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 = {
+        "Content-Type": "application/json",
+        "grpc-metadata-mm-model-id": "emotion_aggregated-workflow_lang_en_stock"
+    }
+    input_json = { "raw_document": { "text": text_to_analyze } }
+
+    response = requests.post(url, headers=headers, json=input_json)
+
+    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']
+
+    scores = {
+        'anger': emotions.get('anger', 0),
+        'disgust': emotions.get('disgust', 0),
+        'fear': emotions.get('fear', 0),
+        'joy': emotions.get('joy', 0),
+        'sadness': emotions.get('sadness', 0)
+    }
+
+    dominant_emotion = max(scores, key=scores.get)
+
+    scores['dominant_emotion'] = dominant_emotion
+    return scores

server.py

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+"""Flask web server for emotion detection."""
+
+from flask import Flask, request, render_template
+from EmotionDetection import emotion_detector
+
+app = Flask(__name__)
+
+@app.route('/')
+def index():
+    """Render the main HTML page with input form."""
+    return render_template('index.html')
+
+@app.route('/emotionDetector', methods=['GET'])
+def detect_emotion():
+    """Handle emotion detection logic and return formatted output or error."""
+    text = request.args.get('textToAnalyze')  # Get the input from the query string
+    result = emotion_detector(text)
+
+    # Error handling for blank input or 400 status from Watson API
+    if result['dominant_emotion'] is None:
+        return "Invalid text! Please try again!"
+
+    # Create response string for display
+    response_str = (
+        f"For the given statement, the system response is "
+        f"'anger': {result['anger']}, "
+        f"'disgust': {result['disgust']}, "
+        f"'fear': {result['fear']}, "
+        f"'joy': {result['joy']} and "
+        f"'sadness': {result['sadness']}. "
+        f"The dominant emotion is {result['dominant_emotion']}."
+    )
+
+    return response_str
+
+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):
+        result = emotion_detector("I am glad this happened")
+        self.assertEqual(result['dominant_emotion'], 'joy')
+
+    def test_anger(self):
+        result = emotion_detector("I am really mad about this")
+        self.assertEqual(result['dominant_emotion'], 'anger')
+
+    def test_disgust(self):
+        result = emotion_detector("I feel disgusted just hearing about this")
+        self.assertEqual(result['dominant_emotion'], 'disgust')
+
+    def test_sadness(self):
+        result = emotion_detector("I am so sad about this")
+        self.assertEqual(result['dominant_emotion'], 'sadness')
+
+    def test_fear(self):
+        result = emotion_detector("I am really afraid that this will happen")
+        self.assertEqual(result['dominant_emotion'], 'fear')
+
+if __name__ == '__main__':
+    unittest.main()