Delete ats_optimizer

ats_optimizer/README.md

@@ -1,28 +0,0 @@
-# ATS Optimizer Pro
-
-Complete solution to optimize resumes for applicant tracking systems.
-
-## Features
-
-1. **Format-Preserving Analysis**  
-   - Parses PDF/Word resumes while keeping original formatting
-   - Advanced section detection
-
-2. **5-Factor ATS Scoring**  
-   - Keyword matching (TF-IDF + exact match)  
-   - Section completeness  
-   - Semantic similarity (Qdrant vectors)  
-   - Experience matching  
-   - Education verification  
-
-3. **AI-Powered Optimization**  
-   - DeepSeek API integration  
-   - 3-step iterative refinement  
-   - Format-aware rewriting  
-
-## Setup
-
-1. Install requirements:
-   ```bash
-   pip install -r requirements.txt
-   python -m spacy download en_core_web_md

ats_optimizer/app.py

@@ -1,139 +0,0 @@
-import streamlit as st
-from ats_optimizer.core import ResumeAnalyzer, ResumeOptimizer
-from ats_optimizer.data_models import Resume, JobDescription
-from ats_optimizer.utils.file_handlers import FileHandler  # Updated import
-# from ats_optimizer.utils import FileHandler, Config
-from ats_optimizer.utils.config_manager import Config
-from pathlib import Path
-import tempfile
-import sys
-
-# Fix module imports for Hugging Face
-sys.path.append(str(Path(__file__).parent))
-
-# def main():
-#     # Initialize components
-#     config = Config('config.yaml')
-#     analyzer = ResumeAnalyzer()
-#     optimizer = ResumeOptimizer(config.deepseek_api_key)
-    
-#     # Streamlit UI
-#     st.title("🚀 ATS Optimizer Pro")
-#     st.markdown("Upload your resume and job description to analyze and optimize for ATS compatibility")
-
-#     # File upload section
-#     with st.expander("Upload Files", expanded=True):
-#         col1, col2 = st.columns(2)
-#         with col1:
-#             resume_file = st.file_uploader("Resume", type=["pdf", "docx"], key="resume_upload")
-#         with col2:
-#             jd_file = st.file_uploader("Job Description", type=["pdf", "docx", "txt"], key="jd_upload")
-
-#     if st.button("Analyze", type="primary"):
-#         if resume_file and jd_file:
-#             with st.spinner("Processing files..."):
-#                 try:
-#                     # Save uploaded files
-#                     with tempfile.TemporaryDirectory() as temp_dir:
-#                         resume_path = FileHandler.save_uploaded_file(resume_file, temp_dir)
-#                         jd_path = FileHandler.save_uploaded_file(jd_file, temp_dir)
-                        
-#                         if not resume_path or not jd_path:
-#                             st.error("Failed to process uploaded files")
-#                             return
-                        
-#                         # Analyze documents
-#                         resume = analyzer.parse_resume(resume_path)
-#                         jd = analyzer.parse_jd(jd_path)
-                        
-#                         # Calculate score
-#                         score = analyzer.calculate_ats_score(resume, jd)
-                        
-#                         # Display results
-#                         st.subheader("📊 Analysis Results")
-#                         st.metric("Overall ATS Score", f"{score['overall_score']:.1f}%")
-                        
-#                         with st.expander("Detailed Scores"):
-#                             st.write(f"Keyword Match: {score['keyword_score']:.1f}%")
-#                             st.write(f"Section Completeness: {score['section_score']:.1f}%")
-#                             st.write(f"Experience Match: {score['experience_score']:.1f}%")
-                        
-#                         # Optimization section
-#                         st.subheader("🛠 Optimization")
-#                         if st.button("Optimize Resume", key="optimize_btn"):
-#                             with st.spinner("Rewriting resume..."):
-#                                 optimized = optimizer.rewrite_resume(resume, jd)
-#                                 temp_output = Path(temp_dir) / "optimized_resume.docx"
-#                                 FileHandler.save_resume(optimized, str(temp_output))
-                                
-#                                 st.success("Optimization complete!")
