app.py

import streamlit as st
from utils.utils import *
from agents import prompts 
import pyperclip
from dotenv import load_dotenv
import pandas as pd
import hashlib

load_dotenv()

def hash_inputs(resume_text, job_title, must_have, job_pref):
    # Generate a hash based on the inputs
    input_str = resume_text + job_title + must_have + job_pref 
    return hashlib.md5(input_str.encode()).hexdigest()

def hash_sel_inputs(resume_text, job_title, must_have, job_pref):
    # Generate a hash based on the inputs
    input_str = str(resume_text) + job_title + must_have + job_pref
    return hashlib.md5(input_str.encode()).hexdigest()
def table_resp(lists):
    d={}
    sc=[[],[],[],[],[],[],[]]
    for i in lists:
        sc[0].append(i['candidate_name'])
        sc[1].append(str(i['overall_match_score'])+'%')
        sc[2].append(str(i['skills_keywords_score'])+'%')
        sc[3].append(str(i['experience_score'])+'%')
        sc[4].append(str(i['education_certifications_score'])+'%')
        sc[5].append(str(i['preferred_qualifications_score'])+'%')
        sc[6].append(i['score_interpretation'])
    cols=['Candidate Name','Match Score','Skills & Keywords (40%)','Experience & Responsibilities (30%)','Education & Certifications (20%)','Preferred Qualifications (10%)','Score Interpretation']
    for i in range(len(sc)):
        d[cols[i]]=sc[i]
    df = pd.DataFrame(d)
    return df

def table_resp_exp(lists):
    d={}
    sc=[[],[],[],[],[],[],[]]
    for i in lists:
        sc[0].append(i['candidate_name'])
        sc[1].append(i['overall_match_score'])
        sc[2].append('('+str(i['skills_keywords_score'])+' out of 40) '+i['skills_keywords_explanation'])
        sc[3].append('('+str(i['experience_score'])+' out of 30) '+i['experience_explanation'])
        sc[4].append('('+str(i['education_certifications_score'])+' out of 20) '+i['education_certifications_explanation'])
        sc[5].append('('+str(i['preferred_qualifications_score'])+' out of 10) '+i['preferred_qualifications_explanation'])
        sc[6].append(i['score_interpretation'])
    cols=['Candidate Name','Match Score','Skills & Keywords (40%)','Experience & Responsibilities (30%)','Education & Certifications (20%)','Preferred Qualifications (10%)','Score Interpretation']
    for i in range(len(sc)):
        d[cols[i]]=sc[i]
    df = pd.DataFrame(d)
    return df

def expand(ext_res):
    formatted_resp=f"""
**Candidate:** {ext_res['candidate_name']}

**Match Score**: {ext_res['overall_match_score']}%

**Skills & Keywords** ({ext_res['skills_keywords_score']}% out of 40%):
{ext_res['skills_keywords_explanation']}

**Experience & Responsibilities** ({ext_res['experience_score']}% out of 30%):
{ext_res['experience_explanation']}

**Education & Certifications** ({ext_res['education_certifications_score']}% out of 20%):
{ext_res['education_certifications_explanation']}

**Preferred Qualifications** ({ext_res['preferred_qualifications_score']}% out of 10%):
{ext_res['preferred_qualifications_explanation']}

**Score Interpretation**: {ext_res['score_interpretation']}
"""            
    return formatted_resp

def concise_resp(ext_res):
        formatted_resp=f"""
**Candidate:** {ext_res['candidate_name']}

**Match Score**: {ext_res['overall_match_score']}%

**Skills & Keywords**: {ext_res['skills_keywords_score']}% out of 40%

**Experience**: {ext_res['experience_score']}% out of 30%

**Education & Certifications**: {ext_res['education_certifications_score']}% out of 20%

**Preferred Qualifications**: {ext_res['preferred_qualifications_score']}% out of 10%

**Score Interpretation**: {ext_res['score_interpretation']}
"""

        return formatted_resp
    
def filecheck(resume_file):
    if len(resume_file)==1 and resume_file is not None:
      resume_text = parse_resume(resume_file[0])
      return resume_text    
        
def filecheck_error(resume_file):
    if len(resume_file)==0:
            st.warning("Please upload a Resume.")
    else:
        st.warning("Please upload only one Resume.")

def filescheck(resume_file):
    if len(resume_file)>1 and resume_file is not None:
      resume_text = parse_resumes(resume_file)
      return resume_text
    

def filescheck_error(resume_file):
    if len(resume_file)==0:
            st.warning("Please upload Resumes.")
    else:
        st.warning("Please upload more than 1 Resume for selection.")

def main():
    if 'analysis' not in st.session_state:
        st.session_state.analysis = None
    if 'jobadv' not in st.session_state:
        st.session_state.jobadv = None
    if 'analysis_mc' not in st.session_state:
        st.session_state.analysis_mc = None
    if 'analysis' not in st.session_state:
        st.session_state.analysis = None
    if 'input_hash' not in st.session_state:
        st.session_state.input_hash = None
    if 'analysis_mc_s' not in st.session_state:
        st.session_state.analysis_mc_s = None
    if 'analysis_s' not in st.session_state:
        st.session_state.analysis_s = None
    if 'input_hash_sel' not in st.session_state:
        st.session_state.input_hash_sel = None
    if 'analysis_mc_s_exp' not in st.session_state:
        st.session_state.analysis_mc_s_exp = None

    st.title("SmartHire-Assistant")


