Update app.py

app.py

@@ -11,6 +11,16 @@ from sklearn.svm import SVC
 from sklearn.linear_model import LogisticRegression
 from sklearn.neighbors import KNeighborsClassifier
 
+# Set dark theme and page config
+st.set_page_config(page_title="🎗️Cancer Prediction🎗️", page_icon="🩺", layout="centered")
+st.markdown("""
+    <style>
+    body { background-color: #121212; color: white; }
+    .stButton>button { background-color: #ff4b4b; color: white; }
+    .stSelectbox, .stRadio, .stNumberInput, .stSlider { color: white; }
+    </style>
+""", unsafe_allow_html=True)
+
 # Load dataset
 def load_data():
     df = pd.read_csv('cancer_prediction_data (2).csv')
@@ -22,7 +32,6 @@ def preprocess_data(df):
     ordinal = ['Tumor_Grade', 'Symptoms_Severity', 'Alcohol_Consumption', 'Exercise_Frequency']
     nominal = ['Gender', 'Family_History', 'Smoking_History']
     
-    # Pipelines
     numeric_preprocess = Pipeline([
         ('imputer', SimpleImputer(strategy='mean')),
         ('scaler', StandardScaler())
@@ -36,7 +45,6 @@ def preprocess_data(df):
         ('encoder', OneHotEncoder(sparse_output=False, handle_unknown='ignore'))
     ])
     
-    # Column Transformer
     preprocess = ColumnTransformer([
         ('num', numeric_preprocess, numeric),
         ('ord', ordinal_preprocess, ordinal),
@@ -66,45 +74,45 @@ def train_model(X_train, y_train, preprocess, model_name):
     return pipeline
 
 # Streamlit UI
-st.markdown("<h1 style='text-align: center; color: #1976D2;'>🎗️ Cancer Prediction Using ML 🎗️</h1>", unsafe_allow_html=True)
+st.markdown("<h1 style='text-align: center; color: white;'>🩺 Cancer Prediction 🩺</h1>", unsafe_allow_html=True)
+st.write("An intelligent system for early cancer detection using machine learning.")
 
 df = load_data()
 X_train, X_test, y_train, y_test, preprocess = preprocess_data(df)
 
-model_name = st.radio("Select Model", ['Decision Tree', 'SVM', 'Logistic Regression', 'KNN'])
+st.subheader("🔬 Choose a Machine Learning Model")
+model_name = st.selectbox("", ['Decision Tree', 'SVM', 'Logistic Regression', 'KNN'])
 
-if st.button("Train Model"):
+if st.button("🚀 Train & Evaluate Model"):
     model = train_model(X_train, y_train, preprocess, model_name)
     accuracy = model.score(X_test, y_test)
-    st.write(f"Model Accuracy: {accuracy:.2f}")
+    st.success(f"Model trained successfully! Accuracy: {accuracy:.2f}")
     st.session_state['trained_model'] = model
-    st.success("Model trained successfully!")
 
 # Prediction Section
-st.markdown("<h2 style='color: #D32F2F;'>🔍 Make a Prediction</h2>", unsafe_allow_html=True)
+st.markdown("<h2 style='color: red;'>🔍 Make a Prediction</h2>", unsafe_allow_html=True)
+age = st.number_input("📅 Age", min_value=18, max_value=100, value=30)
+tumor_size = st.number_input("🧬 Tumor Size (cm)", min_value=1.0, max_value=10.0, value=5.0)
+smoking_history = st.radio("🚬 Smoking History", ['Non-Smoker', 'Former Smoker', 'Current Smoker'])
+alcohol_consumption = st.selectbox("🍷 Alcohol Consumption", ['None', 'Low', 'Moderate', 'High'])
 
-age = st.number_input("Age", min_value=18, max_value=100, value=30)
-tumor_size = st.number_input("Tumor Size", min_value=1.0, max_value=10.0, value=5.0)
-tumor_grade = st.radio("Tumor Grade", [1, 2, 3])
-symptoms_severity = st.radio("Symptoms Severity", [1, 2, 3])
-smoking_history = st.radio("Smoking History", [0, 1, 2])
-alcohol_consumption = st.radio("Alcohol Consumption", [0, 1, 2, 3])
-exercise_frequency = st.radio("Exercise Frequency", [0, 1, 2, 3])
-gender = st.radio("Gender", [0, 1])
-family_history = st.radio("Family History", [0, 1])
+tumor_grade = st.selectbox("Tumor Grade", [1, 2, 3])
+symptoms_severity = st.selectbox("Symptoms Severity", [1, 2, 3])
+exercise_frequency = st.selectbox("Exercise Frequency", ['Never', 'Rarely', 'Occasionally', 'Regularly'])
+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]]
 
-if st.button("Predict Cancer Presence"):
+if st.button("🔮Predict Cancer Presence"):
     if 'trained_model' in st.session_state:
         model = st.session_state['trained_model']
         input_df = pd.DataFrame(input_data, columns=X_train.columns)
-        
-        # Transform input data using the same preprocessor
         input_transformed = model.named_steps['preprocessor'].transform(input_df)
         prediction = model.named_steps['classifier'].predict(input_transformed)
-
-        st.write("**Cancer Prediction:**", "**🟥Positive**" if prediction[0] == 1 else "**🟩Negative**")
+        
+        st.write("Cancer Prediction:", "✅ Positive" if prediction[0] == 1 else "❌ Negative")
     else:
-        st.error("Please train a model first!")
+        st.error("Please train a model first!")
+