-#                                 with open(temp_output, "rb") as f:
-#                                     st.download_button(
-#                                         "Download Optimized Resume",
-#                                         data=f,
-#                                         file_name="optimized_resume.docx",
-#                                         mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document"
-#                                     )
-                
-#                 except Exception as e:
-#                     st.error(f"An error occurred: {str(e)}")
-#                     st.stop()
-#         else:
-#             st.warning("Please upload both resume and job description files")
-
-# if __name__ == "__main__":
-#     main()
-
-def main():
-    # Initialize components
-    config = Config('config.yaml')
-    analyzer = ResumeAnalyzer()
-    optimizer = ResumeOptimizer(config.deepseek_api_key)
-    
-    # Streamlit UI
-    st.title("ATS Optimizer Pro")
-    
-    # File upload
-    resume_file = st.file_uploader("Upload Resume", type=["pdf", "docx"])
-    jd_file = st.file_uploader("Upload Job Description", type=["pdf", "docx", "txt"])
-    
-    if st.button("Analyze"):
-        if resume_file and jd_file:
-            with st.spinner("Processing..."):
-                try:
-                    # Create temp directory
-                    with tempfile.TemporaryDirectory() as temp_dir:
-                        # Save uploaded files
-                        resume_path = FileHandler.save_uploaded_file(resume_file, temp_dir)
-                        jd_path = FileHandler.save_uploaded_file(jd_file, temp_dir)
-                        
-                        if not resume_path or not jd_path:
-                            st.error("Failed to process uploaded files")
-                            return
-                        
-                        # Analyze documents
-                        resume = analyzer.parse_resume(resume_path)
-                        jd = analyzer.parse_jd(jd_path)
-                        
-                        # Calculate score
-                        score = analyzer.calculate_ats_score(resume, jd)
-                        
-                        # Display results
-                        st.subheader("Analysis Results")
-                        st.json(score)
-                        
-                        # Optimization
-                        if st.button("Optimize Resume"):
-                            optimized = optimizer.rewrite_resume(resume, jd)
-                            output_path = os.path.join(temp_dir, "optimized_resume.docx")
-                            if FileHandler.save_resume(optimized, output_path):
-                                with open(output_path, "rb") as f:
-                                    st.download_button(
-                                        "Download Optimized Resume",
-                                        data=f,
-                                        file_name="optimized_resume.docx"
-                                    )
-                except Exception as e:
-                    st.error(f"An error occurred: {str(e)}")
-        else:
-            st.warning("Please upload both resume and job description files")

ats_optimizer/config.yaml

@@ -1,18 +0,0 @@
-deepseek:
-  api_key: "sk-cf0b89ca56e44cb9980113909ebe687e"
-  model: "deepseek-chat"
-  temperature: 0.7
-
-google_sheets:
-  enabled: true # Set to true if using Google Sheets or else false
-  credentials: "credentials.json"
-  # sheet_id: "your-sheet-id"
-  sheet_name: "Job Applications"
-
-scoring:
-  weights:
-    keyword: 0.3
