Emotion-Aware-Chatbot
Publicada el 2026-07-24
Descripción de la oferta
Emotion-Aware AI Chatbot with Real-Time Facial Emotion Detection | Computer Vision | Deep Learning | Generative AI ## Project Overview Developed a production-ready Emotion-Aware AI Chatbot capable of detecting a user's facial emotions in real time and generating intelligent, empathetic responses using Google Gemini AI. Unlike traditional chatbots, this system understands the user's emotional state before responding, creating a more natural and human-like conversational experience. The application combines Computer Vision, Deep Learning, and Generative AI to recognize facial expressions such as Happy, Sad, Angry, Fear, Surprise, Neutral, and Disgust, then dynamically adapts chatbot responses and voice output based on the detected emotion. The complete solution is deployed as a modern web application with a responsive Glassmorphism user interface, making it suitable for real-world AI applications in healthcare, education, customer support, and digital assistants. --- # Key Features > Real-Time Facial Emotion Detection using Computer Vision > Custom CNN Model Trained on the FER2013 Dataset > Emotion Recognition (7 Human Emotions) > AI-Powered Conversations using Google Gemini >Context-Aware and Emotion-Based Responses > Dynamic AI Voice Output (Text-to-Speech) > Live Webcam Emotion Analysis > Modern Responsive Glassmorphism User Interface > Fast Real-Time Processing > Production-Ready Flask Web Application # Technology Stack #Programming Languages * Python * JavaScript * HTML5 * CSS3 # Artificial Intelligence & Machine Learning * TensorFlow * Keras * OpenCV * Custom CNN * FER2013 Dataset * Google Gemini API # Web Technologies * Flask * Tailwind CSS * Web Speech API # Development Tools * Git * Jupyter Notebook * Docker ## Problem Solved Traditional chatbots provide generic responses without understanding users' emotions. This project bridges the gap between Artificial Intelligence and Emotional Intelligence by enabling the chatbot to recognize facial expressions and generate emotionally appropriate responses, significantly improving user engagement and the overall conversational experience. ## Applications * Mental Health & Emotional Wellness * Healthcare Assistance * Educational Platforms * Customer Support Automation * AI Virtual Assistants * Employee Wellness Monitoring * Smart Learning Systems * Human-Computer Interaction Research ## Technical Highlights * Real-Time Emotion Recognition * CNN-Based Deep Learning Model * Multi-Modal AI Architecture * Computer Vision Integration * Google Gemini Prompt Engineering * Emotion-Based AI Response Generation * Voice Interaction System * Responsive Web Deployment * Modular and Scalable Architecture ## Outcome This project demonstrates how **Computer Vision, Deep Learning, and Large Language Models (LLMs)can work together to build emotionally intelligent conversational systems. It provides a scalable foundation for next-generation AI assistants capable of understanding human emotions and delivering personalized, empathetic interactions.
Skills
- Deep Learning
- Computer Vision
- JavaScript
- Python
- Flask
- Keras
- Jupyter
- Tailwind CSS
- CSS
- Machine Learning
- Customer Support
- UI/UX Design
- TensorFlow
- Docker
- Inteligencia Emocional
- Prompt Engineering
- HTML
- Git
- Ayuda a Domicilio
Fuente original: freelancer