Create app.ipynb

app.ipynb

@@ -0,0 +1,31 @@
+from transformers import pipeline
+import gradio as gr
+
+def recommend_training(employee_name, technical_competence, behavioral_competence, feedback):
+    data_analysis_skills = (technical_competence + behavioral_competence) / 2
+    
+    if data_analysis_skills >= 4.5:
+        recommendation = f"Congratulations {employee_name}! Your data analysis skills are exceptional. Keep leveraging tools like Power BI to visualize insights and consider advanced courses in statistical analysis. For more information, check out these resources: [Advanced Statistical Analysis Course](https://example.com/statistical-analysis-course), [Power BI Documentation](https://docs.microsoft.com/en-us/power-bi/)."
+    elif 3.5 <= data_analysis_skills < 4.5:
+        recommendation = f"Well done {employee_name}! Your data analysis skills are good. Consider diving deeper into Power BI functionalities and attending workshops on data storytelling. For more information, check out these resources: [Power BI Workshops](https://example.com/power-bi-workshops), [Data Storytelling Guide](https://example.com/data-storytelling-guide)."
+    elif 2.5 <= data_analysis_skills < 3.5:
+        recommendation = f"{employee_name}, there's room for improvement in your data analysis skills. We recommend focusing on mastering Power BI for more advanced data visualization techniques. For more information, check out these resources: [Mastering Power BI Course](https://example.com/power-bi-course), [Data Visualization Best Practices](https://example.com/data-visualization-best-practices)."
+    else:
+        recommendation = f"{employee_name}, your data analysis skills need significant improvement. We suggest enrolling in comprehensive training programs covering Power BI and basic statistical analysis. For more information, check out these resources: [Comprehensive Power BI Training](https://example.com/power-bi-training), [Basic Statistical Analysis Course](https://example.com/statistical-analysis-course)."
+    return recommendation
+
+# Creating Gradio interface
+interface = gr.Interface(
+    fn=recommend_training,
+    inputs=[
+        gr.Textbox(label="Employee Name"),
+        gr.Slider(minimum=0, maximum=5, label="Technical Competence (out of 5)"),
+        gr.Slider(minimum=0, maximum=5, label="Behavioral Competence (out of 5)"),
+        gr.Textbox(label="Appraisee and Manager's Feedback")
+    ],
+    outputs=gr.Textbox(label="Recommendation"),
+    title="Data Analyst Performance Recommendation Engine",
+    description="Enter the Data Analyst's name, technical competence, behavioral competence, and appraisee and manager's feedback to receive recommendations for the next quarter.",
+)
+
+interface.launch()