Final Project

EmotionDetection/__init__.py

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+from .emotion_detection import emotion_detector

EmotionDetection/emotion_detection.py

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+import requests
+import json
+
+def emotion_detector(text_to_analyze):
+    url = "https://sn-watson-emotion.labs.skills.network/v1/watson.runtime.nlp.v1/NlpService/EmotionPredict"
+    headers = {"grpc-metadata-mm-model-id": "emotion_aggregated-workflow_lang_en_stock"}
+    data = { "raw_document": { "text": text_to_analyze } }
+
+    response = requests.post(url, json=data, headers=headers)
+
+    if response.status_code == 400:
+        return {
+            'anger': None,
+            'disgust': None,
+            'fear': None,
+            'joy': None,
+            'sadness': None,
+            'dominant_emotion': None
+        }
+
+    result = json.loads(response.text)
+
+    emotions = result['emotionPredictions'][0]['emotion']
+    anger = emotions['anger']
+    disgust = emotions['disgust']
+    fear = emotions['fear']
+    joy = emotions['joy']
+    sadness = emotions['sadness']
+
+    # Get the dominant emotion
+    emotion_scores = {
+        'anger': anger,
+        'disgust': disgust,
+        'fear': fear,
+        'joy': joy,
+        'sadness': sadness
+    }
+
+    dominant_emotion = max(emotion_scores, key=emotion_scores.get)
+
+    return {
+        'anger': anger,
+        'disgust': disgust,
+        'fear': fear,
+        'joy': joy,
+        'sadness': sadness,
+        'dominant_emotion': dominant_emotion
+    }

server.py

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+"""Server module for Emotion Detection web application using Flask."""
+
+from flask import Flask, render_template, request, jsonify
+from EmotionDetection import emotion_detector
+
+app = Flask(__name__)
+
+
+@app.route('/emotionDetector', methods=['GET', 'POST'])
+@app.route('/emotionDetector', methods=['GET', 'POST'])
+def emotion_detector_endpoint():
+    """Endpoint for detecting emotions from a given text.
+
+    Accepts GET and POST requests:
+    - POST: expects JSON {"text": "<user_text>"}
+    - GET: expects query parameter textToAnalyze=<user_text>
+
+    Returns:
+        JSON response containing emotion scores and dominant emotion,
+        or an error message if the input is invalid.
+    """
+    if request.method == 'POST':
+        data = request.get_json()
+        text_to_analyze = data.get('text', '')
+    else:
+        text_to_analyze = request.args.get('textToAnalyze', '')
+
+    result = emotion_detector(text_to_analyze)
+
+    if result['dominant_emotion'] is None:
+        return jsonify({"message": "Invalid text! Please try again!"})
+
+    formatted_response = {
+        "anger": result['anger'],
+        "disgust": result['disgust'],
+        "fear": result['fear'],
+        "joy": result['joy'],
+        "sadness": result['sadness'],
+        "dominant_emotion": result['dominant_emotion']
+    }
+
+    return jsonify(formatted_response)
+
+
+
+@app.route('/')
+def index():
+    """Render the main index page."""
+    return render_template('index.html')
+
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000, debug=True)

test_emotion_detection.py

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+import unittest
+from EmotionDetection import emotion_detector
+
+class TestEmotionDetection(unittest.TestCase):
+
+    def test_joy(self):
+        text = "I am glad this happened"
+        result = emotion_detector(text)
+        self.assertEqual(result['dominant_emotion'], 'joy')
+
+    def test_anger(self):
+        text = "I am really mad about this"
+        result = emotion_detector(text)
+        self.assertEqual(result['dominant_emotion'], 'anger')
+
+    def test_disgust(self):
+        text = "I feel disgusted just hearing about this"
+        result = emotion_detector(text)
+        self.assertEqual(result['dominant_emotion'], 'disgust')
+
+    def test_sadness(self):
+        text = "I am so sad about this"
+        result = emotion_detector(text)
+        self.assertEqual(result['dominant_emotion'], 'sadness')
+
+    def test_fear(self):
+        text = "I am really afraid that this will happen"
+        result = emotion_detector(text)
+        self.assertEqual(result['dominant_emotion'], 'fear')
+
+if __name__ == '__main__':
+    unittest.main()