Update app.py

app.py

@@ -16,7 +16,7 @@ dt_model = components["model"]
 unique_values = components["unique_values"]
 
 
-st.image("https://pbs.twimg.com/media/DywhyJiXgAIUZej?format=jpg&name=medium")
+st.image("https://i.ytimg.com/vi/WULwst0vW8g/maxresdefault.jpg")
 st.title("Income Prediction App")
 
 # Sidebar with input field descriptions
@@ -133,4 +133,40 @@ if st.button("Predict"):
 
     # Show prediction probability
     st.subheader("Prediction Probability")
-    st.write(f"The probability of the individual having an income over $50K is: {prediction_proba[0][1]:.2f}")
+    st.write(f"The probability of the individual having an income over $50K is: {prediction_proba[0][1]:.2f}")
+
+
+
+import streamlit as st
+import requests
+
+# Streamlit UI for user input
+st.title("Income Prediction App")
+st.sidebar.header("Enter User Information")
+
+# Create input fields for user input
+age = st.number_input("Age", min_value=0)
+gender = st.selectbox("Gender", ["Male", "Female"])
+# Include other input fields...
+
+# Button to trigger prediction
+if st.button("Predict"):
+    # Create a dictionary of user input
+    user_input = {
+        "age": age,
+        "gender": gender,
+        # Include other input fields...
+    }
+
+    # Send a POST request to the FastAPI server
+    response = requests.post("http://fastapi-server:8000/predict/", json=user_input)
+    
+    # Check if the request was successful
+    if response.status_code == 200:
+        prediction_data = response.json()
+        # Display prediction result to the user
+        st.subheader("Prediction Result")
+        st.write(f"Income: {prediction_data['income_prediction']}")
+        st.write(f"Prediction Probability: {prediction_data['prediction_probability']:.2f}")
+    else:
+        st.error("Error: Unable to get prediction")