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| import streamlit as st | |
| import pandas as pd | |
| import subprocess | |
| import os | |
| from groq import Groq | |
| import json | |
| GROQ_API_KEY = os.getenv("GROQ_API_KEY") | |
| if GROQ_API_KEY is None: | |
| raise RuntimeError("GROQ_API_KEY environment variable not set.") | |
| client = Groq(api_key=GROQ_API_KEY) | |
| # **Streamlit App Header** | |
| st.title("Welcome to the Shortlisting App") | |
| st.markdown(""" | |
| Through this application, your organization can streamline the hiring process for any open position. | |
| Simply import candidate resumes in CSV format and upload them here. | |
| By providing a Job Description, our system evaluates the candidates and outputs the top 5 best candidates for further review. | |
| """) | |
| st.subheader("Job Description Builder") | |
| ad_creator = st.text_area("Insert Details regarding the role you want to hire for. This includes: Company Name, Location, Role Name, Key Qualifications, etc.", height=100) | |
| def save_user_input(input_text,filename): | |
| """Save user input to a JSON file.""" | |
| data = {"input": input_text} | |
| if os.path.exists(filename): | |
| with open(filename, "r") as file: | |
| existing_data = json.load(file) | |
| else: | |
| existing_data = [] | |
| existing_data.append(data) | |
| with open(filename, "w") as file: | |
| json.dump(existing_data, file, indent=4) | |
| def create_job_description(job_ad): | |
| """Generate the Job Description using the Groq API.""" | |
| try: | |
| # save user input into JSON | |
| save_user_input(job_ad, "user_input_jobAdBuilder.json") | |
| # JobAdBuilder.py ausführen | |
| result = subprocess.run( | |
| ["python", "JobAdBuilder.py", job_ad], | |
| capture_output=True, | |
| text=True, | |
| check=True | |
| ) | |
| return result.stdout | |
| except subprocess.CalledProcessError as e: | |
| st.error(f"Error running JobAdBuilder.py: {e.stderr}") | |
| return None | |
| if st.button("Save and Build Job Description"): | |
| if ad_creator.strip(): | |
| output = create_job_description(ad_creator) | |
| if output: | |
| st.success("Job description successfully built!") | |
| st.subheader("Builder Output:") | |
| adjusted_output = st.text_area("Adjust the Job Description if needed:", output, height= 500) | |
| if st.button("Save Adjusted Job Description"): | |
| with open("AdjustedJobDescription.json", "w") as file: | |
| json.dump({"adjusted_output": adjusted_output}, file, indent=4) | |
| st.success("Adjusted Job Description saved successfully!") | |
| else: | |
| st.error("The Job Description field cannot be empty!") | |
| # **Job Description Input** | |
| st.subheader("Job Description Analyzer") | |
| job_description = st.text_area("Insert the Job Description here:", height=500) | |
| def save_and_analyze_job_description(job_description): | |
| """Analyze the Job Description using the Groq API.""" | |
| try: | |
| # save user input | |
| save_user_input(job_description, "user_input_JobDescriptionAnalyzer.json") | |
| # JobDescription.py ausführen | |
| result = subprocess.run( | |
| ["python", "JobDescription.py", job_description], | |
| capture_output=True, | |
| text=True, | |
| check=True | |
| ) | |
| return result.stdout | |
| except subprocess.CalledProcessError as e: | |
| st.error(f"Error running JobDescription.py: {e.stderr}") | |
| return None | |
| def display_analysis_result(analysis_result): | |
| """Display the analysis result a structured format.""" | |
| st.markdown(f"**Job Title:** {analysis_result.get('jobTitle', '')}") | |
| st.markdown(f"**Company:** {analysis_result.get('company', '')}") | |
| st.markdown(f"**Location:** {analysis_result.get('location', '')}") | |
| st.markdown("**Key Responsibilities:**") | |
| for responsibility in analysis_result.get('keyResponsibilities', []): | |
| st.markdown(f"- {responsibility}") | |
| st.markdown("**Required Skills:**") | |
| for skill in analysis_result.get('requiredSkills', []): | |
| st.markdown(f"- {skill}") | |
| st.markdown("**Preferred Qualifications:**") | |
| for qualification in analysis_result.get('preferredQualifications', []): | |
| st.markdown(f"- {qualification}") | |
| if st.button("Save and Analyze Job Description"): | |
| if job_description.strip(): | |
| output = save_and_analyze_job_description(job_description) | |
| if output: | |
| st.success("Job description successfully analyzed!") | |
| st.subheader("Analyzer Output:") | |
| try: | |
| analysis_result = json.loads(output) | |
| display_analysis_result(analysis_result) | |
| #Save the analysis result to a JSON file for later use | |
| if os.path.exists("JobAnalyzed.json"): | |
| with open("JobAnalyzed.json", "r") as file: | |
| existing_data = json.load(file) | |
| else: | |
| existing_data = [] | |
| existing_data.append(analysis_result) | |
| with open("JobAnalyzed.json", "w") as file: | |
| json.dump(existing_data, file, indent=4) | |
| adjusted_output = st.text_area("Adjust the Analyzed Job Description if needed:", json.dumps(analysis_result, indent=4), height= 500) | |
| if st.button("Save Adjusted Analyzed Job Description"): | |
| if os.path.exists("AdjustedJobAnalyzed.json"): | |
| with open("AdjustedJobAnalyzed.json", "r") as file: | |
| existing_data = json.load(file) | |
| else: | |
| existing_adjusted_data = [] | |
| existing_adjusted_data.append({"adjusted_output": adjusted_output}) | |
| with open("AdjustedJobAnalyzed.json", "w") as file: | |
| json.dump(existing_adjusted_data, file, indent=4) | |
| st.success("Adjusted Analyzed Job Description saved!") | |
| except json.JSONDecodeError: | |
| st.text(output) | |
| else: | |
| st.error("The Job Description field cannot be empty!") | |
| # **CSV Upload** | |
| st.subheader("CSV Upload") | |
| resume_list = st.file_uploader("Upload candidate resumes in CSV format:", type=["csv"]) | |
| def process_csv_file(csv_file): | |
| """Laden und Verarbeiten der CSV-Datei.""" | |
| try: | |
| df = pd.read_csv(csv_file) | |
| st.success("CSV file successfully uploaded!") | |
| st.dataframe(df.head()) # show first rows for checking | |
| return df | |
| except Exception as e: | |
| st.error(f"Error loading CSV file: {e}") | |
| return None | |
| if resume_list: | |
| df = process_csv_file(resume_list) | |
| # **CSV Processing with bestFit.py** | |
| if st.button("Find the best candidates for the position."): | |
| if resume_list: | |
| csv_file_path = "uploaded_file.csv" | |
| with open(csv_file_path, "wb") as file: | |
| file.write(resume_list.getbuffer()) | |
| try: | |
| # Führe das Skript aus | |
| result = subprocess.run( | |
| ["python", "bestFit.py", csv_file_path], | |
| capture_output=True, | |
| text=True, | |
| check=True | |
| ) | |
| st.subheader("Output from bestFit.py:") | |
| try: | |
| analysis_result = json.loads(result.stdout) | |
| st.json(analysis_result) | |
| except json.JSONDecodeError: | |
| st.text(result.stdout) | |
| except subprocess.CalledProcessError as e: | |
| st.error(f"Error running bestFit.py: {e.stderr}") | |
| else: | |
| st.error("Please upload a CSV file before processing!") |