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Update app.py
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app.py
CHANGED
@@ -1,14 +1,10 @@
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import os
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import streamlit as st
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import docx
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import docx2txt
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import tempfile
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import time
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import re
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import pandas as pd
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from functools import lru_cache
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#
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try:
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from transformers import pipeline
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has_pipeline = True
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from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoModelForSeq2SeqLM
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import torch
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has_pipeline = False
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st.warning("Using basic transformers functionality instead of pipeline API")
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#
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st.set_page_config(page_title="Resume-Job Fit Analyzer", initial_sidebar_state="collapsed")
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st.markdown("<style>[data-testid=
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#####################################
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# Model Loading & Text Processing
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#####################################
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@st.cache_resource
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def load_models():
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with st.spinner("Loading AI models...
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models = {}
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# Load summarization model
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if has_pipeline:
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models['summarizer'] = pipeline("summarization", model="Falconsai/text_summarization", max_length=100
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else:
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try:
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models['summarizer_model'] = AutoModelForSeq2SeqLM.from_pretrained("Falconsai/text_summarization")
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models['summarizer_tokenizer'] = AutoTokenizer.from_pretrained("Falconsai/text_summarization")
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except Exception as e:
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st.error(f"Error loading summarization model: {e}")
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models['summarizer_model'] = None
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models['summarizer_tokenizer'] = None
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# Load
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if has_pipeline:
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models['evaluator'] = pipeline("sentiment-analysis", model="CR7CAD/RobertaFinetuned")
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else:
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models['evaluator_tokenizer'] = AutoTokenizer.from_pretrained("CR7CAD/RobertaFinetuned")
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except Exception as e:
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st.error(f"Error loading sentiment model: {e}")
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models['evaluator_model'] = None
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models['evaluator_tokenizer'] = None
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return models
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def summarize_text(text, models, max_length=100):
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if has_pipeline and 'summarizer' in models:
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try:
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return models['summarizer'](input_text)[0]['summary_text']
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except
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st.warning(f"Error in pipeline summarization: {e}")
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if 'summarizer_model' in models and models['summarizer_model']:
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try:
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tokenizer = models['summarizer_tokenizer']
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model = models['summarizer_model']
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inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=1024)
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summary_ids = model.generate(inputs.input_ids, max_length=max_length, min_length=30, num_beams=4
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return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
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except
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st.warning(f"Error in manual summarization: {e}")
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# Fallback
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sentences = re.split(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s', text)
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else:
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break
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if summary_sentences:
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original_order = sorted([(sentences.index(s), s) for s in summary_sentences])
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summary_sentences = [s for _, s in original_order]
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return " ".join(summary_sentences)
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# Keep job fit evaluation function intact as it's critical
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def evaluate_job_fit(resume_summary, job_requirements, models):
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start_time = time.time()
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required_skills = job_requirements["required_skills"]
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years_required = job_requirements["years_experience"]
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job_title = job_requirements["title"]
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job_summary = job_requirements["summary"]
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skills_mentioned = extract_skills(resume_summary)
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matching_skills = [skill for skill in required_skills if skill in skills_mentioned]
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skill_match_percentage = len(matching_skills) / len(required_skills) if required_skills else 0
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experience_pattern = r'(\d+)\+?\s*years?\s*(?:of)?\s*experience'
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experience_match = re.search(experience_pattern, resume_summary, re.IGNORECASE)
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years_experience = 0
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if experience_match:
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try:
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years_experience = int(experience_match.group(1))
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except:
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years_experience = 0
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exp_match_ratio = min(1.0, years_experience / max(1, years_required)) if years_required > 0 else 0.5
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job_title_lower = job_title.lower()
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title_match = 0
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title_words = [word for word in job_title_lower.split() if len(word) > 3]
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title_matches = sum(1 for word in title_words if word in resume_summary.lower())
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title_match = title_matches / len(title_words) if title_words else 0
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skill_score = min(2, skill_match_percentage * 3)
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exp_score = min(2, exp_match_ratio * 2)
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title_score = min(2, title_match * 2)
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name_match = re.search(r'Name:\s*(.*?)(?=\n|\Z)', resume_summary)
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name = name_match.group(1).strip() if name_match else "The candidate"
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age_match = re.search(r'Age:\s*(.*?)(?=\n|\Z)', resume_summary)
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age = age_match.group(1).strip() if age_match else "unspecified age"
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industry_match = re.search(r'Expected Industry:\s*(.*?)(?=\n|\Z)', resume_summary)
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industry = industry_match.group(1).strip() if industry_match else "unspecified industry"
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weighted_score = (skill_score * 0.5) + (exp_score * 0.3) + (title_score * 0.2)
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if weighted_score >= 1.5:
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fit_score = 2 # Good fit
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elif weighted_score >= 0.8:
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fit_score = 1 # Potential fit
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else:
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fit_score = 0 # Not a fit
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missing_skills = [skill for skill in required_skills if skill not in skills_mentioned]
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if fit_score == 2:
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fit_assessment = f"{fit_score}: GOOD FIT - {name} demonstrates strong alignment with the {job_title} position. Their background in {industry} and professional experience appear well-suited for this role's requirements. The technical expertise matches what the position demands."
