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app.py

@@ -0,0 +1,13 @@
+import streamlit as st
+from components.sidebar import render_sidebar
+from components.home import render_home
+from components.dashboard import render_dashboard
+
+# Sidebar navigation
+page = render_sidebar()
+
+# Route to appropriate page
+if page == "🏠 Home":
+    render_home()
+elif page == "📊 Analytics Dashboard":
+    render_dashboard()

components/charts.py

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+import plotly.express as px
+import pandas as pd
+
+def generate_chart(analysis_option, data):
+    if analysis_option == "Employee Satisfaction":
+        return px.bar(data, x="DepartmentType", y="Satisfaction Score", 
+                      title="Satisfaction Score by Department", color="DepartmentType")
+
+    elif analysis_option == "Department Performance":
+        return px.line(data, x="DepartmentType", y=["Performance Score", "Current Employee Rating"], 
+                       title="Departmental Performance Trends", markers=True)
+
+    elif analysis_option == "Training Analytics":
+        return px.bar(data, x="Training Program Name", y="Training Outcome", 
+                      title="Training Participation by Program", color="Training Program Name")
+
+    elif analysis_option == "Cost-Benefit Analysis":
+        return px.scatter(data, x="DepartmentType", y="ROI", title="ROI by Department", size="ROI")
+
+    elif analysis_option == "Training Effectiveness":
+        return px.bar(data, x="Training Program Name", y="Performance Score", 
+                      title="Training Effectiveness by Program", color="Training Program Name")
+
+    elif analysis_option == "Diversity & Inclusion":
+        return px.bar(data, x="Category", y="Count", color="Group",
+                      title="Diversity & Inclusion Breakdown", barmode="stack")
+
+    elif analysis_option == "Career Development":
+        return px.histogram(data, x="Career Movements", title="Career Progression Histogram")
+
+    return None

components/dashboard.py

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+import streamlit as st
+import plotly.express as px
+import pandas as pd
+from services.api import fetch_data
+
+def render_dashboard():
+    st.sidebar.subheader("Select Analysis")
+    analysis_option = st.sidebar.selectbox(
+        "Choose an analysis type:", [
+            "Employee Satisfaction",
+            "Department Performance",
+            "Training Analytics",
+            "Engagement vs Performance",
+            "Cost-Benefit Analysis",
+            "Training Effectiveness",
+            "Diversity & Inclusion",
+            "Work-Life Balance Impact",
+            "Career Development"
+        ]
+    )
+
+    # API endpoint mapping
+    endpoint_mapping = {
+        "Employee Satisfaction": "/satisfaction-analysis",
+        "Department Performance": "/department-performance",
+        "Training Analytics": "/training-analytics",
+        "Engagement vs Performance": "/engagement-performance-correlation",
+        "Cost-Benefit Analysis": "/cost-benefit-analysis",
+        "Training Effectiveness": "/training-effectiveness",
+        "Diversity & Inclusion": "/diversity-dashboard",
+        "Work-Life Balance Impact": "/worklife-balance-impact",
+        "Career Development": "/career-development"
+    }
+
+    # Fetch data
+    data = fetch_data(endpoint_mapping[analysis_option])
+
+    if data is None:
+        st.error("Failed to fetch data.")
+        return
+
+    st.title(f"📊 {analysis_option}")
+
+    # Correlation metric
+    if isinstance(data, dict) and "correlation_coefficient" in data:
+        st.metric("Correlation Coefficient", data["correlation_coefficient"])
+        return
+
+    # Display DataFrame
+    if isinstance(data, pd.DataFrame) and not data.empty:
+        st.dataframe(data)
+
+        # Generate visualization
+        fig = None
+        if analysis_option == "Employee Satisfaction":
+            fig = px.bar(data, x="DepartmentType", y="Satisfaction Score", 
+                         title="Satisfaction Score by Department", color="DepartmentType")
+        elif analysis_option == "Department Performance":
+            fig = px.line(data, x="DepartmentType", y=["Performance Score", "Current Employee Rating"], 
+                          title="Departmental Performance Trends", markers=True)
+        elif analysis_option == "Training Analytics":
+            fig = px.bar(data, x="Training Program Name", y="Training Outcome", 
+                         title="Training Participation by Program", color="Training Program Name")
+        elif analysis_option == "Cost-Benefit Analysis":
+            fig = px.scatter(data, x="DepartmentType", y="ROI", title="ROI by Department", size="ROI")
+        elif analysis_option == "Training Effectiveness":
+            fig = px.bar(data, x="Training Program Name", y="Performance Score", 
+                         title="Training Effectiveness by Program", color="Training Program Name")
+        elif analysis_option == "Diversity & Inclusion":
+            fig = px.bar(data, x="Category", y="Count", color="Group",
+                         title="Diversity & Inclusion Breakdown", barmode="stack")
+        elif analysis_option == "Career Development":
+            fig = px.histogram(data, x="Career Movements", title="Career Progression Histogram")
+
+        # Display chart
+        if fig:
+            st.plotly_chart(fig)

components/home.py

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+import streamlit as st
+from services.api import fetch_data
+
+def render_home():
+    st.title("🏠 Welcome to the HR Analytics Home Page")
+    st.write("""
+        This dashboard provides insights into employee satisfaction, performance metrics, 
+        training effectiveness, diversity & inclusion, and much more.
+    """)
+
+    # Fetch available API endpoints
+    home_data = fetch_data("/home")
+
+    if isinstance(home_data, dict):
+        st.subheader("📌 Available API Endpoints")
+        st.json(home_data.get("endpoints", {}))
+
+    st.markdown("### 🔍 How to Use")
+    st.write("""
+        - Navigate to the **Analytics Dashboard** from the sidebar.
+        - Select an analysis type to fetch insights.
+        - Visualize results with interactive charts.
+    """)

components/sidebar.py

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+import streamlit as st
+
+def render_sidebar():
+    st.sidebar.title("HR Analytics Dashboard")
+    return st.sidebar.radio("Select Page", ["🏠 Home", "📊 Analytics Dashboard"])

requirements.txt

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+streamlit
+requests
+pandas
+plotly

services/api.py

@@ -0,0 +1,21 @@
+import requests
+import pandas as pd
+import streamlit as st
+
+API_BASE_URL = "http://127.0.0.1:8000"
+
+def fetch_data(endpoint):
+    url = f"{API_BASE_URL}{endpoint}"
+    try:
+        response = requests.get(url)
+        response.raise_for_status()
+        data = response.json()
+
+        # Handle scalar responses
+        if isinstance(data, dict) and len(data) == 1:
+            return data
+
+        return pd.DataFrame(data)
+    except requests.exceptions.RequestException as e:
+        st.error(f"Error fetching data: {e}")
+        return None

utils/helpers.py

@@ -0,0 +1,5 @@
+import pandas as pd
+
+def clean_data(data):
+    """Cleans and preprocesses data before visualization."""
+    return data.dropna().reset_index(drop=True) if isinstance(data, pd.DataFrame) else data