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import gradio as gr
from agent import Agent
from utils import extract_employee_data, get_survey_sentiment
from strategy import StrategyFactory
# Initialize Agent
agent = Agent("L&D Recommendation Agent")
agent.persona = "You are an AI-powered HR assistant specializing in learning and development recommendations."
agent.instruction = "Provide clear, actionable, and personalized learning recommendations within 70 words."
# Gradio Function
def recommend_learning(employee_name, strategy_name):
try:
agent.strategy = strategy_name
# Extract Employee Data
employee_data = extract_employee_data(employee_name)
if isinstance(employee_data, str):
return employee_data
# Get Survey Sentiment
sentiment = get_survey_sentiment(employee_name)
rating = employee_data.get('rating', 0)
experience = employee_data.get('experience', 0)
role = employee_data.get('role', "Unknown")
# Task for Recommendation
task = f"""
Employee {employee_name} ({role}, {experience} years).
Performance Rating: {rating}, Sentiment: {sentiment}.
Identify skill gaps and recommend personalized learning programs to enhance employee performance.
"""
# Generate Recommendation
return agent.execute(task)
except Exception as e:
return str(e)
# Interface
iface = gr.Interface(
fn=recommend_learning,
inputs=[
gr.Textbox(label="Employee Name"),
gr.Dropdown(choices=StrategyFactory.available_strategies(), label="Select Strategy")
],
outputs="text",
title="AI-Based Learning & Development Recommendation System",
description="Enter an employee's name and select a strategy to generate personalized learning recommendations."
)
if __name__ == "__main__":
iface.launch()
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