import streamlit as st import google.generativeai as genai from PIL import Image import fitz # PyMuPDF from docx import Document import json from pathlib import Path from datetime import datetime import re import pytesseract import io def extract_text_from_pdf(pdf_file): """Extract text from uploaded PDF file.""" text_content = [] try: pdf_bytes = pdf_file.read() doc = fitz.open(stream=pdf_bytes, filetype="pdf") for page_num in range(len(doc)): page = doc[page_num] text_content.append(page.get_text()) return "\n".join(text_content) except Exception as e: st.error(f"Error in PDF extraction: {str(e)}") return "" def extract_text_from_docx(docx_file): """Extract text from uploaded DOCX file.""" try: doc = Document(docx_file) text_content = [] for paragraph in doc.paragraphs: text_content.append(paragraph.text) return "\n".join(text_content) except Exception as e: st.error(f"Error in DOCX extraction: {str(e)}") return "" def parse_date(date_str): """Parse date from various formats.""" try: # Handle 'Present' or 'Current' if date_str.lower() in ['present', 'current', 'now']: return datetime.now() date_str = date_str.strip() formats = [ '%Y', '%b %Y', '%B %Y', '%m/%Y', '%m-%Y', '%Y/%m', '%Y-%m' ] for fmt in formats: try: return datetime.strptime(date_str, fmt) except ValueError: continue year_match = re.search(r'\b20\d{2}\b', date_str) if year_match: return datetime.strptime(year_match.group(), '%Y') return None except Exception: return None def calculate_experience(work_history): """Calculate total years of experience from work history.""" total_experience = 0 current_year = datetime.now().year for job in work_history: duration = job.get('duration', '') if not duration: continue parts = re.split(r'\s*-\s*|\s+to\s+', duration) if len(parts) != 2: continue start_date = parse_date(parts[0]) end_date = parse_date(parts[1]) if start_date and end_date: years = (end_date.year - start_date.year) + \ (end_date.month - start_date.month) / 12 total_experience += max(0, years) return round(total_experience, 1) def parse_resume(file_uploaded, api_key): """Parse resume and extract information.""" genai.configure(api_key=api_key) model = genai.GenerativeModel('gemini-1.5-pro') prompt = """Extract the following information from this resume: 1. Summarize the following resume in 100 words, focusing on key skills, experience, and qualifications 2. Full Name 3. Email Address 4. Phone Number 5. Education History (including degree, institution, graduation year, and field of study) 6. Companies worked at with positions and EXACT duration (e.g., "Jan 2020 - Present" or "2018-2020") 7. Skills 8. LinkedIn Profile URL Return the information in this JSON format: { "summary": "", "name": "", "email": "", "phone": "", "education": [ { "degree": "", "institution": "", "year": "", "field": "", "gpa": "" } ], "work_experience": [ { "company": "", "position": "", "duration": "" } ], "skills": [], "linkedin": "" } For skills include tools and technologies in output if present any in resume. For work experience durations, please specify exact dates in format: "MMM YYYY - MMM YYYY" or "YYYY - Present" , please return in one order either in ascending or descending. Only return the JSON object, nothing else. If any field is not found, leave it empty.""" try: file_extension = Path(file_uploaded.name).suffix.lower() if file_extension == '.pdf': text_content = extract_text_from_pdf(file_uploaded) elif file_extension in ['.docx', '.doc']: text_content = extract_text_from_docx(file_uploaded) elif file_extension in ['.jpg', '.jpeg', '.png']: image = Image.open(file_uploaded) text_content = pytesseract.image_to_string(image) else: st.error(f"Unsupported file format: {file_extension}") return None response = model.generate_content(f"{prompt}\n\nResume Text:\n{text_content}") try: response_text = response.text json_start = response_text.find('{') json_end = response_text.rfind('}') + 1 json_str = response_text[json_start:json_end] result = json.loads(json_str) total_exp = calculate_experience(result.get('work_experience', [])) result['total_years_experience'] = total_exp return result except json.JSONDecodeError as e: st.error(f"Error parsing response: {str(e)}") return None except Exception as e: st.error(f"Error processing resume: {str(e)}") return None def format_education(edu): """Format education details for display.""" parts = [] if edu.get('degree'): parts.append(edu['degree']) if edu.get('field'): parts.append(f"in {edu['field']}") if edu.get('institution'): parts.append(f"from {edu['institution']}") if edu.get('year'): parts.append(f"({edu['year']})") if edu.get('gpa') and edu['gpa'].strip(): parts.append(f"- GPA: {edu['gpa']}") return " ".join(parts) def main(): st.title("Resume Parser") st.write("Upload a resume (PDF, DOCX, or Image) to extract information") # Get API key from secrets or user input api_key = st.secrets["GEMINI_API_KEY"] if "GEMINI_API_KEY" in st.secrets else st.text_input("Enter Gemini API Key", type="password") uploaded_file = st.file_uploader("Choose a resume file", type=["pdf", "docx", "doc", "jpg", "jpeg", "png"]) if uploaded_file and api_key: with st.spinner('Analyzing resume...'): result = parse_resume(uploaded_file, api_key) if result: st.subheader("Extracted Information") # Display summary in a text area st.text_area("Summary", result.get('summary', 'Not found'), height=100) # Display personal information col1, col2, col3 = st.columns(3) with col1: st.write("**Name:**", result.get('name', 'Not found')) with col2: st.write("**Email:**", result.get('email', 'Not found')) with col3: st.write("**Phone:**", result.get('phone', 'Not found')) # Display total experience total_exp = result.get('total_years_experience', 0) exp_text = f"{total_exp:.1f} years" if total_exp >= 1 else f"{total_exp * 12:.0f} months" st.write("**Total Experience:**", exp_text) # Display education st.subheader("Education") if result.get('education'): for edu in result['education']: st.write(f"- {format_education(edu)}") else: st.write("No education information found") # Display work experience st.subheader("Work Experience") if result.get('work_experience'): for exp in result['work_experience']: duration = f" ({exp.get('duration', 'Duration not specified')})" if exp.get('duration') else "" st.write(f"- {exp.get('position', 'Role not found')} at {exp.get('company', 'Company not found')}{duration}") else: st.write("No work experience found") # Display Skills st.subheader("Skills:") if result.get('skills'): for skill in result['skills']: st.write(f"- {skill}") else: st.write("- No skills found") # Display LinkedIn profile st.write("**LinkedIn Profile:**", result.get('linkedin', 'Not found')) if __name__ == "__main__": main()