Task7

EmotionDetection/__init__.py

@@ -0,0 +1 @@
+from . import emotion_detection

EmotionDetection/emotion_detection.py

@@ -0,0 +1,58 @@
+import requests
+import json
+import operator
+
+# Define a function named sentiment_analyzer that takes a string input (text_to_analyse)
+def emotion_detector(text_to_analyse):
+
+    # URL of the sentiment analysis service
+    url = 'https://sn-watson-emotion.labs.skills.network/v1/watson.runtime.nlp.v1/NlpService/EmotionPredict'
+
+    # Create a dictionary with the text to be analyzed    
+    myobj = { "raw_document": { "text": text_to_analyse } }
+
+    # Set the headers required for the API request
+    header = {"grpc-metadata-mm-model-id": "emotion_aggregated-workflow_lang_en_stock"}
+
+    # Send a POST request to the API with the text and headers    
+    response = requests.post(url, json = myobj, headers=header)
+
+    # Parse the response from the API
+    formatted_response = json.loads(response.text)
+
+    # If the response status code is 200, extract emotion scores from the response
+    if response.status_code == 200:
+        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']
+
+    # If the response status code is 400, set scores to None
+    elif response.status_code == 400:
+        anger_score = None
+        disgust_score = None
+        fear_score = None
+        joy_score = None
+        anger_score = None
+        sadness_score = None
+
+    # Create a Disctionary with emotions and scores
+    emotions = {'anger': anger_score,
+        'disgust': disgust_score,
+        'fear': fear_score,
+        'joy': joy_score,
+        'sadness': sadness_score
+    }
+
+    # Calculate the dominant emotion only if the status code is 200
+    if response.status_code == 200:
+        dominant_emotion = max(emotions.items(), key=operator.itemgetter(1))[0]
+    else:
+        dominant_emotion = None
+
+    # Add the dominant emotion to the dictionary
+    emotions['dominant_emotion'] = dominant_emotion
+
+    # Return the label and score in a dictionary
+    return emotions

server.py

@@ -0,0 +1,36 @@
+from flask import Flask, render_template, request
+from EmotionDetection.emotion_detection import emotion_detector
+
+app = Flask("Emotion Detector")
+
+@app.route("/emotionDetector")
+def emo_detector():
+    # Retrieve the text to analyze from the request arguments
+    text_to_analyze = request.args.get('textToAnalyze')
+
+    # Pass the text to the sentiment_analyzer function and store the response
+    response = emotion_detector(text_to_analyze)
+
+    # Extract the emotion and score from the response
+    anger            = response['anger']
+    disgust          = response['disgust']
+    fear             = response['fear']
+    joy              = response['joy']
+    sadness          = response['sadness']
+    dominant_emotion = response['dominant_emotion']
+
+    # Return a formatted string with the emotion response
+    if anger is None or disgust is None or fear is None or joy is None or sadness is None or dominant_emotion is None:
+        return "Invalid text! Please try again!"
+    else:
+        return "For the given statement, the system response is \
+        'anger' : {}, 'disgust' : {}, 'fear' : {}, 'joy' : {} and 'sadness' : {}. \
+        The dominant emotion is <b>{}</b>.".format(anger, disgust, fear, joy, sadness, dominant_emotion)
+
+@app.route("/")
+def render_index_page():
+
+    return render_template('index.html')
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=5000)

test_emotion_detection.py

@@ -0,0 +1,28 @@
+from EmotionDetection.emotion_detection import emotion_detector
+import unittest
+
+class TestEmotionDetector(unittest.TestCase):
+
+    def test_emotion_detector(self):
+
+        # Test case for joy dominant emotion
+        result_1 = emotion_detector('I am glad this happened')
+        self.assertEqual(result_1['dominant_emotion'], 'joy')
+
+        # Test case for anger dominant emotion
+        result_2 = emotion_detector('I am really mad about this')
+        self.assertEqual(result_2['dominant_emotion'], 'anger')
+
+        # Test case for disgust dominant emotion
+        result_3 = emotion_detector('I feel disgusted just hearing about this')
+        self.assertEqual(result_3['dominant_emotion'], 'disgust')    
+
+        # Test case for sadness dominant emotion
+        result_4 = emotion_detector('I am so sad about this')
+        self.assertEqual(result_4['dominant_emotion'], 'sadness')    
+
+        # Test case for fear dominant emotion
+        result_5 = emotion_detector('I am really afraid that this will happen')
+        self.assertEqual(result_5['dominant_emotion'], 'fear')    
+
+unittest.main()