#Emotion Recognition and Depression Detection System using Deep Learning
-This project implements a web application for detecting signs of depression in user-uploaded videos.

#Tech Stack
1)Backend: Python (Flask)
2)Database: SQLite
3)Machine Learning: Keras (TensorFlow backend)
4)Computer Vision: OpenCV
5)Natural Language Processing: TextBlob
6)Speech Recognition: SpeechRecognition
7)Web Development: Flask-Migrate, Flask-SQLAlchemy

#Algorithms
1.Facial Emotion Recognition: Convolutional Neural Network (CNN) trained on facial expressions
2.Sentiment Analysis: TextBlob library for analyzing audio transcripts

#Functionalities <br1.Users can upload videos.
2.The system detects emotions in the video frames using facial recognition.
3.Audio from the video is extracted and analyzed for sentiment using speech recognition and text analysis.
4.The system combines the results from facial recognition and sentiment analysis to provide an overall indication of potential depression.
5.The results are displayed on the user interface.

#Installation and Usage (Modify as needed)

1. Dependencies:

#Make sure you have the following libraries installed in your Python environment:

numpy
requests
Flask
Flask-SQLAlchemy
Flask-Migrate
keras
OpenCV
tensorflow
TextBlob
SpeechRecognition
moviepy

2. Database Setup:

The system uses an SQLite database for storing user information. You can create the database tables by running the application with the debug=True flag:
python app.py

3. Running the Application: Start the application:
   python app.py
   

Here's a View of the Actual Website

1)The homepage with a video upload button-

2)The results page displaying detected emotions and sentiment analysis output-

3. Service provided like Doctor Consultation/Recommendation-