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elif fit_score == 1:
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fit_assessment = f"{fit_score}: POTENTIAL FIT - {name} shows potential for the {job_title} role with some relevant experience, though there are gaps in certain technical areas. Their {industry} background provides partial alignment with the position requirements. Additional training might be needed in {', '.join(missing_skills[:2])} if pursuing this opportunity."
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else:
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fit_assessment = f"{fit_score}: NO FIT - {name}'s current background shows limited alignment with this {job_title} position. Their experience level and technical background differ significantly from the role requirements. A position better matching their {industry} expertise might be more suitable."
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execution_time = time.time() - start_time
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return fit_assessment, fit_score, execution_time
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#####################################
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# File & Information Extraction
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#####################################
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@st.cache_data
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def extract_text_from_file(file_obj):
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ext = os.path.splitext(filename)[1].lower()
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if ext == ".docx":
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try:
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document = docx.Document(file_obj)
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return "\n".join(para.text for para in document.paragraphs if para.text.strip())
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except Exception as e:
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return f"Error processing DOCX file: {e}"
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elif ext == ".doc":
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try:
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with tempfile.NamedTemporaryFile(delete=False, suffix='.doc') as temp_file:
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temp_file.write(file_obj.getvalue())
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text = docx2txt.process(temp_path)
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except Exception:
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text = "Could not process .doc file. Please convert to .docx format."
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os.unlink(temp_path)
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return text
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except Exception as e:
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return f"Error processing DOC file: {e}"
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elif ext == ".txt":
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try:
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return file_obj.getvalue().decode("utf-8")
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except Exception as e:
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return f"Error processing TXT file: {e}"
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else:
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return "Unsupported file type. Please upload a .docx, .doc, or .txt file."
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#
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@lru_cache(maxsize=32)
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def extract_name(text_start):
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lines = [line.strip() for line in text_start.split('\n') if line.strip()]
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if lines:
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first_line = lines[0]
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if 5 <= len(first_line) <= 40 and not any(x in first_line.lower() for x in ["resume", "cv", "curriculum", "vitae"
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return first_line
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for line in lines[:3]:
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if len(line.split()) <= 4 and not any(x in line.lower() for x in ["address", "phone", "email", "resume", "cv"]):
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return line
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return "Unknown (please extract from resume)"
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def extract_age(text):
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for pattern in age_patterns:
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match = re.search(pattern, text.lower())
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if match:
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if len(match.group(1)) == 4: #
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try:
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except:
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pass
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return match.group(1)
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return "Not specified"
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def extract_industry(text):
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industries = {
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"Technology": ["software", "programming", "developer", "IT", "tech"],
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"Finance": ["banking", "financial", "accounting", "finance"],
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"Healthcare": ["medical", "health", "hospital", "clinical"],
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"Education": ["teaching", "education", "university", "school"],
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"Marketing": ["marketing", "advertising", "digital marketing"],
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"Engineering": ["engineer", "engineering", "mechanical"],
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"Data Science": ["data science", "machine learning", "AI"],
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"Management": ["manager", "management", "leadership"]
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}
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text_lower = text.lower()
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if industry_counts:
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return max(industry_counts.items(), key=lambda x: x[1])[0]
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return "Not clearly specified"
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def extract_job_position(text):
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for pattern in objective_patterns:
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match = re.search(pattern, text.lower(), re.IGNORECASE | re.DOTALL)
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if match:
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if len(objective_text) > 10:
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words = objective_text.split()
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return " ".join(words[:10]).title() + "..." if len(words) > 10 else objective_text.title()
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return "Not explicitly stated"
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def extract_skills(text):
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tech_skills = [
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"Python", "Java", "JavaScript", "HTML", "CSS", "SQL", "React", "Angular",
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"Machine Learning", "Data Analysis", "AWS", "Docker", "Git", "Agile",
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"Communication", "Leadership", "Project Management"
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]
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return
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def summarize_resume_text(resume_text, models):
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name = extract_name(resume_text[:500])
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age = extract_age(resume_text)
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industry = extract_industry(resume_text)
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job_position = extract_job_position(resume_text)
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skills = extract_skills(resume_text)
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try:
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if has_pipeline and 'summarizer' in models:
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model_summary = models['summarizer'](resume_text[:2000], max_length=100, min_length=30
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else:
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model_summary = summarize_text(resume_text, models, max_length=100)
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except
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model_summary = "Error generating summary. Please check the original resume."
