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| import streamlit as st | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| from convert import ExtractPDFText | |
| from ATS_score import calculateATSscore, skill_gap_analysis | |
| from model import modelFeedback | |
| import time | |
| # Streamlit app title | |
| st.title("Resume Screening Assistance") | |
| # Define required skills for the job | |
| required_skills = ["Python", "Machine Learning", "Cloud (AWS/Azure)", "Data Analysis", "React", "Docker"] | |
| # Job Description Input | |
| job_description = st.text_area("Paste the job description below:", placeholder="Enter the job description here...") | |
| # Upload Resumes | |
| uploaded_files = st.file_uploader("Upload your resumes (PDF only):", type="pdf", accept_multiple_files=True) | |
| # Add a submit button | |
| if st.button("Submit"): | |
| if uploaded_files and job_description: | |
| resumes_data = [] | |
| # Initialize progress bar | |
| progress_bar = st.progress(0) | |
| total_resumes = len(uploaded_files) | |
| # Placeholder to show the current processing file | |
| current_file_placeholder = st.empty() | |
| for idx, file in enumerate(uploaded_files, start=1): | |
| try: | |
| # Update the current file being processed in the placeholder | |
| current_file_placeholder.text(f"Currently processing: {file.name}") | |
| # Extract resume text | |
| text = ExtractPDFText(file) | |
| # Calculate ATS score and skill gap analysis | |
| ats_score = calculateATSscore(text, job_description) | |
| skill_analysis = skill_gap_analysis(text, required_skills) | |
| # Generate AI feedback | |
| feedback = modelFeedback(ats_score, text, job_description) | |
| # Append results for each resume | |
| resumes_data.append({ | |
| "name": file.name, | |
| "ATS Score": ats_score, | |
| "Skills Present": skill_analysis["present"], | |
| "Missing Skills": skill_analysis["missing"], | |
| "Feedback": feedback | |
| }) | |
| # Update progress bar | |
| progress_bar.progress(idx / total_resumes) | |
| except Exception as e: | |
| st.error(f"Error processing {file.name}: {e}") | |
| # Clear the current file placeholder after processing all files | |
| current_file_placeholder.empty() | |
| if resumes_data: | |
| # Sort resumes by ATS score | |
| sorted_resumes = sorted(resumes_data, key=lambda x: x["ATS Score"], reverse=True) | |
| # Add rankings | |
| for rank, resume in enumerate(sorted_resumes, start=1): | |
| resume["Rank"] = rank | |
| # Display results | |
| st.subheader("Top Matches:") | |
| for resume in sorted_resumes: | |
| st.write(f"#### Rank {resume['Rank']}: {resume['name']}") | |
| st.write(f"**ATS Score:** {resume['ATS Score']*100:.0f}%") | |
| st.write("**Missing Skills:**", ", ".join(resume["Missing Skills"]) if resume["Missing Skills"] else "None") | |
| st.write("**Feedback:**") | |
| st.markdown(f"> {resume['Feedback']}") | |
| st.write("---") | |
| # Skill Gap Analysis Table | |
| st.subheader("Skill Gap Analysis") | |
| for resume in sorted_resumes: | |
| st.write(f"**{resume['name']} (Rank {resume['Rank']})**") | |
| skill_data = { | |
| "Required Skills": required_skills, | |
| "Present in Resume": ["✅" if skill in resume["Skills Present"] else "❌" for skill in required_skills], | |
| "Missing": [skill if skill in resume["Missing Skills"] else "—" for skill in required_skills] | |
| } | |
| df = pd.DataFrame(skill_data) | |
| st.table(df) | |
| # Visualize ATS Scores with Rankings | |
| st.subheader("ATS Score Distribution") | |
| names_with_ranks = [f"Rank {r['Rank']}: {r['name']}" for r in sorted_resumes] | |
| scores = [r["ATS Score"]*100 for r in sorted_resumes] | |
| plt.figure(figsize=(8, 5)) | |
| plt.bar(names_with_ranks, scores, color='skyblue') | |
| plt.title("ATS Scores by Resume (Ranked)") | |
| plt.ylabel("ATS Score (%)") | |
| plt.xticks(rotation=45, ha="right") | |
| st.pyplot(plt) | |
| else: | |
| st.error("No resumes could be processed. Please check the uploaded files.") | |
| else: | |
| st.warning("Please upload resumes and provide a job description before submitting.") | |