main.py

from fastapi import FastAPI, HTTPException,Query
import pandas as pd
from supabase import create_client, Client
import os
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Read Supabase credentials
SUPABASE_URL = os.getenv("SUPABASE_URL")
SUPABASE_KEY = os.getenv("SUPABASE_KEY")

# Initialize FastAPI and Supabase
app = FastAPI()
supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)

# Fetch data from Supabase
try:
    response = supabase.table("HR analysis").select("*").execute()
    data = pd.DataFrame(response.data) if response.data else pd.DataFrame()
except Exception as e:
    print(f"Error fetching data: {e}")
    data = pd.DataFrame()

# Convert date columns
for col in ['Survey Date', 'StartDate', 'DOB']:
    if col in data.columns:
        data[col] = pd.to_datetime(data[col], errors='coerce')

# Calculate Age
if 'DOB' in data.columns:
    data['Age'] = (pd.to_datetime("today") - data['DOB']).dt.days // 365

# Clean Performance Score
score_map = {"Exceeds": 5, "Fully Meets": 4, "Needs Improvement": 3, "PIP": 2}
if 'Performance Score' in data.columns:
    data['Performance Score'] = data['Performance Score'].map(lambda x: score_map.get(str(x).strip(), None))
    data['Performance Score'] = pd.to_numeric(data['Performance Score'], errors='coerce')

# Endpoints with try-except handling

@app.get("/satisfaction-analysis")
def satisfaction_analysis(department: str = Query(None, description="Filter by department")):
    try:
        if "DepartmentType" not in data.columns or "Satisfaction Score" not in data.columns:
            raise HTTPException(status_code=500, detail="Required columns missing in dataset")

        filtered_data = data.copy()

        if department:
            department = department.strip().title()  # Normalize input
            filtered_data = filtered_data[
                filtered_data["DepartmentType"].str.strip().str.title() == department
            ]

        if filtered_data.empty:
            return []  # Return empty JSON instead of error

        result = filtered_data.groupby("DepartmentType")["Satisfaction Score"].mean().reset_index()
        return result.to_dict(orient="records")
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))



@app.get("/department-performance")
def department_performance():
    try:
        result = data.groupby("DepartmentType")[["Performance Score", "Current Employee Rating"]].mean().reset_index()
        return result.to_dict(orient="records")
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/training-analytics")
def training_analytics(program_name: str = Query(None, description="Filter by training program name")):
    try:
        filtered_data = data if program_name is None else data[data["Training Program Name"] == program_name]

        if filtered_data.empty:
            return []

        result = filtered_data.groupby("Training Program Name")["Training Outcome"].value_counts(normalize=True).unstack(fill_value=0)
        return result.reset_index().to_dict(orient="records")
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))



@app.get("/engagement-performance")
def engagement_performance():
    try:
        correlation = data[['Engagement Score', 'Performance Score']].corr().iloc[0, 1]
        return {"correlation_coefficient": correlation}
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/cost-benefit-analysis")
def cost_benefit_analysis():
    try:
        result = data.groupby("DepartmentType").apply(lambda x: x['Performance Score'].mean() / x['Training Cost'].sum()).reset_index(name="ROI")
        return result.to_dict(orient="records")
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/training-effectiveness")
def training_effectiveness():
    try:
        result = data.groupby("Training Program Name")["Performance Score"].mean().reset_index()
        return result.to_dict(orient="records")
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.get("/diversity-inclusion")
def diversity_dashboard():
    try:
        if "DepartmentType" not in data.columns or "GenderCode" not in data.columns:
            raise HTTPException(status_code=500, detail="Required columns missing in dataset")

        # Compute gender distribution by department
        diversity_metrics = data.groupby("DepartmentType")["GenderCode"].value_counts(normalize=True).unstack(fill_value=0).reset_index()

        return diversity_metrics.to_dict(orient="records")
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


@app.get("/work-life-balance")
def worklife_balance_impact():
    try:
        correlation = data[['Work-Life Balance Score', 'Performance Score']].corr().iloc[0, 1]
        return {"correlation_coefficient": round(correlation, 3)}  # Return as a JSON object
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))


@app.get("/career-development")
def career_development(employee_id: str = Query(None, description="Filter by Employee ID")):
    try:
        if "Employee ID" not in data.columns or "StartDate" not in data.columns:
            raise HTTPException(status_code=500, detail="Required columns missing in dataset")

        # Print available Employee IDs for debugging
        print("Available Employee IDs:", data["Employee ID"].unique())

        filtered_data = data.copy()

        if employee_id:
            employee_id = employee_id.strip()  # Remove leading/trailing spaces
            filtered_data = filtered_data[filtered_data["Employee ID"].astype(str) == employee_id]

        if filtered_data.empty:
            return []  # Return an empty list if no matching records

        career_progress = filtered_data.groupby("Employee ID")["StartDate"].count().reset_index(name="Career Movements")
        return career_progress.to_dict(orient="records")
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))