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return
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def extract_job_requirements(job_description, models):
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tech_skills = [
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"Python", "Java", "JavaScript", "SQL", "React", "Angular",
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"
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"Communication", "Leadership", "Project Management"
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]
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# Extract title
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title_patterns = [r'^([^:.\n]+?)(position|role|job)', r'^([^:.\n]+?)\n']
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job_title = "Not specified"
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match
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years_required =
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# Extract skills
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required_skills = [skill for skill in tech_skills if re.search(r'\b' + re.escape(skill.lower()) + r'\b',
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#
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if not required_skills:
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words = re.findall(r'\b\w{4,}\b',
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word_counts = {}
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for
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word_counts[word] = word_counts.get(word, 0) + 1
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required_skills = [word.capitalize() for word, _ in sorted(word_counts.items(), key=lambda x: x[1], reverse=True)[:5]]
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job_summary = summarize_text(job_description, models, max_length=100)
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return {
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"title": job_title,
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"years_experience": years_required,
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"required_skills": required_skills,
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"summary":
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}
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def analyze_job_fit(resume_summary, job_description, models):
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job_requirements = extract_job_requirements(job_description, models)
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assessment, fit_score,
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return assessment, fit_score, time.time() -
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# Load models at startup
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models = load_models()
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#####################################
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# Main Function
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#####################################
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def main():
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"""Main function for the Streamlit application"""
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st.title("Resume-Job Fit Analyzer")
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st.markdown(
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"""
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Upload your resume file in **.docx**, **.doc**, or **.txt** format and enter a job description to see how well you match with the job requirements.
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"""
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)
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# Resume upload
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uploaded_file = st.file_uploader("Upload your resume (.docx, .doc, or .txt)", type=["docx", "doc", "txt"])
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#
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job_description = st.text_area("Enter Job Description", height=200, placeholder="Paste the job description here...")
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# Process button
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if uploaded_file
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status_text = st.empty()
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# Step 1: Extract text
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resume_text = extract_text_from_file(uploaded_file)
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if resume_text.startswith("Error") or resume_text == "Unsupported file type. Please upload a .docx, .doc, or .txt file.":
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st.error(resume_text)
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else:
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# Step 2: Generate summary
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summary,
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# Display summary
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st.subheader("Your Resume Summary")
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st.markdown(summary)
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# Step 3:
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assessment, fit_score,
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#
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status_text.empty()
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# Display job fit results
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420 |
st.subheader("Job Fit Assessment")
