Update app.py

app.py

@@ -78,19 +78,21 @@ st.title("🎗️ Cancer Prediction Using Machine Learning 🎗️")
 st.markdown("""<style>.big-font {font-size:20px !important;}</style>
 <p class="big-font">Provide patient details below to predict cancer presence:</p>""", unsafe_allow_html=True)
 
+# Updated controls with radio buttons or buttons
 col1, col2 = st.columns(2)
+
 with col1:
-    age = st.slider("Age", 18, 100, 30)
-    tumor_size = st.slider("Tumor Size", 1.0, 10.0, 5.0)
-    tumor_grade = st.selectbox("Tumor Grade", [1, 2, 3])
-    symptoms_severity = st.selectbox("Symptoms Severity", [1, 2, 3])
+    age = st.radio("Age", [18, 20, 30, 40, 50, 60, 70, 80, 90, 100])
+    tumor_size = st.radio("Tumor Size", [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0])
+    tumor_grade = st.radio("Tumor Grade", ['High', 'Low', 'Medium'])
+    symptoms_severity = st.radio("Symptoms Severity", ['Mild', 'Moderate', 'Severe'])
 
 with col2:
-    smoking_history = st.selectbox("Smoking History", [0, 1, 2])
-    alcohol_consumption = st.selectbox("Alcohol Consumption", [0, 1, 2, 3])
-    exercise_frequency = st.selectbox("Exercise Frequency", [0, 1, 2, 3])
-    gender = st.selectbox("Gender", [0, 1])
-    family_history = st.selectbox("Family History", [0, 1])
+    smoking_history = st.radio("Smoking History", ['Never Smoker', 'Former Smoker', 'Current Smoker'])
+    alcohol_consumption = st.radio("Alcohol Consumption", ['Low', 'Moderate', 'High'])
+    exercise_frequency = st.radio("Exercise Frequency", ['Rarely', 'Occasionally', 'Regularly', 'Never'])
+    gender = st.radio("Gender", ['Male', 'Female'])
+    family_history = st.radio("Family History", ["No", "Yes"])
 
 input_data = [[age, tumor_size, tumor_grade, symptoms_severity, smoking_history, 
                alcohol_consumption, exercise_frequency, gender, family_history]]
@@ -104,4 +106,4 @@ if st.button("Predict Cancer Presence"):
         prediction = model.named_steps['classifier'].predict(input_transformed)
         st.markdown(f"**Prediction Result: {'🟥 Positive' if prediction[0] == 1 else '🟩 Negative'}**")
     else:
-        st.error("Please train a model first!")
+        st.error("Please train a model first!")