Update app.py

app.py

@@ -1,5 +1,6 @@
 import streamlit as st
 import numpy as np
+import tensorflow
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 from tensorflow.keras.models import load_model
 from tensorflow.keras.preprocessing.text import Tokenizer
@@ -17,7 +18,7 @@ def preprocess(text):
 
 # Load Keras models
 model1 = load_model("model1.h5")  # Suicide risk
-model2 = load_model("model2.h5")  # Diagnosis classifier
+model2 = load_model("best_model (2).keras")  # Diagnosis classifier
 
 # Model prediction wrappers
 def model1_predict(text):
@@ -63,7 +64,7 @@ def therapist_pipeline(user_input):
     if risk == 1:
         response = (
             "I'm really sorry you're feeling this way. You're not alone — please talk to someone you trust "
-            "or a professional. I'm here to listen, but it's important to get real support too. 💙"
+            "or a professional. I'm here to listen, but it's important to get real support too. Please contact 9-8-8 if you need immediate support. I hope you get better. 💙"
         )
     else:
         diagnosis_code = model2_predict(user_input)