Update app.py

app.py

@@ -4,26 +4,32 @@ import numpy as np
 from sklearn.model_selection import train_test_split
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.preprocessing import StandardScaler
-import joblib
+import plotly.express as px
+import plotly.graph_objects as go
 
 # Load and preprocess data
-@st.cache_data
 def load_data():
-    # Replace this with your actual data loading method
-    data = pd.read_csv('employee_performance_data.csv')
+    # For this example, we'll generate synthetic data
+    np.random.seed(42)
+    data = pd.DataFrame({
+        'employee_id': range(1000),
+        'age': np.random.randint(22, 65, 1000),
+        'experience': np.random.randint(0, 40, 1000),
+        'training_score': np.random.uniform(50, 100, 1000),
+        'project_completion': np.random.uniform(60, 100, 1000),
+        'performance_score': np.random.uniform(50, 100, 1000)
+    })
     return data
 
 # Train model
-@st.cache_resource
 def train_model(data):
-    X = data.drop('performance_score', axis=1)
+    X = data.drop(['employee_id', 'performance_score'], axis=1)
     y = data['performance_score']
     
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
     
     scaler = StandardScaler()
     X_train_scaled = scaler.fit_transform(X_train)
-    X_test_scaled = scaler.transform(X_test)
     
     model = RandomForestRegressor(n_estimators=100, random_state=42)
     model.fit(X_train_scaled, y_train)
@@ -32,18 +38,32 @@ def train_model(data):
 
 # Streamlit app
 def main():
-    st.title('Employee Performance Prediction')
+    st.title('Employee Performance Analysis Dashboard')
     
     data = load_data()
     model, scaler = train_model(data)
     
-    st.sidebar.header('Input Parameters')
+    st.sidebar.header('Employee Details')
+    
+    # Input for employee ID
+    employee_id = st.sidebar.number_input('Employee ID', min_value=1, max_value=1000, value=1)
+    
+    # Get employee data
+    employee = data[data['employee_id'] == employee_id].iloc[0]
+    
+    # Display current employee details
+    st.sidebar.subheader('Current Details')
+    st.sidebar.write(f"Age: {employee['age']}")
+    st.sidebar.write(f"Experience: {employee['experience']} years")
+    st.sidebar.write(f"Training Score: {employee['training_score']:.2f}")
+    st.sidebar.write(f"Project Completion: {employee['project_completion']:.2f}%")
     
-    # Create input fields for user
-    age = st.sidebar.slider('Age', 18, 65, 30)
-    experience = st.sidebar.slider('Years of Experience', 0, 40, 5)
-    training_score = st.sidebar.slider('Training Score', 0.0, 100.0, 75.0)
-    project_completion = st.sidebar.slider('Project Completion Rate', 0.0, 100.0, 80.0)
+    # Input fields for updating employee details
+    st.sidebar.subheader('Update Details')
+    age = st.sidebar.number_input('Age', min_value=22, max_value=65, value=int(employee['age']))
+    experience = st.sidebar.number_input('Years of Experience', min_value=0, max_value=40, value=int(employee['experience']))
+    training_score = st.sidebar.slider('Training Score', 50.0, 100.0, float(employee['training_score']))
+    project_completion = st.sidebar.slider('Project Completion Rate', 60.0, 100.0, float(employee['project_completion']))
     
     # Create a dataframe with user input
     user_input = pd.DataFrame({
@@ -59,18 +79,48 @@ def main():
     # Make prediction
     prediction = model.predict(user_input_scaled)
     
-    st.subheader('Predicted Performance Score')
-    st.write(f"{prediction[0]:.2f}")
+    # Display results
+    col1, col2 = st.columns(2)
     
+    with col1:
+        st.subheader('Predicted Performance Score')
+        fig = go.Figure(go.Indicator(
+            mode = "gauge+number",
+            value = prediction[0],
+            domain = {'x': [0, 1], 'y': [0, 1]},
+            title = {'text': "Performance"},
+            gauge = {'axis': {'range': [None, 100]},
+                     'steps' : [
+                         {'range': [0, 60], 'color': "lightgray"},
+                         {'range': [60, 80], 'color': "gray"},
+                         {'range': [80, 100], 'color': "darkgray"}],
+                     'threshold': {'line': {'color': "red", 'width': 4}, 'thickness': 0.75, 'value': 90}}))
+        st.plotly_chart(fig)
+
+    with col2:
+        st.subheader('Feature Importance')
+        importances = model.feature_importances_
+        feature_importance = pd.DataFrame({'feature': user_input.columns, 'importance': importances})
+        feature_importance = feature_importance.sort_values('importance', ascending=False)
+        fig = px.bar(feature_importance, x='importance', y='feature', orientation='h')
+        st.plotly_chart(fig)
+    
+    # Performance Interpretation
     st.subheader('Performance Interpretation')
     if prediction[0] >= 90:
-        st.write("Excellent performance!")
+        st.write("Excellent performance! This employee is exceeding expectations.")
     elif prediction[0] >= 75:
-        st.write("Good performance")
+        st.write("Good performance. The employee is meeting expectations.")
     elif prediction[0] >= 60:
-        st.write("Average performance")
+        st.write("Average performance. There's room for improvement.")
     else:
-        st.write("Needs improvement")
+        st.write("Performance needs improvement. Consider additional training or support.")
+    
+    # Historical Performance
+    st.subheader('Historical Performance')
+    historical_data = data[data['employee_id'] == employee_id]
+    fig = px.line(historical_data, x='employee_id', y='performance_score', title='Performance Over Time')
+    st.plotly_chart(fig)
 
 if __name__ == '__main__':
     main()