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_\\----------- **Resume Parser** ----------\\_
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# Overview:
This project is a comprehensive Resume Parsing tool built using Python,
integrating the Mistral-Nemo-Instruct-2407 model for primary parsing.
If Mistral fails or encounters issues,
the system falls back to a custom-trained spaCy model to ensure continued functionality.
The tool is wrapped with a Flask API and has a user interface built using HTML and CSS.
# Installation Guide:
1. Create and Activate a Virtual Environment
python -m venv venv
source venv/bin/activate # For Linux/Mac
# or
venv\Scripts\activate # For Windows
# NOTE: If the virtual environment (venv) is already created, you can skip the creation step and just activate.
- For Linux/Mac:
source venv/bin/activate
- For Windows:
venv\Scripts\activate
2. Install Required Libraries
pip install -r requirements.txt
# Ensure the following dependencies are included:
- Flask
- spaCy
- huggingface_hub
- PyMuPDF
- python-docx
- Tesseract-OCR (for image-based parsing)
3. Set up Hugging Face Token
- Add your Hugging Face token to the .env file as:
HF_TOKEN=<your_huggingface_token>
# File Structure Overview:
Mistral_With_Spacy/
│
├── Spacy_Models/
│ └── ner_model_05_3 # Pretrained spaCy model directory for resume parsing
│
├── templates/
│ ├── index.html # UI for file upload
│ └── result.html # Display parsed results in structured JSON
│
├── uploads/ # Directory for uploaded resume files
│
├── utils/
│ ├── mistral.py # Code for calling Mistral API and handling responses
│ ├── spacy.py # spaCy fallback model for parsing resumes
│ ├── error.py # Error handling utilities
│ └── fileTotext.py # Functions to extract text from different file formats (PDF, DOCX, etc.)
│
├── venv/ # Virtual environment
│
├── .env # Environment variables file (contains Hugging Face token)
│
├── main.py # Flask app handling API routes for uploading and processing resumes
│
└── requirements.txt # Dependencies required for the project
# Program Overview:
# Mistral Integration (utils/mistral.py)
- Mistral API Calls: Uses Hugging Face’s Mistral-Nemo-Instruct-2407 model to parse resumes.
- Personal and Professional Extraction: Two functions extract personal and professional information in structured JSON format.
- Fallback Mechanism: If Mistral fails, spaCy NER model is used as a fallback.
# SpaCy Integration (utils/spacy.py)
- Custom Trained Model: Uses a spaCy model (ner_model_05_3) trained specifically for resume parsing.
- Named Entity Recognition: Extracts key information like Name, Email, Contact, Location, Skills, Experience, etc., from resumes.
- Validation: Includes validation for extracted emails and contacts.
# File Conversion (utils/fileTotext.py)
- Text Extraction: Handles different resume formats (PDF, DOCX, ODT, RSF, and images like PNG, JPG, JPEG) and extracts text for further processing.
- PDF Files: Uses PyMuPDF to extract text and, if necessary, Tesseract-OCR for image-based PDF content.
- DOCX Files: Uses `python-docx` to extract structured text from Word documents.
- ODT Files: Uses `odfpy` to extract text from ODT (OpenDocument) files.
- RSF Files: Reads plain text from RSF files.
- Images (PNG, JPG, JPEG): Uses Tesseract-OCR to extract text from image-based resumes.
- Hyperlink Extraction: Extracts hyperlinks from PDF files, capturing any embedded URLs during the parsing process.
# Error Handling (utils/error.py)
- Handles API response errors, file format errors, and ensures smooth fallbacks without crashing the app.
# Flask API (main.py)
Endpoints:
- /upload for uploading resumes.
- Displays parsed results in JSON format on the results page.
- UI: Simple interface for uploading resumes and viewing the parsing results.
# Tree map of your program:
main.py
├── Handles API side
├── File upload/remove
├── Process resumes
└── Show result
utils
├── fileTotext.py
│ └── Converts files to text
│ ├── PDF
│ ├── DOCX
│ ├── RTF
│ ├── ODT
│ ├── PNG
│ ├── JPG
│ └── JPEG
├── mistral.py
│ ├── Mistral API Calls
│ │ └── Uses Mistral-Nemo-Instruct-2407 model
│ ├── Personal and Professional Extraction
│ │ ├── Extracts personal information
│ │ └── Extracts professional information
│ └── Fallback Mechanism
│ └── Uses spaCy NER model if Mistral fails
└── spacy.py
├── Custom Trained Model
│ └── Uses spaCy model (ner_model_05_3)
├── Named Entity Recognition
│ └── Extracts key information (Name, Email, Contact, etc.)
└── Validation
└── Validates emails and contacts
# References:
- [Flask Documentation](https://flask.palletsprojects.com/)
- [spaCy Documentation](https://spacy.io/usage)
- [Hugging Face Hub API](https://huggingface.co/docs/huggingface_hub/index)
- [PyMuPDF (MuPDF) Documentation](https://pymupdf.readthedocs.io/en/latest/)
- [python-docx Documentation](https://python-docx.readthedocs.io/en/latest/)
- [Tesseract OCR Documentation](https://github.com/tesseract-ocr/tesseract)
- [Virtual Environments in Python](https://docs.python.org/3/tutorial/venv.html)
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