|
421 |
-
|
422 |
-
|
423 |
-
|
424 |
-
0: "NO FIT",
|
425 |
-
1: "POTENTIAL FIT",
|
426 |
-
2: "GOOD FIT"
|
427 |
-
}
|
428 |
-
|
429 |
-
# Show the score prominently with appropriate coloring
|
430 |
-
score_label = fit_labels[fit_score]
|
431 |
-
score_colors = {0: "red", 1: "orange", 2: "green"}
|
432 |
-
st.markdown(f"<h2 style='color: {score_colors[fit_score]};'>{score_label}</h2>", unsafe_allow_html=True)
|
433 |
-
|
434 |
-
# Display assessment
|
435 |
st.markdown(assessment)
|
436 |
-
|
437 |
-
st.info(f"Analysis completed in {(summarization_time + assessment_time):.2f} seconds")
|
438 |
|
439 |
-
#
|
440 |
st.subheader("Recommended Next Steps")
|
441 |
-
|
442 |
if fit_score == 2:
|
443 |
st.markdown("""
|
444 |
- Apply for this position as you appear to be a good match
|
@@ -458,6 +389,5 @@ def main():
|
|
458 |
- Consider similar roles with fewer experience requirements
|
459 |
""")
|
460 |
|
461 |
-
# Run the main function
|
462 |
if __name__ == "__main__":
|
463 |
main()
|
|
|
1 |
+
import os, io, re, time, tempfile
|
2 |
import streamlit as st
|
3 |
+
import docx, docx2txt
|
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|
|
4 |
import pandas as pd
|
5 |
from functools import lru_cache
|
6 |
|
7 |
+
# Handle imports
|
8 |
try:
|
9 |
from transformers import pipeline
|
10 |
has_pipeline = True
|
|
|
12 |
from transformers import AutoModelForSequenceClassification, AutoTokenizer, AutoModelForSeq2SeqLM
|
13 |
import torch
|
14 |
has_pipeline = False
|
|
|
15 |
|
16 |
+
# Setup page
|
17 |
st.set_page_config(page_title="Resume-Job Fit Analyzer", initial_sidebar_state="collapsed")
|
18 |
+
st.markdown("""<style>[data-testid="collapsedControl"],[data-testid="stSidebar"] {display: none;}</style>""", unsafe_allow_html=True)
|
19 |
|
20 |
#####################################
|
21 |
# Model Loading & Text Processing
|
22 |
#####################################
|
23 |
+
@st.cache_resource
|
24 |
def load_models():
|
25 |
+
with st.spinner("Loading AI models..."):
|
26 |
models = {}
|
|
|
27 |
# Load summarization model
|
28 |
if has_pipeline:
|
29 |
+
models['summarizer'] = pipeline("summarization", model="Falconsai/text_summarization", max_length=100)
|
30 |
else:
|
31 |
try:
|
32 |
models['summarizer_model'] = AutoModelForSeq2SeqLM.from_pretrained("Falconsai/text_summarization")
|
33 |
models['summarizer_tokenizer'] = AutoTokenizer.from_pretrained("Falconsai/text_summarization")
|
34 |
except Exception as e:
|
35 |
st.error(f"Error loading summarization model: {e}")
|
36 |
+
models['summarizer_model'] = models['summarizer_tokenizer'] = None
|
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|
37 |
|
38 |
+
# Load evaluation model
|
39 |
if has_pipeline:
|
40 |
models['evaluator'] = pipeline("sentiment-analysis", model="CR7CAD/RobertaFinetuned")
|
41 |
else:
|
|
|
44 |
models['evaluator_tokenizer'] = AutoTokenizer.from_pretrained("CR7CAD/RobertaFinetuned")
|
45 |
except Exception as e:
|
46 |
st.error(f"Error loading sentiment model: {e}")
|
47 |
+
models['evaluator_model'] = models['evaluator_tokenizer'] = None
|
|
|
|
|
48 |
return models
|
49 |
|
50 |
def summarize_text(text, models, max_length=100):
|
51 |
+
"""Summarize text with fallbacks"""
|
52 |
+
input_text = text[:1024]
|
53 |
|
54 |
+
# Try pipeline
|
55 |
if has_pipeline and 'summarizer' in models:
|
56 |
try:
|
57 |
return models['summarizer'](input_text)[0]['summary_text']
|
58 |
+
except: pass
|
|
|
59 |
|
60 |
+
# Try manual model
|
61 |
if 'summarizer_model' in models and models['summarizer_model']:
|
62 |
try:
|
63 |
tokenizer = models['summarizer_tokenizer']
|
64 |
model = models['summarizer_model']
|
65 |
inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=1024)
|
66 |
+
summary_ids = model.generate(inputs.input_ids, max_length=max_length, min_length=30, num_beams=4)
|
67 |
return tokenizer.decode(summary_ids[0], skip_special_tokens=True)
|
68 |
+
except: pass
|
|
|
69 |
|
70 |
+
# Fallback - extract sentences
|
71 |
sentences = re.split(r'(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s', text)
|
72 |
+
scored = [(1.0/(i+1), s) for i, s in enumerate(sentences) if len(s.split()) >= 4]
|
73 |
+
scored.sort(reverse=True)
|
74 |
+
|
75 |
+
result, length = [], 0
|
76 |
+
for _, sentence in scored:
|
77 |
+
if length + len(sentence.split()) <= max_length:
|
78 |
+
result.append(sentence)
|
79 |
+
length += len(sentence.split())
|
80 |
+
|
81 |
+
if result:
|
82 |
+
ordered = sorted([(sentences.index(s), s) for s in result])
|
83 |
+
return " ".join(s for _, s in ordered)
|
84 |
+
return ""
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
85 |
|
86 |
#####################################
|
87 |
+
# File Processing & Information Extraction
|
88 |
#####################################
|
89 |
+
@st.cache_data
|
90 |
def extract_text_from_file(file_obj):
|
91 |
+
ext = os.path.splitext(file_obj.name)[1].lower()
|
|
|
92 |
|
93 |
if ext == ".docx":
|
94 |
try:
|
95 |
document = docx.Document(file_obj)
|
96 |
+
return "\n".join(para.text for para in document.paragraphs if para.text.strip())[:15000]
|
97 |
except Exception as e:
|
98 |
return f"Error processing DOCX file: {e}"
|
99 |
elif ext == ".doc":
|
100 |
try:
|
101 |
with tempfile.NamedTemporaryFile(delete=False, suffix='.doc') as temp_file:
|
102 |
temp_file.write(file_obj.getvalue())
|
103 |
+
text = docx2txt.process(temp_file.name)
|
104 |
+
os.unlink(temp_file.name)
|
105 |
+
return text[:15000]
|
|
|
|
|
|
|
|
|
|
|
|
|
106 |
except Exception as e:
|
107 |
return f"Error processing DOC file: {e}"
|
108 |
elif ext == ".txt":
|
109 |
try:
|
110 |
+
return file_obj.getvalue().decode("utf-8")[:15000]
|
111 |
except Exception as e:
|
112 |
return f"Error processing TXT file: {e}"
|
113 |
else:
|
114 |
return "Unsupported file type. Please upload a .docx, .doc, or .txt file."
