Update app.py

app.py

@@ -1,11 +1,28 @@
 # Use a pipeline as a high-level helper
 from transformers import pipeline
 import streamlit as st
+import streamlit.components.v1 as components
 
 pipe_1 = pipeline("text-classification", model="mavinsao/mi-roberta-base-finetuned-mental-illness")
 pipe_2 = pipeline("text-classification", model="mavinsao/roberta-mental-finetuned")
 
 
+# Streamlit app with background image
+def st_display_background(image_url):
+    st_style = f"""
+            <style>
+            body {{
+                background-image: url("{image_url}");
+                background-size: cover;
+            }}
+            </style>
+            """
+    st.markdown(st_style, unsafe_allow_html=True)
+
+image_url = "https://images.unsplash.com/photo-1504701954957-2010ec3bcec1?q=80&w=1974&auto=format&fit=crop&ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D"
+st_display_background(image_url)
+
+
 def ensemble_predict(text): 
     # Store results from each model
     results_1 = pipe_1(text)