Update app.py

app.py

@@ -160,11 +160,20 @@ def predict_icd(text_input, model_name, label_count):
     label_probabilities = {label_map[i]: prob * 100 for i, prob in enumerate(probabilities[0]) if label_map[i] in predicted_labels}
     y_pred = np.array(predicted_labels)
     y_pred = "\n".join(y_pred)
+    label_dict = label_dict.set_index('icd9_code')['long_title'].to_dict()
+    predicted_labels_with_titles = [(label, label_dict[label]) for label in predicted_labels]
     
+    # html_output ="<h2>ICD Codes</h2>"
+    # for label in predicted_labels:
+    #     color = hash_to_color(label)
+    #     html_output += f"<div style='background-color: {color}; padding: 5px; margin-bottom: 5px;'>{label} <span style='float:right;'>{label_probabilities[label]:.2f}%</span></div>"
+
     html_output ="<h2>ICD Codes</h2>"
-    for label in predicted_labels:
+    for label, title in predicted_labels_with_titles:
         color = hash_to_color(label)
-        html_output += f"<div style='background-color: {color}; padding: 5px; margin-bottom: 5px;'>{label} <span style='float:right;'>{label_probabilities[label]:.2f}%</span></div>"
+        html_output += f"<div style='background-color: {color}; padding: 5px; margin-bottom: 5px;'>{label} - {title} <span style='float:right;'>{label_probabilities[label]:.2f}%</span></div>"
+
+
     
     return html_output