|
115 |
|
116 |
+
# Information extraction functions
|
117 |
+
def extract_skills(text):
|
118 |
+
"""Extract skills from text"""
|
119 |
+
skill_keywords = {
|
120 |
+
"Programming": ["Python", "Java", "JavaScript", "HTML", "CSS", "SQL", "C++", "C#", "React", "Angular"],
|
121 |
+
"Data Science": ["Machine Learning", "Data Analysis", "Statistics", "TensorFlow", "PyTorch", "AI", "NLP"],
|
122 |
+
"Database": ["SQL", "MySQL", "MongoDB", "PostgreSQL", "Oracle", "Redis"],
|
123 |
+
"Web Dev": ["React", "Angular", "Node.js", "Frontend", "Backend", "Full-Stack", "REST API"],
|
124 |
+
"Software Dev": ["Agile", "Scrum", "Git", "DevOps", "Docker", "CI/CD", "Jenkins"],
|
125 |
+
"Cloud": ["AWS", "Azure", "Google Cloud", "Lambda", "S3", "EC2"],
|
126 |
+
"Business": ["Project Management", "Leadership", "Teamwork", "Agile", "Scrum"]
|
127 |
+
}
|
128 |
+
|
129 |
+
text_lower = text.lower()
|
130 |
+
return [skill for _, skills in skill_keywords.items() for skill in skills if skill.lower() in text_lower]
|
131 |
+
|
132 |
@lru_cache(maxsize=32)
|
133 |
def extract_name(text_start):
|
134 |
+
lines = [line.strip() for line in text_start.split('\n')[:5] if line.strip()]
|
135 |
|
136 |
if lines:
|
137 |
first_line = lines[0]
|
138 |
+
if 5 <= len(first_line) <= 40 and not any(x in first_line.lower() for x in ["resume", "cv", "curriculum", "vitae"]):
|
139 |
return first_line
|
140 |
|
141 |
for line in lines[:3]:
|
142 |
if len(line.split()) <= 4 and not any(x in line.lower() for x in ["address", "phone", "email", "resume", "cv"]):
|
143 |
return line
|
144 |
+
return "Unknown"
|
|
|
145 |
|
146 |
def extract_age(text):
|
147 |
+
for pattern in [r'age:?\s*(\d{1,2})', r'(\d{1,2})\s*years\s*old', r'dob:.*(\d{4})', r'date of birth:.*(\d{4})']:
|
|
|
|
|
148 |
match = re.search(pattern, text.lower())
|
149 |
if match:
|
150 |
+
if len(match.group(1)) == 4: # Birth year
|
151 |
+
try: return str(2025 - int(match.group(1)))
|
152 |
+
except: pass
|
|
|
|
|
153 |
return match.group(1)
|
|
|
154 |
return "Not specified"
|
155 |
|
156 |
def extract_industry(text):
|
157 |
industries = {
|
158 |
+
"Technology": ["software", "programming", "developer", "IT", "tech", "computer", "digital"],
|
159 |
+
"Finance": ["banking", "financial", "accounting", "finance", "analyst"],
|
160 |
+
"Healthcare": ["medical", "health", "hospital", "clinical", "nurse", "doctor"],
|
161 |
+
"Education": ["teaching", "teacher", "professor", "education", "university", "school"],
|
162 |
+
"Marketing": ["marketing", "advertising", "digital marketing", "social media", "brand"],
|
163 |
+
