Emotion Forge is an AI-powered project that analyzes and interprets human emotions for smarter, more empathetic interactions. Designed for versatility, it can be integrated into apps, chatbots, or research tools to add an emotional intelligence layer to any system.
Understanding human emotion is key to building more natural and responsive AI systems. Emotion Forge takes in input (text, and optionally voice/facial data) and outputs emotional insights — such as sentiment, emotion category, and intensity — that downstream applications can use to respond more intelligently.
- 🎭 Emotion detection and classification (e.g., happy, sad, angry, anxious, neutral)
- 📊 Sentiment scoring (positive / negative / neutral with confidence levels)
- 💬 Easy integration into chatbots and conversational AI
- 🔌 Simple API for use in external apps or research pipelines
- 🧩 Modular design — swap in different models or data sources
- 📈 Logging/analytics of emotional trends over time
⚠️ Update this list based on which features are actually implemented (e.g., text-only vs. multi-modal).
- Language: Python
- AI/ML: NLP / Machine Learning models (e.g., Transformers, scikit-learn, or custom model — specify yours)
- API Layer: Flask / FastAPI (specify)
- Data Handling: Pandas, NumPy
- Other: (Add any specific libraries — e.g., HuggingFace, NLTK, spaCy, OpenCV for facial emotion, etc.)
emotion_forge_ai/
│
├── data/ # Datasets used for training/testing
├── models/ # Trained model files / weights
├── src/ # Core source code
│ ├── preprocessing.py # Data cleaning & preparation
│ ├── model.py # Emotion detection model
│ └── inference.py # Prediction / inference logic
├── api/ # API endpoints for integration
├── notebooks/ # Jupyter notebooks (experiments, EDA)
├── requirements.txt # Python dependencies
└── README.md
⚠️ Adjust to match your actual folder/file structure.
-
Clone the repository
git clone https://github.com/Gayathri-Manjunath/emotion_forge_ai.git cd emotion_forge_ai -
Create a virtual environment
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
-
Install dependencies
pip install -r requirements.txt
-
Run the application
python app.py
Example usage for analyzing text input:
from src.inference import analyze_emotion
result = analyze_emotion("I can't believe how great today turned out!")
print(result)
# Output: {'emotion': 'joy', 'confidence': 0.92}Example API request (if exposed via Flask/FastAPI):
curl -X POST http://127.0.0.1:5000/analyze \
-H "Content-Type: application/json" \
-d '{"text": "I am feeling really anxious about tomorrow."}'
⚠️ Replace with your actual usage examples and endpoint routes.
- Chatbots & Virtual Assistants — respond based on user's emotional state
- Customer Support Tools — flag frustrated or upset customers in real time
- Mental Health Research — analyze emotional trends in text data
- Social Media Analysis — track public sentiment on topics or brands
- Gaming & Interactive Apps — adapt experiences based on player emotion
- Multi-modal emotion detection (voice tone, facial expressions)
- Real-time streaming analysis
- Support for multiple languages
- Pretrained model fine-tuning on custom datasets
- Dashboard for visualizing emotional trends
Contributions, issues, and feature requests are welcome. Feel free to check the issues page.
This project is licensed under the MIT License — feel free to use and modify it.
Your Name GitHub: @Gayathri-Manjunath