use markdown to plot scores

app.py

@@ -2,7 +2,6 @@ import json
 import os
 import gradio as gr
 import requests
-import intro
 import recruiting_assistant
 
 
@@ -83,7 +82,7 @@ with demo:
     """)
     text_vacancy = gr.Textbox(hint="Paste here a Vacancy...", lines=7, label="Copy/paste here a vacancy")
     b1 = gr.Button("Search Resume")
-    text_search_result = gr.Textbox(hint="Top resumes will appear here ...", label="Top resumes found in the database")
+    text_search_result = gr.Textbox(hint="Top resumes will appear here ...", label="Top resumes found in the database", enable_markdown=True)
     b1.click(search_resume, inputs=text_vacancy, outputs=text_search_result)
     gr.Markdown("""
     ## 2. Select an appropriate resume for this vacancy, paste it in the textfield and write a relevant introduction email
@@ -93,8 +92,9 @@ with demo:
     gr.Markdown("""
     ## 3. You have a relevant introduction email to send to the customer
     """)
-    text_intro = gr.Textbox(label="Intro Email")
-    evaluation = gr.Textbox(label="Evaluation of the skills")
+    text_intro = gr.Textbox(label="Intro Email", enable_markdown=True)
+    # evaluation = gr.Textbox(label="Evaluation of the skills", enable_markdown=True)
+    evaluation = gr.Markdown(label="Evaluation of the skills")
     b2.click(recruiting_assistant.create_intro,
              inputs=[
                  text_vacancy,
@@ -103,7 +103,7 @@ with demo:
              outputs=[text_intro, evaluation]
     )
 
-    examples = gr.Examples(
+    gr.Examples(
         examples=examples,
         fn=search_resume,
         inputs=text_vacancy,

recruiting_assistant.py

@@ -171,9 +171,33 @@ def create_intro(vacancy=vacancy, resume=resume):
 
     result = match_resume_vacancy_skills_chain({"vacancy": vacancy, "resume": resume})
     print(result)
-    return result["introduction_email"], json.dumps({"resume skills":result['resume_skills'], "check_past_experiences": result['check_past_experiences']}, indent=4)
 
+    resume_skills = json.loads(result['resume_skills'])
+    relevant_skills = len(resume_skills["skills_present"])
+    total_skills = len(resume_skills["skills_present"] + resume_skills["skills_not_present"])
+    score_skills = round(100.0*(relevant_skills/total_skills),2)
+
+
+    check_past_experiences = json.loads(result['check_past_experiences'])
+    relevant_experiences = len(check_past_experiences["relevant_experiences"])
+    total_experiences = len(check_past_experiences["relevant_experiences"] + check_past_experiences["irrelevant_experiences"])
+    score_experiences = round(100.0*(relevant_experiences/total_experiences),2)
+
+    new_line = '\n'
+
+    score = f"""
+    ## Skills (Score: {score_skills}%)
+    Relevant Skills: {",".join(resume_skills["skills_present"])}
+    Not Relevant Skills: {",".join(resume_skills["skills_not_present"])}
     
+    
+    ## Experience (Score: {score_experiences}%)
+    Relevant experience: {(new_line+"-").join(check_past_experiences["relevant_experiences"])}
+    Not Relevant experience: {(new_line+"-").join(check_past_experiences["irrelevant_experiences"])}
+    
+    """
+    # return result["introduction_email"], json.dumps({"resume skills":result['resume_skills'], "check_past_experiences": result['check_past_experiences']}, indent=4)
+    return result["introduction_email"], score
 
 if __name__ == '__main__':
     create_intro(vacancy=vacancy,resume=resume)