Update app.py

app.py

@@ -7,10 +7,10 @@ def predict_mode():
     st.title("📊 Predict Mode: Salary Prediction")
     
     # Input fields for prediction
-    gender = st.selectbox("Gender", ["m", "f"])
-    perc_10 = st.number_input("10th Percentage", min_value=0.0, max_value=100.0, value=80.0)
-    perc_12 = st.number_input("12th Percentage", min_value=0.0, max_value=100.0, value=80.0)
-    tier = st.selectbox("College Tier (Only 1 or 2 accepted)", [1, 2])
+    gender = st.selectbox("Gender 🚻", ["m", "f"])
+    perc_10 = st.number_input("10th Percentage ⑩", min_value=0.0, max_value=100.0, value=80.0)
+    perc_12 = st.number_input("12th Percentage ⑫", min_value=0.0, max_value=100.0, value=80.0)
+    tier = st.selectbox("College Tier (Only 1 or 2 accepted) 🏛️", [1, 2])
     
     specializations = [
         'electronics & instrumentation',
@@ -39,9 +39,9 @@ def predict_mode():
         'mechatronics',
         'information & communication technology'
     ]
-    specialization = st.selectbox("Specialization", specializations)
+    specialization = st.selectbox("Specialization ⚙️", specializations)
     
-    cgpa_input = st.number_input("College GPA (0-10 scale)", min_value=0.0, max_value=10.0, value=7.5)
+    cgpa_input = st.number_input("College GPA (0-10 scale 📝)", min_value=0.0, max_value=10.0, value=7.5)
     college_gpa = cgpa_input * 10  # Scale up to 100
     
     locations = [
@@ -50,7 +50,7 @@ def predict_mode():
         "Punjab", "Madhya Pradesh", "Uttarakhand", "Gujarat", "Jharkhand", "Himachal Pradesh", 
         "Bihar", "Kerala", "Assam", "Jammu and Kashmir", "Sikkim", "Meghalaya", "Goa"
     ]
-    location = st.selectbox("Location", locations)
+    location = st.selectbox("Location 📍", locations)
     
     # Prepare input DataFrame (ensure column names match training data)
     input_df = pd.DataFrame({