"Engineering": ["engineer", "engineering", "mechanical", "civil", "electrical"],
|
164 |
+
"Data Science": ["data science", "machine learning", "AI", "analytics", "big data"],
|
165 |
+
"Management": ["manager", "management", "leadership", "executive", "director"]
|
166 |
}
|
167 |
|
168 |
text_lower = text.lower()
|
169 |
+
counts = {ind: sum(text_lower.count(kw) for kw in kws) for ind, kws in industries.items()}
|
170 |
+
return max(counts.items(), key=lambda x: x[1])[0] if any(counts.values()) else "Not specified"
|
|
|
|
|
|
|
|
|
|
|
171 |
|
172 |
def extract_job_position(text):
|
173 |
+
text_lower = text.lower()
|
174 |
+
for pattern in [r'objective:?\s*(.*?)(?=\n\n|\n\w+:|\Z)', r'career\s*objective:?\s*(.*?)(?=\n\n|\n\w+:|\Z)',
|
175 |
+
r'summary:?\s*(.*?)(?=\n\n|\n\w+:|\Z)', r'seeking.*position.*as\s*([^.]*)']:
|
176 |
+
match = re.search(pattern, text_lower, re.IGNORECASE | re.DOTALL)
|
|
|
|
|
|
|
177 |
if match:
|
178 |
+
text = match.group(1).strip()
|
179 |
+
for title in ["developer", "engineer", "analyst", "manager", "specialist", "designer"]:
|
180 |
+
if title in text:
|
181 |
+
return next((m.group(1).strip().title() for m in
|
182 |
+
[re.search(r'(\w+\s+' + title + r')', text)] if m), title.title())
|
183 |
+
return " ".join(text.split()[:10]).title() + "..." if len(text.split()) > 10 else text.title()
|
184 |
+
|
185 |
+
# Check for job title near experience
|
186 |
+
for pattern in [r'experience:.*?(\w+\s+\w+(?:\s+\w+)?)(?=\s*at|\s*\()', r'(\w+\s+\w+(?:\s+\w+)?)\s*\(\s*(?:current|present)']:
|
187 |
+
match = re.search(pattern, text_lower, re.IGNORECASE)
|
188 |
+
if match: return match.group(1).strip().title()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
189 |
|
190 |
+
return "Not specified"
|
191 |
|
192 |
+
#####################################
|
193 |
+
# Core Analysis Functions
|
194 |
+
#####################################
|
195 |
def summarize_resume_text(resume_text, models):
|
196 |
+
start = time.time()
|
197 |
|
198 |
+
# Basic info extraction
|
199 |
name = extract_name(resume_text[:500])
|
200 |
age = extract_age(resume_text)
|
201 |
industry = extract_industry(resume_text)
|
202 |
job_position = extract_job_position(resume_text)
|
203 |
skills = extract_skills(resume_text)
|
204 |
|
205 |
+
# Generate summary
|
206 |
try:
|
207 |
if has_pipeline and 'summarizer' in models:
|
208 |
+
model_summary = models['summarizer'](resume_text[:2000], max_length=100, min_length=30)[0]['summary_text']
|
209 |
else:
|
210 |
model_summary = summarize_text(resume_text, models, max_length=100)
|
211 |
+
except:
|
212 |
+
model_summary = "Error generating summary."
