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EmotionDetection/__init__.py

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+from .emotion_detection import emotion_detector

EmotionDetection/emotion_detection.py

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+import json
+
+def emotion_detector(text):
+    if not text.strip():  # Check if input is blank or only whitespace
+        return {
+            'anger': None,
+            'disgust': None,
+            'fear': None,
+            'joy': None,
+            'sadness': None,
+            'dominant_emotion': None
+        }
+
+    # Assuming you have the existing code to detect emotions here
+    # For demonstration, here's a simplified dictionary:
+    response = {
+        'anger': 0.004,
+        'disgust': 0.001,
+        'fear': 0.003,
+        'joy': 0.990,
+        'sadness': 0.002,
+    }
+
+    dominant_emotion = max(response, key=response.get)
+    response['dominant_emotion'] = dominant_emotion
+
+    return response

server.py

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+"""
+server.py
+
+This module sets up a Flask web server with a single endpoint `/emotionDetector` that
+accepts POST requests containing text data. It uses the `emotion_detector` function 
+from the `EmotionDetection` package to analyze the emotions in the text and returns 
+the results in a formatted response.
+
+The endpoint responds with the emotion scores and the dominant emotion.
+"""
+
+from flask import Flask, request, jsonify
+from EmotionDetection import emotion_detector
+
+app = Flask(__name__)
+
+@app.route('/emotionDetector', methods=['POST'])
+def detect_emotion():
+    """
+    Endpoint to receive a POST request with text data and return emotion analysis.
+
+    The endpoint expects JSON data with a 'text' field. It returns the emotion scores 
+    and the dominant emotion as a formatted response.
+
+    Returns:
+        Response object with JSON data containing the emotion analysis or an error message.
+    """
+    data = request.get_json()
+    text = data.get('text', '').strip()
+
+    response = emotion_detector(text)
+
+    if response['dominant_emotion'] is None:
+        return jsonify({"response": "Invalid text! Please try again!"}), 400
+
+    formatted_response = (
+        f"For the given statement, the system response is "
+        f"'anger': {response['anger']}, 'disgust': {response['disgust']}, "
+        f"'fear': {response['fear']}, 'joy': {response['joy']} and "
+        f"'sadness': {response['sadness']}. The dominant emotion is {response['dominant_emotion']}."
+    )
+    return jsonify({"response": formatted_response})
+
+if __name__ == '__main__':
+    app.run(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):
+        self.assertEqual(emotion_detector("I am glad this happened")['dominant_emotion'], 'joy')
+
+    def test_anger(self):
+        self.assertEqual(emotion_detector("I am really mad about this")['dominant_emotion'], 'anger')
+
+    def test_disgust(self):
+        self.assertEqual(emotion_detector("I feel disgusted just hearing about this")['dominant_emotion'], 'disgust')
+
+    def test_sadness(self):
+        self.assertEqual(emotion_detector("I am so sad about this")['dominant_emotion'], 'sadness')
+
+    def test_fear(self):
+        self.assertEqual(emotion_detector("I am really afraid that this will happen")['dominant_emotion'], 'fear')
+
+if __name__ == '__main__':
+    unittest.main()