from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import gradio as gr


st = SentenceTransformer('all-mpnet-base-v2')

def predict(exp, listOfPosition, major_applicant, skills_applicant, yoe, jobdesc, rolename, major_vacancy, skills_vacancy, minimumYoe):
  diffYoe = yoe - minimumYoe
  results = {}
  results['score'] = 0.6
  results['is_accepted'] = True
  return results

with gr.Blocks() as app:
    with gr.Row():
        with gr.Column():
            gr.Markdown("### Applicant Details")
            exp = gr.Textbox(label="Experience")
            listOfPosition = gr.Textbox(label="List of Positions")
            major_applicant = gr.Textbox(label="Major")
            skills_applicant = gr.Textbox(label="Skills")
            yoe = gr.Number(label="Years of Experience", precision=0)
        
        with gr.Column():
            gr.Markdown("### Vacancy Details")
            jobdesc = gr.Textbox(label="Job Description")
            rolename = gr.Textbox(label="Role Name")
            major_vacancy = gr.Textbox(label="Major Required")
            skills_vacancy = gr.Textbox(label="Skills Required")
            minimumYoe = gr.Number(label="Minimum Years of Experience", precision=0)
    gr.Button("Submit Application").click(
      predict,
      inputs=[exp, listOfPosition, major_applicant, skills_applicant, yoe, jobdesc, rolename, major_vacancy, skills_vacancy, minimumYoe],
      outputs=gr.JSON(label="Result")
    )

if __name__ == "__main__":
    app.launch(debug=True)