Update app.py

app.py

@@ -1,3 +1,49 @@
+import gradio as gr
+from transformers import pipeline
+import pandas as pd
+import os
+
+# Load the model
+classifier = pipeline(
+    "text-classification", 
+    model="ashishkgpian/biobert_icd9_classifier_ehr"
+)
+
+# Load ICD9 codes data
+icd9_data = pd.read_csv('D_ICD_DIAGNOSES.csv')
+icd9_data.columns = ['ROW_ID', 'ICD9_CODE', 'SHORT_TITLE', 'LONG_TITLE']
+
+def preprocessing(test_df):
+    test_df.loc[
+        test_df['ICD9_CODE'].str.startswith("V"), 'ICD9_CODE'] = test_df.ICD9_CODE.apply(
+        lambda x: x[:4])
+    test_df.loc[
+        test_df['ICD9_CODE'].str.startswith("E"), 'ICD9_CODE'] = test_df.ICD9_CODE.apply(
+        lambda x: x[:4])
+    test_df.loc[(~test_df.ICD9_CODE.str.startswith("E")) & (
+        ~test_df.ICD9_CODE.str.startswith("V")), 'ICD9_CODE'] = test_df.ICD9_CODE.apply(
+        lambda x: x[:3])
+    return test_df
+
+icd9_data = preprocessing(icd9_data)
+
+def classify_symptoms(text):
+    try:
+        results = classifier(text, top_k=5)
+        formatted_results = []
+        for result in results:
+            code = result['label']
+            code_info = icd9_data[icd9_data['ICD9_CODE'] == code]
+            formatted_results.append({
+                "ICD9 Code": code,
+                "Short Title": code_info['SHORT_TITLE'].iloc[0] if not code_info.empty else "N/A",
+                "Long Title": code_info['LONG_TITLE'].iloc[0] if not code_info.empty else "N/A",
+                "Confidence": f"{result['score']:.2%}"
+            })
+        return formatted_results
+    except Exception as e:
+        return f"Error processing classification: {str(e)}"
+
 custom_css = """
 .gradio-container {
     width: 100% !important;