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app.py

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+import streamlit as st
+import requests
+import pandas as pd
+import plotly.express as px
+
+# Backend API URL
+API_BASE_URL = "https://logeswari-cap-backend.hf.space"
+
+def fetch_data(endpoint, params=None):
+    """Fetch data from the FastAPI backend with optional query parameters."""
+    url = f"{API_BASE_URL}{endpoint}"
+    
+    try:
+        response = requests.get(url, params=params)
+        response.raise_for_status()
+        data = response.json()
+        
+        # Debugging: Print API response
+        print(f"Fetched from {url} | Params: {params} | Response: {data}")
+
+        # Handle single scalar values (like correlation coefficients)
+        if isinstance(data, dict) and len(data) == 1 and isinstance(list(data.values())[0], (int, float)):
+            return pd.DataFrame([data])  # Convert dictionary to DataFrame
+        
+        return pd.DataFrame(data)  # Convert list to DataFrame
+    except requests.exceptions.RequestException as e:
+        st.error(f"Error fetching data: {e}")
+        return None
+
+
+# ✅ Set up Streamlit app layout
+st.set_page_config(page_title="HR Analytics Dashboard", layout="wide")
+
+# ✅ Sidebar Navigation: Two main pages
+st.sidebar.title("📊 HR Analytics Dashboard")
+page = st.sidebar.selectbox("Select Page", ["🏠 Home", "📈 Analytics Dashboard"])
+
+# ✅ HOME PAGE
+if page == "🏠 Home":
+    st.title("🏠 Welcome to the HR Analytics Dashboard")
+    st.write("Explore different HR metrics and insights.")
+
+# ✅ ANALYTICS DASHBOARD (with 9 endpoints)
+elif page == "📈 Analytics Dashboard":
+    
+    # Select an analysis option inside the Analytics page
+    analysis_option = st.sidebar.selectbox("Choose an analysis:", [
+        "Employee Satisfaction", "Department Performance", "Training Analytics", 
+        "Engagement-Performance Correlation", "Cost-Benefit Analysis", 
+        "Training Effectiveness", "Diversity & Inclusion", "Work-Life Balance Impact", 
+        "Career Development Tracking"
+    ])
+
+    def display_chart(data, chart_type, x_col, y_col, title):
+        """Display different types of charts based on user selection."""
+        if data is None or data.empty:
+            st.warning(f"No data available for {title}")
+            return
+
+        st.title(title)
+        st.dataframe(data)  # Show table before visualization
+
+        if chart_type == "bar":
+            fig = px.bar(data, x=x_col, y=y_col, color=y_col, title=title, height=500)
+        elif chart_type == "scatter":
+            fig = px.scatter(data, x=x_col, y=y_col, color=y_col, title=title, size=y_col, height=500)
+        elif chart_type == "line":
+            fig = px.line(data, x=x_col, y=y_col, markers=True, title=title, height=500)
+        elif chart_type == "pie":
+            fig = px.pie(data, names=x_col, values=y_col, title=title)
+        elif chart_type == "histogram":
+            fig = px.histogram(data, x=x_col, title=title, nbins=10, height=500)
+        else:
+            st.warning("Invalid chart type")
+            return
+
+        st.plotly_chart(fig, use_container_width=True)
+
+
+    # ✅ Employee Satisfaction Analysis
+    if analysis_option == "Employee Satisfaction":
+        department = st.sidebar.text_input("Enter Department (optional):")
+        params = {"department": department} if department else None
+        data = fetch_data("/satisfaction-analysis", params)
+
+        if data is not None and not data.empty:
+            st.subheader(f"📊 Employee Satisfaction in {department if department else 'All Departments'}")
+            display_chart(data, "bar", "DepartmentType", "Satisfaction Score", "📊 Employee Satisfaction by Department")
+        else:
+            st.warning(f"No data available for the selected department: {department}")
+
+
+    # ✅ Department Performance Metrics
+    if analysis_option == "Department Performance":
+        data = fetch_data("/department-performance")
+        display_chart(data, "scatter", "DepartmentType", "Performance Score", "📊 Departmental Performance Metrics")
+
+
