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