task 2 finalized

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'
+    header = {"grpc-metadata-mm-model-id": "emotion_aggregated-workflow_lang_en_stock"}
+    input_json = { "raw_document": { "text": text_to_analyze } }
+    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)
+        emotions = formatted_response['emotionPredictions'][0]['emotion']
+        dominant_emotion = max(emotions.items(), key=lambda x: x[1])
+        emotions['dominant_emotion'] = dominant_emotion[0]
+    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 a user to provide
+a text string which will then be analyzed to determine which emotion amongst a set of 5
+is the most likely emotion being conveyed by the given 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():
+    '''Retrieve the provided text string from the user, then pass the text
+    to be analyzed by the emotion detector. Finally, return a response displaying
+    the confidence scores across all emotions and the dominant emotion.
+    '''
+    text_to_analyse = request.args.get('textToAnalyze')
+    emotion_result = emotion_detector(text_to_analyse)
+    anger = emotion_result['anger']
+    disgust = emotion_result['disgust']
+    fear = emotion_result['fear']
+    joy = emotion_result['joy']
+    sadness = emotion_result['sadness']
+    dominant_emotion = emotion_result['dominant_emotion']
+
+    if dominant_emotion is None:
+        return "Invalid text! Please try again"
+
+    response_str = f"""For the given statement, the system response is
+    'anger': {anger}, 'disgust': {disgust}, 'fear': {fear}, 'joy': {joy}, 'sadness': {sadness}.
+    The dominant emotion is <strong>{dominant_emotion}</strong>."""
+    return response_str
+
+@app.route("/")
+def render_index_page():
+    '''Render the index page to the user, this is where the text string to be
+    analyzed is provided and a response is displayed back to the user.
+    '''
+    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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+from EmotionDetection.emotion_detection import emotion_detector
+import unittest
+
+
+class TestEmotionAnalyzer(unittest.TestCase):
+    def test_emotion_analyzer(self):
+        result_1 = emotion_detector('I am glad this happened')
+        self.assertEqual(result_1['dominant_emotion'] , 'joy')
+        result_2 = emotion_detector('I am really mad about this')
+        self.assertEqual(result_2['dominant_emotion'] ,'anger')
+        result_3 = emotion_detector('I feel disgusted just hearing about this')
+        self.assertEqual(result_3['dominant_emotion'] , 'disgust')
+        result_4 = emotion_detector('I am so sad about this')
+        self.assertEqual(result_4['dominant_emotion'] , 'sadness')
+        result_5 = emotion_detector('I am really afraid that this will happen')
+        self.assertEqual(result_5['dominant_emotion'] , 'fear')
+
+unittest.main()