Project finished

EmotionDetection/__init__.py

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+from . import emotion_detection

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"}
+
+    body = { "raw_document": { "text": text_to_analyze } }
+
+    response = requests.post(url, json=body, headers=headers)
+
+    if response.status_code == 400 or response.status_code == 500:
+        emotionsError = {
+        'anger': None,
+        'disgust': None,
+        'fear': None,
+        'joy': None,
+        'sadness': None,
+        'dominant_emotion' : None
+        }
+
+        return emotionsError
+
+    formatted_response = json.loads(response.text)
+
+    anger_score = formatted_response["emotionPredictions"][0]["emotion"]["anger"]
+    disgust_score = formatted_response["emotionPredictions"][0]["emotion"]["disgust"]
+    fear_score = formatted_response["emotionPredictions"][0]["emotion"]["fear"]
+    joy_score = formatted_response["emotionPredictions"][0]["emotion"]["joy"]
+    sadness_score = formatted_response["emotionPredictions"][0]["emotion"]["sadness"]
+
+    emotions = {
+        'anger': anger_score,
+        'disgust': disgust_score,
+        'fear': fear_score,
+        'joy': joy_score,
+        'sadness': sadness_score,
+    }
+
+    emotions['dominant_emotion'] = max(emotions, key = emotions.get)
+
+    return emotions

README.md

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-# Repository for final project
+# Repository for final project "Emotion Detector"
+
+![screeshot](screenshot.png)

server.py

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+"""That application runs an emotion detector."""
+from flask import Flask, render_template, request
+from EmotionDetection.emotion_detection import emotion_detector
+
+app = Flask(__name__)
+
+
+@app.route("/emotionDetector")
+def emotion_detector_server():
+    """Return the result of the emotions of the text in string format."""
+    text_to_analyze = request.args.get('textToAnalyze')
+
+    result = emotion_detector(text_to_analyze)
+
+    if result['dominant_emotion'] is None:
+        return "Invalid text! Please try again!."
+
+    string_result = "For the given statement, the system response is "
+    string_result += f"'anger': {result['anger']}, 'disgust': {result['disgust']}, "
+    string_result += f"'fear': {result['fear']}, 'joy': {result['joy']} and "
+    string_result += f"'sadness': {result['sadness']}. "
+    string_result += f"The dominant emotion is {result['dominant_emotion']}."
+
+    return string_result
+
+@app.route("/")
+def index():
+    """Return the index page."""
+    return render_template('index.html')
+
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000)

test_emotion_detection.py

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+import unittest
+from EmotionDetection.emotion_detection import emotion_detector
+
+class TestEmotion(unittest.TestCase):
+
+    def test_emotions(self):
+        joy = emotion_detector("I am glad this happened")["dominant_emotion"]
+        self.assertEqual(joy, "joy")
+
+        anger = emotion_detector("I am really mad about this")["dominant_emotion"]
+        self.assertEqual(anger, "anger")
+
+        disgust = emotion_detector("I feel disgusted just hearing about this")["dominant_emotion"]
+        self.assertEqual(disgust, "disgust")
+
+        sadness = emotion_detector("I am so sad about this")["dominant_emotion"]
+        self.assertEqual(sadness, "sadness")
+
+        fear = emotion_detector("I am really afraid that this will happen")["dominant_emotion"]
+        self.assertEqual(fear, "fear")
+
+unittest.main()