Update app.py

app.py

@@ -1,5 +1,7 @@
 import gradio as gr
 from transformers import pipeline
+import pandas as pd
+import os
 
 # Load the model
 classifier = pipeline(
@@ -7,26 +9,34 @@ classifier = pipeline(
     model="ashishkgpian/biobert_icd9_classifier_ehr"
 )
 
+# Load ICD9 codes data
+icd9_data = pd.read_csv('')
+
 def classify_symptoms(text):
     try:
         results = classifier(text, top_k=5)
         formatted_results = []
         for result in results:
+            code = result['label']
+            # Look up additional information
+            code_info = icd9_data[icd9_data['ICD9_CODE'] == code]
             formatted_results.append({
-                "ICD9 Code": result['label'], 
+                "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)}"
 
-# Enhanced CSS with better color contrast and readability
+# Enhanced CSS with violet theme and better text contrast
 custom_css = """
 .gradio-container {
     max-width: 1200px !important;
     margin: auto !important;
     padding: 2rem !important;
-    background-color: #f0f4f7 !important;
+    background-color: #f5f3f7 !important;
 }
 .main-container {
     text-align: center;
@@ -37,12 +47,12 @@ custom_css = """
     box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
 }
 h1 {
-    color: #2c3e50 !important;
+    color: #4a148c !important;
     font-size: 2.5rem !important;
     margin-bottom: 0.5rem !important;
 }
 h3 {
-    color: #34495e !important;
+    color: #6a1b9a !important;
     font-size: 1.2rem !important;
     font-weight: normal !important;
 }
@@ -55,15 +65,15 @@ h3 {
 }
 textarea {
     background: white !important;
-    color: #2c3e50 !important;
-    border: 2px solid #3498db !important;
+    color: #000000 !important;
+    border: 2px solid #7b1fa2 !important;
     border-radius: 8px !important;
     padding: 1rem !important;
     font-size: 1.1rem !important;
     min-height: 120px !important;
 }
 .submit-btn {
-    background-color: #2ecc71 !important;
+    background-color: #6a1b9a !important;
     color: white !important;
     padding: 0.8rem 2rem !important;
     border-radius: 8px !important;
@@ -72,7 +82,7 @@ textarea {
     transition: background-color 0.3s ease !important;
 }
 .submit-btn:hover {
-    background-color: #27ae60 !important;
+    background-color: #4a148c !important;
 }
 .output-container {
     background: white !important;
@@ -82,7 +92,7 @@ textarea {
 }
 .output-container pre {
     background: #f8f9fa !important;
-    color: #2c3e50 !important;
+    color: #000000 !important;
     border-radius: 8px !important;
     padding: 1rem !important;
 }
@@ -93,6 +103,9 @@ textarea {
     margin-top: 1rem !important;
     box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1) !important;
 }
+.example-text {
+    color: #000000 !important;
+}
 .footer {
     text-align: center;
     margin-top: 2rem;
@@ -100,7 +113,7 @@ textarea {
     background: white;
     border-radius: 10px;
     box-shadow: 0 2px 8px rgba(0, 0, 0, 0.1);
-    color: #2c3e50;
+    color: #4a148c;
 }
 """
 
@@ -124,7 +137,7 @@ with gr.Blocks(css=custom_css) as demo:
             
     with gr.Row(elem_classes=["output-container"]):
         output = gr.JSON(
-            label="Suggested ICD9 Diagnostic Codes"
+            label="Suggested ICD9 Diagnostic Codes with Descriptions"
         )
     
     with gr.Row(elem_classes=["examples-container"]):
@@ -136,7 +149,8 @@ with gr.Blocks(css=custom_css) as demo:
                 ["Elderly patient with chronic knee pain, reduced mobility, and signs of inflammation"]
             ],
             inputs=input_text,
-            label="Example Clinical Cases"
+            label="Example Clinical Cases",
+            elem_classes=["example-text"]
         )
     
     submit_btn.click(fn=classify_symptoms, inputs=input_text, outputs=output)