-    section: 0.2
-    vector: 0.25
-    experience: 0.15
-    education: 0.1

ats_optimizer/core/__init__.py

@@ -1,10 +0,0 @@
-# from .analyzer import ResumeAnalyzer
-# from .optimizer import ResumeOptimizer
-# from .tracker import ApplicationTracker
-
-from ats_optimizer.core.analyzer import ResumeAnalyzer
-from ats_optimizer.core.optimizer import ResumeOptimizer
-from ats_optimizer.core.tracker import ApplicationTracker
-
-__all__ = ['ResumeAnalyzer', 'ResumeOptimizer', 'ApplicationTracker']
-# __all__ = ['ResumeAnalyzer', 'ResumeOptimizer'] #if doesnt want to use tracker & comment above import too

ats_optimizer/core/analyzer.py

@@ -1,41 +0,0 @@
-from qdrant_client import QdrantClient
-from ats_optimizer.data_models.resume import Resume
-from ats_optimizer.data_models.job_description import JobDescription
-# from utils.logger import logger
-# from ..utils.logger import logger
-from ats_optimizer.utils.logger import logger
-
-class ResumeAnalyzer:
-    def __init__(self):
-        self.client = QdrantClient(":memory:")
-        self.client.set_model("BAAI/bge-base-en")
-    
-    def parse_resume(self, file_path: str) -> Resume:
-        """Step 1: Parse resume with formatting preservation"""
-        # Uses python-docx for Word docs, pdfminer for PDFs
-        raw_text, formatting = FileHandler.extract_with_formatting(file_path)
-        return Resume(raw_text, formatting)
-    
-    def parse_jd(self, file_path: str) -> JobDescription:
-        """Step 2: Analyze job description"""
-        raw_text = FileHandler.extract_text(file_path)
-        return JobDescription(raw_text)
-    
-    def calculate_ats_score(self, resume: Resume, jd: JobDescription) -> dict:
-        """Step 3: Comprehensive ATS scoring"""
-        # Enhanced scoring with 5 factors
-        scores = {
-            'keyword': self._keyword_match_score(resume, jd),
-            'section': self._section_completeness(resume, jd),
-            'vector': self._vector_similarity(resume, jd),
-            'experience': self._experience_match(resume, jd),
-            'education': self._education_match(resume, jd)
-        }
-        scores['overall'] = sum(w * s for w, s in [
-            (0.3, scores['keyword']),
-            (0.2, scores['section']),
-            (0.25, scores['vector']),
-            (0.15, scores['experience']),
-            (0.1, scores['education'])
-        ])
-        return scores

ats_optimizer/core/form_filler/background.js

@@ -1,10 +0,0 @@
-chrome.runtime.onMessage.addListener((request, sender, sendResponse) => {
-    if (request.action === "fillForm") {
-      chrome.tabs.query({active: true, currentWindow: true}, (tabs) => {
-        chrome.tabs.sendMessage(tabs[0].id, {
-          action: "fillFields",
-          resumeData: request.data
-        });
-      });
-    }
-  });

ats_optimizer/core/form_filler/content.js

@@ -1,14 +0,0 @@
-chrome.runtime.onMessage.addListener((msg, sender, sendResponse) => {
-    if (msg.action === "fillFields") {
-      const fields = {
-        'input[name*="name"]': msg.resumeData.name,
-        'input[name*="email"]': msg.resumeData.email,
-        'textarea[name*="experience"]': msg.resumeData.experience
-      };
-      
-      Object.entries(fields).forEach(([selector, value]) => {
-        const el = document.querySelector(selector);
-        if (el) el.value = value;
-      });
-    }
-  });

ats_optimizer/core/form_filler/manifest.json

@@ -1,17 +0,0 @@
-{
-    "manifest_version": 3,
-    "name": "ATS AutoFill",
-    "version": "1.0",
-    "permissions": ["activeTab", "storage"],
-    "action": {
-      "default_popup": "popup.html",
-      "default_icon": "icon.png"
-    },
