This repository contains the projects completed as part of my Codec Technologies Python Developer Internship.
The repository includes two Python-based web applications demonstrating practical experience in Artificial Intelligence, Natural Language Processing, Flask, database management, PDF processing, and web development.
Status: Completed
An AI-assisted web application that automatically extracts important candidate information from PDF resumes and stores the extracted data in a searchable SQLite database.
The application extracts:
- 👤 Candidate name
- 📧 Email address
- 📱 Phone number
- 🛠️ Technical skills
- 🎓 Education information
Technologies:
- Python
- Flask
- PDFPlumber
- spaCy
- SQLite
- HTML
- CSS
Features:
- 📤 PDF resume upload
- 📖 PDF text extraction
- 👤 Candidate information extraction
- 🛠️ Technical skill extraction
- 🎓 Education extraction
- 💾 SQLite database storage
- 🔄 Duplicate candidate prevention
- 🔎 Candidate search
- 🌐 Flask web interface
⚠️ Missing information handling
➡️ View Automated Resume Parser
Status: Completed
An AI-powered web application that analyzes user reviews and classifies their sentiment as POSITIVE or NEGATIVE using a pre-trained Transformer model from Hugging Face.
The application displays the predicted sentiment and confidence score and stores analyzed reviews in a local SQLite database for viewing through the Review History page.
Technologies:
- Python
- Flask
- Hugging Face Transformers
- PyTorch
- SQLite
- HTML
- CSS
Features:
- 🤖 AI-based sentiment analysis
- 🟢 Positive sentiment detection
- 🔴 Negative sentiment detection
- 📊 Confidence score
- 💾 Review storage using SQLite
- 📜 Review history
- 🌐 Flask web interface
- 🎨 User-friendly interface
⚠️ Empty review validation
Sentiment Model:
distilbert-base-uncased-finetuned-sst-2-english
The model performs binary sentiment classification:
- POSITIVE
- NEGATIVE
Note: The current model does not provide a separate NEUTRAL class. Neutral-style reviews are therefore classified into either POSITIVE or NEGATIVE.
➡️ View AI-Based Sentiment Analyzer
| Project | Main Purpose | AI/NLP | Database | Framework |
|---|---|---|---|---|
| Automated Resume Parser | Resume information extraction | spaCy / NLP | SQLite | Flask |
| AI-Based Sentiment Analyzer | Review sentiment classification | Hugging Face Transformers | SQLite | Flask |
Through these projects, I gained practical experience with:
- Python
- Flask
- Natural Language Processing
- Artificial Intelligence
- Hugging Face Transformers
- spaCy
- PDF text extraction
- Regular expressions
- SQLite
- HTML
- CSS
- Jinja templating
- Git
- GitHub
- Project documentation
Codec_Projects/
│
├── Automated_Resume_Parser/
│ ├── app.py
│ ├── parser.py
│ ├── db.py
│ ├── extract_text.py
│ ├── search_candidates.py
│ ├── requirements.txt
│ ├── README.md
│ ├── .gitignore
│ ├── templates/
│ ├── static/
│ └── screenshots/
│
├── AI_Based_Sentiment_Analyzer/
│ ├── app.py
│ ├── requirements.txt
│ ├── README.md
│ ├── .gitignore
│ ├── templates/
│ ├── static/
│ └── screenshots/
│
└── README.md
Each project contains its own README.md with detailed installation and usage instructions.
cd Automated_Resume_Parser
pip install -r requirements.txt
python -m spacy download en_core_web_sm
python app.py
Open:
http://127.0.0.1:5000
cd AI_Based_Sentiment_Analyzer
pip install -r requirements.txt
python app.py
Open:
http://127.0.0.1:5000
Both projects currently use SQLite as their local database solution.
SQLite was selected because it is lightweight, easy to configure, and suitable for these internship projects.
Stores:
- Candidate name
- Phone number
- Skills
- Education
Stores:
- Review ID
- Review text
- Sentiment
- Confidence score
Local database files and generated test data are excluded from the GitHub repository using .gitignore.
Possible future improvements include:
- PostgreSQL integration
- MongoDB integration
- Advanced NLP-based extraction
- Improved resume parsing for different layouts
- DOC/DOCX resume support
- Resume ranking and candidate scoring
- Advanced sentiment classification
- Authentication and user accounts
- Admin dashboards
- CSV/Excel data export
- Cloud deployment
These projects provided practical experience in:
- Python application development
- Flask web development
- Artificial Intelligence
- Natural Language Processing
- Machine Learning
- Pre-trained Transformer models
- PDF processing
- Database management
- HTML and CSS
- Git and GitHub
- Project documentation
These projects were developed as part of the Codec Technologies Python Developer Internship.
The projects were created to gain practical experience in Python development, Artificial Intelligence, Natural Language Processing, web application development, database management, and GitHub-based project organization.
Riya Sharma
B.Tech Computer Science Engineering Student
Codec Technologies Python Developer Internship – 2026