|
|
|
213 |
|
214 |
+
# Format result
|
215 |
+
summary = f"Name: {name}\n\nAge: {age}\n\nExpected Industry: {industry}\n\n"
|
216 |
+
summary += f"Expected Job Position: {job_position}\n\nSkills: {', '.join(skills)}\n\nSummary: {model_summary}"
|
217 |
|
218 |
+
return summary, time.time() - start
|
219 |
|
220 |
def extract_job_requirements(job_description, models):
|
221 |
tech_skills = [
|
222 |
+
"Python", "Java", "JavaScript", "SQL", "HTML", "CSS", "React", "Angular", "Machine Learning", "AWS",
|
223 |
+
"Azure", "Docker", "MySQL", "MongoDB", "Project Management", "Agile", "Leadership", "Git", "DevOps"
|
|
|
224 |
]
|
225 |
|
226 |
+
clean_text = job_description.lower()
|
227 |
|
228 |
+
# Extract job title
|
|
|
229 |
job_title = "Not specified"
|
230 |
+
for pattern in [r'^([^:.\n]+?)(position|role|job)', r'^([^:.\n]+?)\n', r'hiring.*? ([^:.\n]+?)(:-|[.:]|\n|$)']:
|
231 |
+
match = re.search(pattern, clean_text, re.IGNORECASE)
|
232 |
+
if match:
|
233 |
+
title = match.group(1).strip() if len(match.groups()) >= 1 else match.group(2).strip()
|
234 |
+
if 3 <= len(title) <= 50:
|
235 |
+
job_title = title.capitalize()
|
236 |
+
break
|
237 |
+
|
238 |
+
# Extract years required
|
239 |
+
years_required = 0
|
240 |
+
for pattern in [r'(\d+)(?:\+)?\s*(?:years|yrs).*?experience', r'experience.*?(\d+)(?:\+)?\s*(?:years|yrs)']:
|
241 |
+
match = re.search(pattern, clean_text, re.IGNORECASE)
|
242 |
+
if match:
|
243 |
+
try:
|
244 |
+
years_required = int(match.group(1))
|
245 |
+
break
|
246 |
+
except: pass
|
247 |
|
248 |
# Extract skills
|
249 |
+
required_skills = [skill for skill in tech_skills if re.search(r'\b' + re.escape(skill.lower()) + r'\b', clean_text)]
|
250 |
|
251 |
+
# Fallback if no skills found
|
252 |
if not required_skills:
|
253 |
+
words = [w for w in re.findall(r'\b\w{4,}\b', clean_text)
|
254 |
+
if w not in ["with", "that", "this", "have", "from", "they", "will", "what", "your"]]
|
255 |
word_counts = {}
|
256 |
+
for w in words: word_counts[w] = word_counts.get(w, 0) + 1
|
257 |
+
required_skills = [w.capitalize() for w, _ in sorted(word_counts.items(), key=lambda x: x[1], reverse=True)[:5]]
|
|
|
|
|
|
|
|
|
|
|
258 |
|
259 |
return {
|
260 |
"title": job_title,
|
261 |
"years_experience": years_required,
|
262 |
"required_skills": required_skills,
|
263 |
+
"summary": summarize_text(job_description, models, max_length=100)
|
264 |
}
|
265 |
|
266 |
+
def evaluate_job_fit(resume_summary, job_requirements, models):
|
267 |
+
start = time.time()
|
268 |
+
|
269 |
+
# Basic extraction
|
270 |
+
required_skills = job_requirements["required_skills"]
|
271 |
+
years_required = job_requirements["years_experience"]
|
272 |
+
job_title = job_requirements["title"]
|
273 |
+
skills_mentioned = extract_skills(resume_summary)
|
274 |
+
|
275 |
+
# Calculate matches
|
276 |
+
matching_skills = [skill for skill in required_skills if skill in skills_mentioned]
|
277 |
+
skill_match = len(matching_skills) / len(required_skills) if required_skills else 0
|
278 |
+
|
279 |
+
# Extract experience
|
280 |
+
years_experience = 0
|
281 |
+
exp_match = re.search(r'(\d+)\+?\s*years?\s*(?:of)?\s*experience', resume_summary, re.IGNORECASE)
|
282 |
+
if exp_match:
|
283 |
+
try: years_experience = int(exp_match.group(1))
|
284 |
+
except: pass
|
285 |
+
|
286 |
+
# Calculate scores
|
287 |
+
exp_match_ratio = min(1.0, years_experience / max(1, years_required)) if years_required > 0 else 0.5
|
288 |
+
title_words = [w for w in job_title.lower().split() if len(w) > 3]
|
289 |
+
title_match = sum(1 for w in title_words if w in resume_summary.lower()) / len(title_words) if title_words else 0
|
290 |
+
|
291 |
+
# Final scores
|
292 |
+
skill_score = min(2, skill_match * 3)
|
293 |
+
exp_score = min(2, exp_match_ratio * 2)
|
294 |
+
title_score = min(2, title_match * 2)
|
295 |
+
|
296 |
+
# Extract candidate info
|
297 |
+
name = re.search(r'Name:\s*(.*?)(?=\n|\Z)', resume_summary)
|
298 |
+
name = name.group(1).strip() if name else "The candidate"
|
299 |
+
|
300 |
+
industry = re.search(r'Expected Industry:\s*(.*?)(?=\n|\Z)', resume_summary)
|
301 |
+
industry = industry.group(1).strip() if industry else "unspecified industry"
|
302 |
+
|
303 |
+
# Calculate weighted score
|
304 |
+
weighted_score = (skill_score * 0.5) + (exp_score * 0.3) + (title_score * 0.2)
|
305 |
+
fit_score = 2 if weighted_score >= 1.5 else (1 if weighted_score >= 0.8 else 0)
|
306 |
+
|
307 |
+
# Generate assessment
|
308 |
+
missing = [skill for skill in required_skills if skill not in skills_mentioned]
|
309 |
+
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310 |
+
if fit_score == 2:
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311 |
+
assessment = f"{fit_score}: GOOD FIT - {name} demonstrates strong alignment with the {job_title} position. Their background in {industry} appears well-suited for this role's requirements."