+    # ✅ Training Program Analytics
+    if analysis_option == "Training Analytics":
+        program_name = st.sidebar.text_input("Enter Training Program Name (optional):")
+        params = {"program_name": program_name} if program_name else None
+        data = fetch_data("/training-analytics", params)
+
+        if data is not None and not data.empty:
+            st.title("📊 Training Program Analytics")
+            st.dataframe(data)
+
+            if "Training Program Name" in data.columns:
+                melted_data = data.melt(id_vars="Training Program Name", var_name="Outcome", value_name="Percentage")
+                display_chart(melted_data, "bar", "Training Program Name", "Percentage", "📊 Training Outcomes by Program")
+            else:
+                st.warning("Training Program Name column missing in data.")
+
+
+    # ✅ Engagement vs. Performance Correlation
+    if analysis_option == "Engagement-Performance Correlation":
+        data = fetch_data("/engagement-performance")
+        
+        if data is not None and not data.empty:
+            st.title("📊 Engagement vs. Performance Correlation")
+            correlation_value = data.iloc[0, 0]  # Extract correlation coefficient
+            st.metric(label="Correlation Coefficient", value=f"{correlation_value:.2f}")  # Display metric
+
+
+    # ✅ Cost-Benefit Analysis
+    if analysis_option == "Cost-Benefit Analysis":
+        data = fetch_data("/cost-benefit-analysis")
+        display_chart(data, "pie", "DepartmentType", "ROI", "📊 Cost-Benefit Analysis")
+
+
+    # ✅ Training Effectiveness Metrics
+    if analysis_option == "Training Effectiveness":
+        data = fetch_data("/training-effectiveness")
+        display_chart(data, "line", "Training Program Name", "Performance Score", "📊 Training Effectiveness")
+
+
+    # ✅ Diversity and Inclusion
+    if analysis_option == "Diversity & Inclusion":
+        data = fetch_data("/diversity-inclusion")
+
+        if data is not None and not data.empty:
+            st.title("📊 Diversity and Inclusion Dashboard")
+            st.dataframe(data)
+
+            # Convert to long format for plotting
+            melted_data = data.melt(id_vars="DepartmentType", var_name="Gender", value_name="Percentage")
+
+            # Bar chart for gender distribution by department
+            fig = px.bar(melted_data, x="DepartmentType", y="Percentage", color="Gender", barmode="group", 
+                         title="📊 Diversity Breakdown by Department", height=500)
+            st.plotly_chart(fig, use_container_width=True)
+
+
+    # ✅ Work-Life Balance Impact
+    if analysis_option == "Work-Life Balance Impact":
+        data = fetch_data("/work-life-balance")
+
+        if data is not None and not data.empty:
+            st.title("📊 Work-Life Balance vs. Performance Correlation")
+            correlation_value = data.iloc[0, 0] if isinstance(data, pd.DataFrame) else data["correlation_coefficient"]
+            st.metric(label="Correlation Coefficient", value=f"{correlation_value:.2f}")
+
+
+    # ✅ Career Development Tracking
+    if analysis_option == "Career Development Tracking":
+        st.title("📊 Career Development Tracking")
+
+        all_employees = fetch_data("/career-development")
+        
+        if all_employees is not None and not all_employees.empty:
+            employee_ids = all_employees["Employee ID"].unique().tolist()
+            selected_employee = st.sidebar.selectbox("Select Employee ID:", [""] + employee_ids)
+        else:
+            selected_employee = None
+
+        params = {"employee_id": selected_employee} if selected_employee else None
+        data = fetch_data("/career-development", params)
+
+        if data is not None and not data.empty:
+            st.dataframe(data)
+            fig = px.histogram(data, x="Career Movements", title="📊 Career Development Histogram", nbins=10, height=500)
+            st.plotly_chart(fig, use_container_width=True)
+        else:
+            st.warning("No data available for the selected Employee ID.")

requirements.txt

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+streamlit
+requests
+pandas
+plotly