    # Select Task
    st.sidebar.header("Select Task")
    selection = st.sidebar.radio("Select option", ("Generate Job Adverstisment", "Resume Analysis","Resume Selection"))

    # Generate Cover Letter
    if selection == "Generate Job Adverstisment":

      st.header("Job Details")
      st.subheader('Job Title')
      job_title_text = st.text_input("Enter job title here",max_chars=30)
      st.subheader('Job Requirement')
      job_requirement = st.text_area("Enter job requirement here")
      if st.button("Generate Job Adverstisment"):
        if job_requirement is not None:
            prompt_template = prompts.prompt_template_classic 
            jobadv = generate_adv(job_requirement,job_title_text, prompt_template)
            st.subheader("Job Adverstisment:")
            st.markdown(jobadv)
            st.session_state.jobadv = jobadv
            #copytoclipboard()
        else:
            st.warning("Please provide a job requirement.")
    else:
        st.sidebar.header("Resume Analysis Criteria")
        scoretext='''**80-100**: Good match 

**50-79**: Medium match 

**0-49**: Poor match  '''
        criteriatext='''**40%**: Skills and Keywords 

**30%**: Experience & Responsibilities 
                            
**20%**: Education & Certifications 
                            
**10%**: Preferred Qualifications '''
        #st.session_state.dropdown_open= False  # Close the dropdown on click

        #Match Score Range
       # st.sidebar.button("Match Score Range",icon=":material/arrow_drop_down:")
        #st.session_state.dropdown_open= False
        #st.sidebar.button("Match Score Range",icon=":material/arrow_drop_up:")
        st.sidebar.subheader("Match Score Range")
        scorecontainer=st.sidebar.container(height=130)  
        scorecontainer.markdown(scoretext)

        #Criteria weight
        st.sidebar.subheader("Criteria weight")
        criteriacontainer=st.sidebar.container(height=130)  
        criteriacontainer.markdown(criteriatext)

        st.subheader("Upload Resume")
        resume_file = st.file_uploader("Choose a file or drag and drop", type=["pdf"],accept_multiple_files=True)

        #st.header("Job Details")
        st.subheader('Job Title')
        job_title_text = st.text_input("Enter job title here", "",max_chars=30)
        st.subheader('Job Requirements')
        must_have = st.text_area("Enter job must-have requirements here", "")
        st.subheader('Preferred Qualification')
        job_pref = st.text_area("Enter any preferred skills or qualifications here", "")
        resume_text = None
        
        if selection == "Resume Analysis":
          btn1=st.button("Generate Resume Analysis")
          if btn1:
          #Only show Scores
          #if st.button("Match Score"):
            resume_text=filecheck(resume_file)
            if resume_text is not None:
              if job_pref is not None and must_have is not None :
                    current_input_hash = hash_inputs(resume_text, job_title_text, must_have, job_pref)
                    
                    # Check if the inputs have changed
                    if st.session_state.input_hash != current_input_hash:
                        # Inputs have changed, generate new analysis
                        st.session_state.input_hash = current_input_hash
                        prompt_template = prompts.prompt_template_modern
                        response = generate_analysis(resume_text, job_pref, job_title_text, must_have, prompt_template)

                        # Cache the result
                        st.session_state.analysis = expand(response)
                        st.session_state.analysis_mc = concise_resp(response)

                    # Display the cached response based on the button clicked
                        # Match Score button clicked
                    st.subheader("Resume Analysis (Match Score)")
                    st.markdown(st.session_state.analysis_mc)
                    with st.expander("Detailed Analysis"):
                        st.markdown(st.session_state.analysis)

              else:
                    st.warning("Please provide all job details.")
            else:
                filecheck_error(resume_file)

         

        else:
          btn1=st.button("Generate Match Score")
          btn2=st.button("Generate Analysis")
          if btn1 or btn2:
          #Only show Scores
          #if st.button("Match Score"):
            resume_text=filescheck(resume_file)
            if resume_text is not None:
              if job_pref is not None and must_have is not None :
                 current_input_hash = hash_sel_inputs(resume_text, job_title_text, must_have, job_pref)
                 
                # Check if the inputs have changed
                 if st.session_state.input_hash_sel != current_input_hash:
                # Inputs have changed, generate new analysis
                    st.session_state.input_hash_sel = current_input_hash
                    prompt_template = prompts.prompt_template_resumes_
                    response = generate_sel_analysis(resume_text, job_pref, job_title_text, must_have, prompt_template)
                    print('response:',response)
                    response_anal=max(response, key=lambda x: x['overall_match_score'])
                    # Cache the result
                    st.session_state.analysis_s = expand(response_anal)
                    st.session_state.analysis_mc_s = table_resp(response)
                    st.session_state.analysis_mc_s_exp = table_resp_exp(response)

                    # Display the cached response based on the button clicked
                 if btn1:
                        # Match Score button clicked
                        st.subheader("Match Scores")
                        st.dataframe(st.session_state.analysis_mc_s,hide_index=True)
                 elif btn2:
                        # Generate Analysis button clicked
                        print("expands")
                        st.subheader("Resume Analysis (Top Scored)")
                    # Candidate selection
                        st.markdown(st.session_state.analysis_s)
                        with st.expander("Detailed Analysis - All Candidates"):
                            st.dataframe(st.session_state.analysis_mc_s_exp,hide_index=True)

              else:
                st.warning("Please provide all job details.")
            else:
               filescheck_error(resume_file)
            


if __name__ == "__main__":
    main()