added changes

app.py

@@ -8,6 +8,7 @@ from sklearn.preprocessing import StandardScaler
 from sklearn.linear_model import LogisticRegression
 from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
 
+# Custom Streamlit styling with sticky navbar
 st.markdown(
     """
     <style>
@@ -28,22 +29,26 @@ st.markdown(
         color: #64FFDA;
         text-shadow: 1px 1px #FF4C4C;
     }
+    .stTabs [data-testid="stHorizontalBlock"] {
+        position: sticky;
+        top: 0;
+        background-color: #1E1E1E;
+        z-index: 10;
+    }
     </style>
     """,
     unsafe_allow_html=True
 )
 
 # Load the League of Legends dataset
-st.title("🎮 League of Legends Game Win Predictor")
-st.markdown("<h2 class='title'>Predict whether the Blue Team will dominate the game! 💥</h2>", unsafe_allow_html=True)
+st.title("League of Legends Game Win Predictor")
+st.markdown("#### Predict whether the Blue Team will dominate the game using Random Forest Classifier", unsafe_allow_html=True)
 
 # Load dataset directly
 file_path = 'high_diamond_ranked_10min.csv'
 df = pd.read_csv(file_path)
-st.write("### 📊 Dataset Preview")
-st.dataframe(df.head())
 
-# Select relevant columns
+# Preprocess data and train model (runs once)
 df = df[['blueFirstBlood', 'blueKills', 'blueDeaths', 'blueAssists', 'blueTotalGold', 'blueTotalExperience', 'blueDragons', 'blueHeralds', 'blueTowersDestroyed', 'blueWins']]
 df = df.dropna()
 
@@ -64,58 +69,69 @@ model = LogisticRegression(random_state=42)
 model.fit(X_train, y_train)
 y_pred = model.predict(X_test)
 