-    "background": {
-      "service_worker": "background.js"
-    },
-    "content_scripts": [{
-      "matches": ["*://*/*"],
-      "js": ["content.js"]
-    }]
-  }

ats_optimizer/core/form_filler/popup.html

@@ -1,10 +0,0 @@
-<!DOCTYPE html>
-<html>
-<head>
-    <title>ATS AutoFill</title>
-    <script src="popup.js"></script>
-</head>
-<body>
-    <button id="fillButton">Fill Form</button>
-</body>
-</html>

ats_optimizer/core/optimizer.py

@@ -1,39 +0,0 @@
-import requests
-from docx import Document
-from data_models.resume import Resume
-
-from ats_optimizer.data_models.job_description import JobDescription
-from ats_optimizer.data_models.resume import Resume
-
-class ResumeOptimizer:
-    def __init__(self, api_key: str):
-        self.api_key = api_key
-        # self.template = "templates/professional.docx"
-        self.template = "templates/Ramen_DXC.docx"
-    
-    def rewrite_resume(self, resume: Resume, jd: JobDescription) -> Resume:
-        """Step 4: AI rewriting with formatting preservation"""
-        prompt = self._build_optimization_prompt(resume, jd)
-        
-        response = requests.post(
-            "https://api.deepseek.com/v1/chat/completions",
-            headers={"Authorization": f"Bearer {self.api_key}"},
-            json={
-                "model": "deepseek-chat",
-                "messages": [{
-                    "role": "user",
-                    "content": prompt
-                }],
-                "temperature": 0.7
-            }
-        )
-        
-        # Apply optimized content to original format
-        optimized_content = response.json()["choices"][0]["message"]["content"]
-        return self._apply_formatting(resume, optimized_content)
-    
-    def _apply_formatting(self, original: Resume, new_content: str) -> Resume:
-        """Preserve original formatting with new content"""
-        doc = Document(original.file_path)
-        # Advanced formatting preservation logic
-        return Resume.from_docx(doc, new_content)

ats_optimizer/core/tracker.py

@@ -1,57 +0,0 @@
-import gspread
-from oauth2client.service_account import ServiceAccountCredentials
-from pathlib import Path
-import logging
-
-logger = logging.getLogger(__name__)
-
-'''
-class ApplicationTracker:
-    def __init__(self, creds_path: str):
-        self.scope = ["https://www.googleapis.com/auth/spreadsheets"]
-        self.creds = ServiceAccountCredentials.from_json_keyfile_name(creds_path, self.scope)
-    
-    def track(self, application_data: dict):
-        """Track application in Google Sheets with enhanced fields"""
-        client = gspread.authorize(self.creds)
-        sheet = client.open("Job Applications").sheet1
-        
-        sheet.append_row([
-            application_data['company'],
-            application_data['position'],
-            application_data['date'],
-            application_data['status'],
-            application_data['score'],
-            application_data['url'],
-            application_data['resume_version'],
-            application_data['notes']
-        ])
-'''
-class ApplicationTracker:
-    def __init__(self, creds_path: str):
-        if not Path(creds_path).exists():
-            raise FileNotFoundError(f"Credentials file not found at {creds_path}")
-        
-        try:
-            self.scope = ["https://www.googleapis.com/auth/spreadsheets"]
-            self.creds = ServiceAccountCredentials.from_json_keyfile_name(creds_path, self.scope)
-            self.client = gspread.authorize(self.creds)
-        except Exception as e:
-            logger.error(f"Google Sheets auth failed: {e}")
-            raise
-
