Create app.py

app.py

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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()