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312 |
+
elif fit_score == 1:
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313 |
+
assessment = f"{fit_score}: POTENTIAL FIT - {name} shows potential for the {job_title} role but has gaps in certain areas. Additional training might be needed in {', '.join(missing[:2])}."
|
314 |
+
else:
|
315 |
+
assessment = f"{fit_score}: NO FIT - {name}'s background shows limited alignment with this {job_title} position. Their experience and skills differ significantly from the requirements."
|
316 |
+
|
317 |
+
return assessment, fit_score, time.time() - start
|
318 |
+
|
319 |
def analyze_job_fit(resume_summary, job_description, models):
|
320 |
+
start = time.time()
|
321 |
job_requirements = extract_job_requirements(job_description, models)
|
322 |
+
assessment, fit_score, _ = evaluate_job_fit(resume_summary, job_requirements, models)
|
323 |
+
return assessment, fit_score, time.time() - start
|
|
|
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|
324 |
|
325 |
#####################################
|
326 |
+
# Main Function
|
327 |
#####################################
|
328 |
def main():
|
|
|
329 |
st.title("Resume-Job Fit Analyzer")
|
330 |
+
st.markdown("Upload your resume file in **.docx**, **.doc**, or **.txt** format and enter a job description to see how well you match.")
|
|
|
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|
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|
|
|
|
|
|
|
|
|
331 |
|
332 |
+
# Load models and get inputs
|
333 |
+
models = load_models()
|
334 |
+
uploaded_file = st.file_uploader("Upload your resume", type=["docx", "doc", "txt"])
|
335 |
job_description = st.text_area("Enter Job Description", height=200, placeholder="Paste the job description here...")
|
336 |
|
337 |
+
# Process when button clicked
|
338 |
+
if uploaded_file and job_description and st.button("Analyze Job Fit"):
|
339 |
+
progress = st.progress(0)
|
340 |
+
status = st.empty()
|
|
|
341 |
|
342 |
# Step 1: Extract text
|
343 |
+
status.text("Step 1/3: Extracting text from resume...")
|
344 |
resume_text = extract_text_from_file(uploaded_file)
|
345 |
+
progress.progress(25)
|
346 |
|
347 |
if resume_text.startswith("Error") or resume_text == "Unsupported file type. Please upload a .docx, .doc, or .txt file.":
|
348 |
st.error(resume_text)
|
349 |
else:
|
350 |
# Step 2: Generate summary
|
351 |
+
status.text("Step 2/3: Analyzing resume...")
|
352 |
+
summary, summary_time = summarize_resume_text(resume_text, models)
|
353 |
+
progress.progress(50)
|
|
|
|
|
354 |
st.subheader("Your Resume Summary")
|
355 |
st.markdown(summary)
|
356 |
|
357 |
+
# Step 3: Evaluate fit
|
358 |
+
status.text("Step 3/3: Evaluating job fit...")
|
359 |
+
assessment, fit_score, eval_time = analyze_job_fit(summary, job_description, models)
|
360 |
+
progress.progress(100)
|
361 |
+
status.empty()
|
362 |
|
363 |
+
# Display results
|
|
|
|
|
|
|
364 |
st.subheader("Job Fit Assessment")
|
365 |
+
fit_labels = {0: "NOT FIT", 1: "POTENTIAL FIT", 2: "GOOD FIT"}
|
366 |
+
colors = {0: "red", 1: "orange", 2: "green"}
|
367 |
+
st.markdown(f"<h2 style='color: {colors[fit_score]};'>{fit_labels[fit_score]}</h2>", unsafe_allow_html=True)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
368 |
st.markdown(assessment)
|
369 |
+
st.info(f"Analysis completed in {(summary_time + eval_time):.2f} seconds")
|
|
|
370 |
|
371 |
+
# Recommendations
|
372 |
st.subheader("Recommended Next Steps")
|
|
|
373 |
if fit_score == 2:
|
374 |
st.markdown("""
|
375 |
- Apply for this position as you appear to be a good match
|
|
|
389 |
- Consider similar roles with fewer experience requirements
|
390 |
""")
|
391 |
|
|
|
392 |
if __name__ == "__main__":
|
393 |
main()
|