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import gradio as gr
import pandas as pd
import datetime
import numpy as np
from transformers import pipeline
class AIHRAgent:
def __init__(self):
self.resume_scanner = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
self.employee_records = pd.DataFrame(columns=["Name", "Position", "Start Date", "Attendance", "Performance", "Leaves"])
self.company_policies = "Employees are entitled to 24 annual leaves and must adhere to company policies regarding attendance and punctuality."
def screen_resume(self, resume_text, job_description):
results = self.resume_scanner(resume_text, candidate_labels=[job_description, "Not Relevant"])
return f"Relevance Score: {results['scores'][0]:.2f} for the position of {job_description}."
def onboarding_guide(self, employee_name, position):
return f"Welcome {employee_name}! As a {position}, your first day involves orientation, meeting the team, and setting up your work systems."
def add_employee(self, name, position, start_date):
new_employee = {
"Name": name,
"Position": position,
"Start Date": start_date,
"Attendance": 0,
"Performance": "Not Reviewed",
"Leaves": 0
}
self.employee_records = self.employee_records.append(new_employee, ignore_index=True)
return f"Employee {name} added successfully."
def track_attendance(self, employee_name):
if employee_name in self.employee_records["Name"].values:
self.employee_records.loc[self.employee_records["Name"] == employee_name, "Attendance"] += 1
return f"Attendance recorded for {employee_name}."
return f"Employee {employee_name} not found."
def process_payroll(self, employee_name, base_salary):
if employee_name in self.employee_records["Name"].values:
tax = base_salary * 0.1
net_salary = base_salary - tax
return f"Payroll Processed: Gross Salary = {base_salary}, Tax = {tax}, Net Salary = {net_salary}."
return f"Employee {employee_name} not found."
def pulse_survey(self):
return "Pulse Survey: On a scale of 1-5, how satisfied are you with your current role?"
def feedback_analysis(self, feedback_scores):
avg_score = np.mean(feedback_scores)
return f"Average Engagement Score: {avg_score:.2f}. Action Needed: {'Yes' if avg_score < 3 else 'No'}."
def performance_review(self, employee_name, review_score):
if employee_name in self.employee_records["Name"].values:
self.employee_records.loc[self.employee_records["Name"] == employee_name, "Performance"] = review_score
return f"Performance of {employee_name} updated to {review_score}."
return f"Employee {employee_name} not found."
def get_policy(self):
return self.company_policies
def exit_interview(self, employee_name, feedback):
if employee_name in self.employee_records["Name"].values:
self.employee_records = self.employee_records[self.employee_records["Name"] != employee_name]
return f"Exit interview recorded for {employee_name}. Feedback: {feedback}"
return f"Employee {employee_name} not found."
ai_hr = AIHRAgent()
def gradio_interface():
with gr.Blocks() as interface:
gr.Markdown("# **AI HR Agent**")
gr.Markdown("Automate all HR functionalities with an intelligent AI agent.")
with gr.Tab("Recruitment and Onboarding"):
resume_input = gr.Textbox(label="Paste Resume Text")
job_description_input = gr.Textbox(label="Job Description")
resume_output = gr.Textbox(label="Screening Result")
screen_button = gr.Button("Screen Resume")
onboarding_name = gr.Textbox(label="Employee Name")
onboarding_position = gr.Textbox(label="Position")
onboarding_output = gr.Textbox(label="Onboarding Guide")
onboarding_button = gr.Button("Generate Onboarding Guide")
with gr.Tab("Employee Management"):
add_name = gr.Textbox(label="Employee Name")
add_position = gr.Textbox(label="Position")
add_start_date = gr.Textbox(label="Start Date (YYYY-MM-DD)")
add_output = gr.Textbox(label="Add Employee Result")
add_button = gr.Button("Add Employee")
attendance_name = gr.Textbox(label="Employee Name for Attendance")
attendance_output = gr.Textbox(label="Attendance Result")
attendance_button = gr.Button("Record Attendance")
with gr.Tab("Payroll Management"):
payroll_name = gr.Textbox(label="Employee Name")
payroll_salary = gr.Number(label="Base Salary")
payroll_output = gr.Textbox(label="Payroll Result")
payroll_button = gr.Button("Process Payroll")
with gr.Tab("Employee Engagement"):
pulse_output = gr.Textbox(label="Pulse Survey")
pulse_button = gr.Button("Get Pulse Survey")
feedback_scores = gr.Textbox(label="Feedback Scores (comma-separated)")
feedback_output = gr.Textbox(label="Feedback Analysis Result")
feedback_button = gr.Button("Analyze Feedback")
with gr.Tab("Performance Management"):
review_name = gr.Textbox(label="Employee Name")
review_score = gr.Number(label="Review Score")
review_output = gr.Textbox(label="Review Result")
review_button = gr.Button("Update Performance Review")
with gr.Tab("Compliance and Policy Management"):
policy_output = gr.Textbox(label="Company Policies")
policy_button = gr.Button("View Policies")
with gr.Tab("Exit Management"):
exit_name = gr.Textbox(label="Employee Name")
exit_feedback = gr.Textbox(label="Exit Feedback")
exit_output = gr.Textbox(label="Exit Interview Result")
exit_button = gr.Button("Record Exit Interview")
screen_button.click(ai_hr.screen_resume, inputs=[resume_input, job_description_input], outputs=resume_output)
onboarding_button.click(ai_hr.onboarding_guide, inputs=[onboarding_name, onboarding_position], outputs=onboarding_output)
add_button.click(ai_hr.add_employee, inputs=[add_name, add_position, add_start_date], outputs=add_output)
attendance_button.click(ai_hr.track_attendance, inputs=attendance_name, outputs=attendance_output)
payroll_button.click(ai_hr.process_payroll, inputs=[payroll_name, payroll_salary], outputs=payroll_output)
pulse_button.click(lambda: ai_hr.pulse_survey(), outputs=pulse_output)
feedback_button.click(lambda scores: ai_hr.feedback_analysis(list(map(int, scores.split(',')))), inputs=feedback_scores, outputs=feedback_output)
review_button.click(ai_hr.performance_review, inputs=[review_name, review_score], outputs=review_output)
policy_button.click(lambda: ai_hr.get_policy(), outputs=policy_output)
exit_button.click(ai_hr.exit_interview, inputs=[exit_name, exit_feedback], outputs=exit_output)
return interface
interface = gradio_interface()
interface.launch(share=True) |