-    def track(self, application_data: dict, sheet_name="Job Applications"):
-        try:
-            sheet = self.client.open(sheet_name).sheet1
-            sheet.append_row([
-                application_data.get('company', ''),
-                application_data.get('position', ''),
-                application_data.get('date_applied', ''),
-                application_data.get('status', 'Applied'),
-                application_data.get('score', ''),
-                application_data.get('url', '')
-            ])
-            return True
-        except Exception as e:
-            logger.error(f"Tracking failed: {e}")
-            return False

ats_optimizer/credentials.json

@@ -1,13 +0,0 @@
-{
-  "type": "service_account",
-  "project_id": "formal-theater-458022-d2",
-  "private_key_id": "e7494ef90b6d43b0dc15ce9f9db0922225a48694",
-  "private_key": "-----BEGIN PRIVATE KEY-----\nMIIEvQIBADANBgkqhkiG9w0BAQEFAASCBKcwggSjAgEAAoIBAQDDGjEU29ETZSyx\nqr8zakizg+q3IqWq3pQdMVHH72GLq/vugkZiqao11YdFK3igyKIRApacpLAACjmm\nEoUwcKEh1R7WKExbAvyFTLIm65j9wZ2Z2LeAuRRZFjU2uQ+iCXOTQA7YgIPU3sMg\nLI+cMAITr0C4hLRnjgUcPeNQRG8M/Io3Oq4KeTDvEKS99aqVvA7zgGH98awdBd2c\ncciYGHaTUq5sJxK72AKFnPxrLHgbq9hiuc5fw7YdCVzmuVY/LXkwSkosbIsIrxUx\nPHwlnYdoWHZ0aI76x3qluw+MUqZoIEp3ctkIfHaZbLf4yuqTcbUuQ7RtkPFLifge\ndFsM7BnhAgMBAAECggEASLQr8h/wC5A6VYLReXFz4iGYh+JLZh9HhpFobl8QNKJE\nYZ7+Z6neGe2WWPpYG2JosnoKchkU1Q76aJ6iL2jpQthOg3PE8G1ueKYaBVLqUjWi\na0BNMZTGtmQGNHxGDRYEkazfW2KYvey9PfIdGhDx1TALqDcbmzNbSCjv2muGDoou\nzmEcAzdil5M0UNVctqxqAOk4PK3l59dSUdRgJLP6LsceT1xqNDw9Ps9x8vOUY3iq\n0kkOeXu+eTVIkgTxIsJGnBQ0wr+Ao5r12zJ8Y/iyXDVN/RTOl+aakegYiIYjeVZu\nEPyZ2fMNnQCtfSGzoLrcHyFxkhh1Fl2E4ZhW4hWh+QKBgQDgjtlnhBdhNuBGHOp0\nknRKWlhtRXzuIDklCmIS/vK0ZXpCF2wacXo7UKHQ7oVPsA5+0MqeV9teu8hqQm/Y\nZRipHU911zlsR81ZejR4WQ8xDci4Ewpt5JVkx+DfPlNXuPapgGTtg/KBDfKHWnnv\nNr/BSeRSNb5iSPkS/j4ENnrhEwKBgQDea4U0cy9J2YvBhOmb/Bk9fOIBcnlz/B2v\nWt/+ddIZnW1IOYgSd/W8W3GyQ803OzzuHa+y0vLNnGBwapOMPJ8foagy36XYbRxr\nSu8He+oTTJTgwidEnUz1DS8B3IIgi+FJK8YLUL8hVupuBqqI/r9G/wtULTFutzvD\n5/N/rCaruwKBgADOIlNvstHDa5x0wBZ46/fUSRrjM+Z6sRnD5sQgq+gfsQeJo/aY\nT5Lk4B+qq0m03OhxgTh+Iig9ziMrZ9FD04nPtBg9FFSiEUdv275Ou3I2lXCriM8K\nEcsRuGm0hIH9BM1oy3PalEUIMsVvep5z+M4NoMb2sF8T2ejKhphnRZuHAoGAL+rM\nFMOn8WoLwNJIndFPAr8v1Y36+nDbWFbkoOZzMA+JZqD2Xrw3VbABq50NzhNWChqd\nKpJlusQwxqc/SFwbD+581RD3osvG7pqDKoKYqDW8cTuCyDZ3SOfhM6503lwkWeYz\nUWbA9obKFJAdF0yCmuIBZ84gszCIkKkc/WlyH1cCgYEAtkmceBmt/YCPnaVDhk/L\n4hWqcVHqD2RU4DPPhjIXppjahDrcOzjD7Lt9swKK3QZEE840DcIAKVU140V2Neie\nOXqzgrLn/dIe0j15FK4lmY9GhCVU3kTx08JwxEiBIDoi0Z6F926C9dvjGxarpsi7\n77pNNpxzZ9i6mx9dK9ECuMI=\n-----END PRIVATE KEY-----\n",
-  "client_email": "[email protected]",
-  "client_id": "102212927624577642135",
-  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
-  "token_uri": "https://oauth2.googleapis.com/token",
-  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
-  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/ats-optimizer%40formal-theater-458022-d2.iam.gserviceaccount.com",
-  "universe_domain": "googleapis.com"
-}

ats_optimizer/data_models/__init__.py

@@ -1,5 +0,0 @@
-# from .resume import Resume
-from .resume import Resume
-from .job_description import JobDescription
-
-__all__ = ['Resume', 'JobDescription']

ats_optimizer/data_models/job_description.py

@@ -1,32 +0,0 @@
-from dataclasses import dataclass
-from typing import Dict, List
-
-@dataclass
-class JobDescription:
-    # raw_text: str
-    # extracted_keywords: List[str]
-    # keyterms: List[tuple]
-    # entities: List[str]
-    raw_text: str
-    extracted_keywords: list
-    keyterms: list
-    entities: list
-    
-    @property
-    def required_skills(self) -> List[str]:
-        return [kw for kw in self.extracted_keywords if 'skill' in kw.lower()]
