Codebase fix

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_analyse):
+    URL = 'https://sn-watson-emotion.labs.skills.network/v1/watson.runtime.nlp.v1/NlpService/EmotionPredict'
+    header = {"grpc-metadata-mm-model-id": "emotion_aggregated-workflow_lang_en_stock"}
+    input_json = {"raw_document": {"text": text_to_analyse}}
+    response = requests.post(URL, json = input_json, headers = header)
+    status_code = response.status_code
+
+    emotions = {}
+
+    if status_code == 200:
+        formatted_response = json.loads(response.text)
+        # Format output and inslude dominant emotion
+        emotions = formatted_response['emotionPredictions'][0]['emotion']
+        dominant_emotion = max(emotions, key=emotions.get)
+        # Append to emotions dict
+        emotions['dominant_emotion'] = dominant_emotion
+
+    elif status_code == 400:
+        emotions['anger'] = None
+        emotions['disgust'] = None
+        emotions['fear'] = None
+        emotions['joy'] = None
+        emotions['sadness'] = None
+        emotions['dominant_emotion'] = None
+
+    return emotions

server.py

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+'''Deploy a Flask application that will allow the user to provide a text string
+and return a breakdown of five emotions conveyed by the text
+'''
+from flask import Flask, request, render_template
+from EmotionDetection.emotion_detection import emotion_detector
+
+app = Flask("Emotion Detector")
+
+@app.route('/emotionDetector')
+def emotion_analyzer():
+    '''Retrieves (get) the user input text string, pass the text into the 
+    emotion_detector function inside of emotion_detection.py. Error handeling
+    '''
+    text_to_analyse = request.args.get('textToAnalyze')
+    emotion_result = emotion_detector(text_to_analyse)
+
+    if emotion_result['dominant_emotion'] is None:
+        return "Invalid text! Please try again"
+
+    formatted_result = (
+        f"For the given statement, the system response is "
+        f"'anger': {emotion_result['anger']}, "
+        f"'disgust': {emotion_result['disgust']}, "
+        f"'fear': {emotion_result['fear']}, "
+        f"'joy': {emotion_result['joy']} and "
+        f"'sadness': {emotion_result['sadness']}. "
+        f"The dominant emotion is {emotion_result['dominant_emotion']}."
+    )
+    return formatted_result
+
+@app.route("/")
+def render_index_page():
+    '''Render the index page to the user. This is the page that prompts the user
+    to enter text into the box and emotion detection will be returned
+    '''
+    return render_template('index.html')
+
+if __name__ == "__main__":
+    app.run(debug=True, host='0.0.0.0', port=5000)

test_emotion_detection.py

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+from EmotionDetection.emotion_detection import emotion_detector
+import unittest
+
+class TestEmotionDetection(unittest.TestCase):
+    def test_emotion_detector(self):
+        result1 = emotion_detector('I am glad this happened')
+        self.assertEqual(result1['dominant_emotion'], 'joy')
+
+        result2 = emotion_detector('I am really mad about this')
+        self.assertEqual(result2['dominant_emotion'], 'anger')
+
+        result3 = emotion_detector('I feel disgusted just hearing about this')
+        self.assertEqual(result3['dominant_emotion'], 'disgust')
+
+        result4 = emotion_detector('I am so sad about this')
+        self.assertEqual(result4['dominant_emotion'], 'sadness')
+
+        result5 = emotion_detector('I am really afraid that this will happen	')
+        self.assertEqual(result5['dominant_emotion'], 'fear')
+
+unittest.main()