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# PROJECT: DEPRESSION-DETECTION-USING-TWEETS
# AUTHORS: AMEY THAKUR & MEGA SATISH
# GITHUB (AMEY): https://github.com/Amey-Thakur
# GITHUB (MEGA): https://github.com/msatmod
# REPOSITORY: https://github.com/Amey-Thakur/DEPRESSION-DETECTION-USING-TWEETS
# RELEASE DATE: June 5, 2022
# LICENSE: MIT License
# DESCRIPTION: Flask application entry point for the tweet analysis project.
# ==============================================================================
#!/usr/bin/env python3
import pickle
from flask import Flask, request, render_template
from flask_bootstrap import Bootstrap
import app_utilities
# Initialize the Flask application
# Flask-Bootstrap is utilized for enhanced UI styling consistency
app = Flask(__name__)
Bootstrap(app)
@app.route('/')
def index():
"""Renders the landing page for tweet input."""
return render_template('index.html')
@app.route('/predict', methods=['POST'])
def predict():
"""
Handles the form submission and displays the prediction result.
Returns:
Rendered result HTML with the model's prediction outcome.
"""
if request.method == 'POST':
# Retrieve the tweet content submitted via the web interface
tweet = request.form["tweet"]
input_data = [tweet]
# Invoke the backend prediction utility to classify the tweet's sentiment
# The engine utilizes an SVM classifier with spaCy word embeddings
my_prediction = app_utilities.tweet_prediction(str(input_data))
return render_template("result.html", prediction=my_prediction, name=tweet)
@app.errorhandler(404)
def page_not_found(e):
"""
Custom 404 error handler.
Renders the personalized 404 page when a resource is not found.
"""
return render_template('404.html'), 404
# Entry point for the Flask development server
if __name__ == '__main__':
# Execution on port 7860 as required for Hugging Face Spaces
app.run(host='0.0.0.0', port=7860)
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