-    
-    @property
-    def experience_requirements(self) -> Dict:
-        return {
-            'years': self._extract_years(),
-            'technologies': self._extract_tech()
-        }
-    
-    def _extract_years(self) -> int:
-        # Extract years requirement using regex
-        pass
-    
-    def _extract_tech(self) -> List[str]:
-        # Extract required technologies
-        pass

ats_optimizer/data_models/resume.py

@@ -1,23 +0,0 @@
-from dataclasses import dataclass
-from typing import Dict, List
-
-@dataclass
-class Resume:
-    # raw_text: str
-    # formatting: Dict
-    # metadata: Dict
-    # scores: Dict = None
-    raw_text: str
-    extracted_keywords: list
-    keyterms: list
-    entities: list
-    
-    @classmethod
-    def from_docx(cls, file_path: str):
-        """Parse while preserving formatting"""
-        text, formatting = parse_docx_with_formatting(file_path)
-        return cls(text, formatting, extract_metadata(text))
-    
-    def to_docx(self, output_path: str):
-        """Export with original formatting"""
-        apply_formatting(self.raw_text, self.formatting, output_path)

ats_optimizer/requirement.txt

@@ -1,22 +0,0 @@
-# Core
-python-docx==0.8.11
-pdfminer.six==20221105
-#python-docx-template==0.16.0
-docxtpl==0.16.0
-pdf2docx==0.5.6
-qdrant-client==1.6.4
-sentence-transformers==2.2.2
-
-# API & Services
-google-api-python-client==2.104.0
-gspread==5.11.3
-requests==2.31.0
-oauth2client==4.1.3
-
-# UI
-streamlit==1.29.0
-plotly==5.18.0
-
-# NLP
-spacy==3.7.2
-en-core-web-md @ https://github.com/explosion/spacy-models/releases/download/en_core_web_md-3.7.0/en_core_web_md-3.7.0-py3-none-any.whl

ats_optimizer/setup.py

@@ -1,16 +0,0 @@
-# setup.py
-from setuptools import setup, find_packages
-
-setup(
-    name="ats_optimizer",
-    version="0.1",
-    packages=find_packages(),
-    install_requires=[
-        'python-docx>=0.8.11',
-        'pdfminer.six>=20221105',
-        'pyyaml>=6.0',
-        'streamlit>=1.28.0',
-        'spacy>=3.7.2',
-    ],
-    python_requires='>=3.9',
-)

ats_optimizer/utils/__init__.py

@@ -1,5 +0,0 @@
-from .file_handlers import FileHandler
-from .logger import setup_logger
-from .config_manager import Config
-
-__all__ = ['FileHandler', 'setup_logger', 'Config']

ats_optimizer/utils/config_manager.py

@@ -1,9 +0,0 @@
-import yaml
-
-class Config:
-    def __init__(self, config_path: str):
-        with open(config_path, 'r') as f:
-            self.config = yaml.safe_load(f)
-        
-    def __getattr__(self, name):
-        return self.config.get(name)

ats_optimizer/utils/file_handlers.py

@@ -1,69 +0,0 @@
-import os
-from docx import Document
-from pdfminer.high_level import extract_text
-from pathlib import Path
-from tempfile import NamedTemporaryFile
-import uuid
-
-class FileHandler:
-    @staticmethod
-    def read_file(file_path: str) -> str:
-        if file_path.endswith('.docx'):
-            return FileHandler._read_docx(file_path)
-        elif file_path.endswith('.pdf'):
-            return extract_text(file_path)
-        else:
-            with open(file_path, 'r') as f:
-                return f.read()
-    #--
-    
-    @staticmethod
-    def save_uploaded_file(uploaded_file, directory="temp_uploads"):
-        """Save Streamlit uploaded file to a temporary directory"""
-        try:
-            # Create directory if it doesn't exist
-            Path(directory).mkdir(exist_ok=True)
-            
-            # Generate unique filename
-            file_ext = Path(uploaded_file.name).suffix
-            unique_id = uuid.uuid4().hex
-            temp_file = Path(directory) / f"{unique_id}{file_ext}"
