Emotiondetectionfinal

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'
+    myobj = { "raw_document": { "text": text_to_analyse } }  
+    header = {"grpc-metadata-mm-model-id": "emotion_aggregated-workflow_lang_en_stock"}  
+    response = requests.post(url, json = myobj, headers=header)  
+
+    formatted_response = json.loads(response.text)
+    if response.status_code == 200:
+        emotions = formatted_response["emotionPredictions"][0]["emotion"]
+        dominant_emotion = max(emotions, key=emotions.get) 
+
+        anger = formatted_response['emotionPredictions'][0]['emotion']['anger']
+        disgust = formatted_response['emotionPredictions'][0]['emotion']['disgust']
+        fear = formatted_response['emotionPredictions'][0]['emotion']['fear']
+        joy = formatted_response['emotionPredictions'][0]['emotion']['joy']
+        sadness = formatted_response['emotionPredictions'][0]['emotion']['sadness']
+
+        return {'anger': anger, 'disgust': disgust, 'fear': fear, 'joy': joy, 'sadness': sadness,'dominant_emotion': dominant_emotion}
+
+    elif response.status_code == 400:
+        formatted_response ={
+                            'anger': None,
+                            'disgust': None, 
+                            'fear': None, 
+                            'joy': None, 
+                            'sadness': None, 
+                            'dominant_emotion': None}
+        return formatted_response

server.py

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+"""
+Emotion Detection Server
+
+This script defines a Flask-based server for performing emotion detection on user-provided text.
+
+Author:[Anvesh] 
+"""
+from flask import Flask, render_template, request
+from EmotionDetection.emotion_detection import emotion_detector
+app = Flask("Emotion Detection")
+
+def run_emotion_detection():
+    """
+    Main function to run the Emotion Detection application.
+    """
+
+@app.route("/emotionDetector")
+
+def sent_detector():
+    """
+    Analyze the user-provided text for emotions and return the result.
+    """
+    text_to_detect = request.args.get('textToAnalyze')
+    formated_response = emotion_detector(text_to_detect)
+    if formated_response['dominant_emotion'] is None:
+        return "Invalid text! Please try again."
+    return (
+        f"For the given statement, the system response is 'anger': {formated_response['anger']} "
+        f"'disgust': {formated_response['disgust']}, 'fear': {formated_response['fear']}, "
+        f"'joy': {formated_response['joy']} and 'sadness': {formated_response['sadness']}. "
+        f"The dominant emotion is {formated_response['dominant_emotion']}."
+    )
+
+@app.route("/")
+def render_index_page():
+    ''' This function initiates the rendering of the main application
+        page over the Flask channel
+    '''
+    return render_template('index.html')
+
+if __name__ == "__main__":
+    run_emotion_detection()

test_emotion_detection.py

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+from EmotionDetection.emotion_detection import emotion_detector
+import unittest
+
+class TestEmotionDetection(unittest.TestCase):
+    def test_emotion_detection(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()
+
+
+
+