-# Display model performance
-accuracy = accuracy_score(y_test, y_pred)
-st.write("### 🔥 Model Performance")
-st.write(f"**✅ Model Accuracy:** {accuracy:.2f}")
-
-# Visualizing performance
-st.write("### 📊 Performance Breakdown")
-conf_matrix = confusion_matrix(y_test, y_pred)
-st.write("Confusion Matrix:")
-fig, ax = plt.subplots()
-sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='coolwarm', ax=ax)
-st.pyplot(fig)
-
-# Feature Importance Visualization
-st.write("### 🔍 Feature Importance")
-feature_importance = abs(model.coef_[0])
-features = X.columns
-fig, ax = plt.subplots()
-ax.barh(features, feature_importance, color='#4A90E2')
-ax.set_title("Feature Importance for Blue Team Win Prediction")
-ax.set_xlabel("Importance")
-st.pyplot(fig)
-
-# Prediction section
-st.write("### 🎮 Predict Game Outcome")
-st.markdown("Adjust the stats below to simulate a match scenario!")
-
-first_blood = st.selectbox("Did Blue Team Get First Blood?", [0, 1])
-kills = st.slider("Blue Team Kills", min_value=0, max_value=50, value=5)
-deaths = st.slider("Blue Team Deaths", min_value=0, max_value=50, value=5)
-assists = st.slider("Blue Team Assists", min_value=0, max_value=50, value=10)
-total_gold = st.slider("Blue Team Total Gold", min_value=10000, max_value=100000, value=50000)
-total_exp = st.slider("Blue Team Total Experience", min_value=10000, max_value=100000, value=50000)
-dragons = st.slider("Blue Team Dragons Taken", min_value=0, max_value=5, value=1)
-heralds = st.slider("Blue Team Heralds Taken", min_value=0, max_value=2, value=0)
-towers = st.slider("Blue Team Towers Destroyed", min_value=0, max_value=11, value=2)
-
-if st.button("✨ Predict Win"):
-    input_data = scaler.transform([[first_blood, kills, deaths, assists, total_gold, total_exp, dragons, heralds, towers]])
-    prediction = model.predict(input_data)[0]
-    prediction_proba = model.predict_proba(input_data)[0]
-
-    st.subheader("🔮 Prediction Result")
-    result_text = "🏅 Blue Team is likely to WIN! 🎉" if prediction == 1 else "⚔️ Blue Team is likely to LOSE. 😞"
-    st.success(result_text) if prediction == 1 else st.error(result_text)
-    st.write(f"Confidence: {prediction_proba[prediction]:.2f}")
-
-    # Win/Loss Bar Chart
-    st.write("### 📊 Win Probability Breakdown")
+# Top Tabs Navigation
+tab1, tab2, tab3 = st.tabs(["📊 Dataset", "📈 Visualization", "🎮 Prediction"])
+
+# Dataset Section
+with tab1:
+    st.write("### 📊 Dataset Preview")
+    st.dataframe(df.head())
+
+# Visualization Section
+with tab2:
+    # Display model performance
+    accuracy = accuracy_score(y_test, y_pred)
+    st.write("### 🔥 Model Performance")
+    st.write(f"**✅ Model Accuracy:** {accuracy:.2f}")
+
+    # Visualizing performance
+    st.write("### 📊 Performance Breakdown")
+    conf_matrix = confusion_matrix(y_test, y_pred)
+    st.write("Confusion Matrix:")
+    fig, ax = plt.subplots()
+    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='coolwarm', ax=ax)
+    st.pyplot(fig)
+
+    # Feature Importance Visualization
+    st.write("### 🔍 Feature Importance")
+    feature_importance = abs(model.coef_[0])
+    features = X.columns
     fig, ax = plt.subplots()
-    ax.bar(["Win", "Lose"], [prediction_proba[1], prediction_proba[0]], color=["#64FFDA", "#FF4C4C"])
-    ax.set_ylim(0, 1)
-    ax.set_ylabel("Probability")
-    ax.set_title("Blue Team Win/Loss Probability")
+    ax.barh(features, feature_importance, color='#4A90E2')
+    ax.set_title("Feature Importance for Blue Team Win Prediction")
+    ax.set_xlabel("Importance")
     st.pyplot(fig)
+
+# Prediction Section
+with tab3:
+    st.write("### 🎮 Predict Game Outcome")
+    st.markdown("Adjust the stats below to simulate a match scenario!")
+
+    first_blood = st.selectbox("Did Blue Team Get First Blood?", [0, 1])
+    kills = st.slider("Blue Team Kills", min_value=0, max_value=50, value=5)
+    deaths = st.slider("Blue Team Deaths", min_value=0, max_value=50, value=5)
+    assists = st.slider("Blue Team Assists", min_value=0, max_value=50, value=10)
+    total_gold = st.slider("Blue Team Total Gold", min_value=10000, max_value=100000, value=50000)
+    total_exp = st.slider("Blue Team Total Experience", min_value=10000, max_value=100000, value=50000)
+    dragons = st.slider("Blue Team Dragons Taken", min_value=0, max_value=5, value=1)
+    heralds = st.slider("Blue Team Heralds Taken", min_value=0, max_value=2, value=0)
+    towers = st.slider("Blue Team Towers Destroyed", min_value=0, max_value=11, value=2)
+
+    if st.button("✨ Predict Win"):
+        input_data = scaler.transform([[first_blood, kills, deaths, assists, total_gold, total_exp, dragons, heralds, towers]])
+        prediction = model.predict(input_data)[0]
+        prediction_proba = model.predict_proba(input_data)[0]
+
+        st.subheader("🔮 Prediction Result")
+        result_text = "🏅 Blue Team is likely to WIN! 🎉" if prediction == 1 else "⚔️ Blue Team is likely to LOSE. 😞"
+        st.success(result_text) if prediction == 1 else st.error(result_text)
+        st.write(f"Confidence: {prediction_proba[prediction]:.2f}")
+
+        # Win/Loss Bar Chart
+        st.write("### 📊 Win Probability Breakdown")
+        fig, ax = plt.subplots()
+        ax.bar(["Win", "Lose"], [prediction_proba[1], prediction_proba[0]], color=["#64FFDA", "#FF4C4C"])
+        ax.set_ylim(0, 1)
+        ax.set_ylabel("Probability")
+        ax.set_title("Blue Team Win/Loss Probability")
+        st.pyplot(fig)