-            
-            # Save file
-            with open(temp_file, "wb") as f:
-                f.write(uploaded_file.getbuffer())
-            
-            return str(temp_file)
-        except Exception as e:
-            print(f"Error saving file: {e}")
-            return None
-
-    @staticmethod
-    def cleanup_temp_files(directory="temp_uploads"):
-        """Remove temporary files"""
-        try:
-            for file in Path(directory).glob("*"):
-                file.unlink()
-        except Exception as e:
-            print(f"Error cleaning files: {e}")
-   #--
-   
-    @staticmethod
-    def _read_docx(file_path: str) -> str:
-        doc = Document(file_path)
-        return '\n'.join([para.text for para in doc.paragraphs])
-    
-    @staticmethod
-    def save_resume(resume_data: dict, output_path: str):
-        if output_path.endswith('.docx'):
-            FileHandler._save_as_docx(resume_data, output_path)
-        else:
-            with open(output_path, 'w') as f:
-                f.write(resume_data['content'])
-
-    @staticmethod
-    def _save_as_docx(resume_data: dict, output_path: str):
-        doc = Document()
-        # Add formatting preservation logic here
-        doc.save(output_path)
-    

ats_optimizer/utils/file_handlers1.py

@@ -1,55 +0,0 @@
-import os
-from pathlib import Path
-import uuid
-from docx import Document
-from pdfminer.high_level import extract_text
-
-class FileHandler:
-    @staticmethod
-    def save_uploaded_file(uploaded_file, directory="temp_uploads"):
-        """Save Streamlit uploaded file to temporary directory"""
-        try:
-            # Create directory if needed
-            Path(directory).mkdir(exist_ok=True)
-            
-            # Generate unique filename
-            file_ext = Path(uploaded_file.name).suffix
-            unique_id = uuid.uuid4().hex
-            temp_file = Path(directory) / f"{unique_id}{file_ext}"
-            
-            # Save file
-            with open(temp_file, "wb") as f:
-                f.write(uploaded_file.getbuffer())
-            
-            return str(temp_file)
-        except Exception as e:
-            print(f"Error saving file: {e}")
-            return None
-
-    @staticmethod
-    def save_resume(resume_data, output_path):
-        """Save resume content to file"""
-        try:
-            if output_path.endswith('.docx'):
-                doc = Document()
-                for paragraph in resume_data.split('\n'):
-                    doc.add_paragraph(paragraph)
-                doc.save(output_path)
-            else:
-                with open(output_path, 'w') as f:
-                    f.write(resume_data)
-            return True
-        except Exception as e:
-            print(f"Error saving resume: {e}")
-            return False
-
-    @staticmethod
-    def cleanup_temp_files(directory="temp_uploads"):
-        """Remove temporary files"""
-        try:
-            for file in Path(directory).glob("*"):
-                file.unlink()
-            return True
-        except Exception as e:
-            print(f"Error cleaning files: {e}")
-            return False

ats_optimizer/utils/logger.py

@@ -1,18 +0,0 @@
-import logging
-
-def setup_logger(name: str = 'ats_optimizer'):
-    logger = logging.getLogger(name)
-    logger.setLevel(logging.INFO)
-    
-    handler = logging.StreamHandler()
-    formatter = logging.Formatter(
-        '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
-    )
-    handler.setFormatter(formatter)
-    logger.addHandler(handler)
-    
-    return logger
-
-
-# Create a module-level logger instance
-logger = setup_logger()