Input medical symptom description
+    
+    Returns:
+        dict: Top classification results with ICD9 codes and probabilities
+    """
+    try:
+        # Get classification results
+        results = classifier(text, top_k=5)
+        
+        # Format results for more readable output
+        formatted_results = []
+        for result in results:
+            formatted_results.append({
+                "ICD9 Code": result['label'], 
+                "Confidence": f"{result['score']:.2%}"
+            })
+        
+        return formatted_results
+    
+    except Exception as e:
+        return f"Error processing classification: {str(e)}"
+
+# Create Gradio interface
+demo = gr.Interface(
+    fn=classify_symptoms,
+    inputs=gr.Textbox(
+        label="Enter Medical Symptoms", 
+        placeholder="Describe patient symptoms here..."
+    ),
+    outputs=gr.JSON(label="Top 5 ICD9 Classifications"),
+    title="BioBERT ICD9 Symptom Classifier",
+    description="Classify medical symptoms into ICD9 diagnostic codes using a fine-tuned BioBERT model.",
+    theme="huggingface",
+    examples=[
+        ["Patient experiencing chest pain and shortness of breath"],
+        ["Recurring headaches with nausea"],
+        ["Diabetic symptoms including frequent urination"]
+    ]
+)
+
+if __name__ == "__main__":
+    demo.launch()